Tag Archives: Availability

From Idea to Launch: Getting Your First Customers

Post Syndicated from Gleb Budman original https://www.backblaze.com/blog/how-to-get-your-first-customers/

line outside of Apple

After deciding to build an unlimited backup service and developing our own storage platform, the next step was to get customers and feedback. Not all customers are created equal. Let’s talk about the types, and when and how to attract them.

How to Get Your First Customers

First Step – Don’t Launch Publicly
Launch when you’re ready for the judgments of people who don’t know you at all. Until then, don’t launch. Sign up users and customers either that you know, those you can trust to cut you some slack (while providing you feedback), or at minimum those for whom you can set expectations. For months the Backblaze website was a single page with no ability to get the product and minimal info on what it would be. This is not to counter the Lean Startup ‘iterate quickly with customer feedback’ advice. Rather, this is an acknowledgement that there are different types of feedback required based on your development stage.

Sign Up Your Friends
We knew all of our first customers; they were friends, family, and previous co-workers. Many knew what we were up to and were excited to help us. No magic marketing or tech savviness was required to reach them – we just asked that they try the service. We asked them to provide us feedback on their experience and collected it through email and conversations. While the feedback wasn’t unbiased, it was nonetheless wide-ranging, real, and often insightful. These people were willing to spend time carefully thinking about their feedback and delving deeper into the conversations.

Broaden to Beta
Unless you’re famous or your service costs $1 million per customer, you’ll probably need to expand quickly beyond your friends to build a business – and to get broader feedback. Our next step was to broaden the customer base to beta users.

Opening up the service in beta provides three benefits:

  1. Air cover for the early warts. There are going to be issues, bugs, unnecessarily complicated user flows, and poorly worded text. Beta tells people, “We don’t consider the product ‘done’ and you should expect some of these issues. Please be patient with us.”
  2. A request for feedback. Some people always provide feedback, but beta communicates that you want it.
  3. An awareness opportunity. Opening up in beta provides an early (but not only) opportunity to have an announcement and build awareness.

Pitching Beta to Press
Not all press cares about, or is even willing to cover, beta products. Much of the mainstream press wants to write about services that are fully live, have scale, and are important in the marketplace. However, there are a number of sites that like to cover the leading edge – and that means covering betas. Techcrunch, Ars Technica, and SimpleHelp covered our initial private beta launch. I’ll go into the details of how to work with the press to cover your announcements in a post next month.

Private vs. Public Beta
Both private and public beta provide all three of the benefits above. The difference between the two is that private betas are much more controlled, whereas public ones bring in more users. But this isn’t an either/or – I recommend doing both.

Private Beta
For our original beta in 2008, we decided that we were comfortable with about 1,000 users subscribing to our service. That would provide us with a healthy amount of feedback and get some early adoption, while not overwhelming us or our server capacity, and equally important not causing cash flow issues from having to buy more equipment. So we decided to limit the sign-up to only the first 1,000 people who signed up; then we would shut off sign-ups for a while.

But how do you even get 1,000 people to sign up for your service? In our case, get some major publications to write about our beta. (Note: In a future post I’ll explain exactly how to find and reach out to writers. Sign up to receive all of the entrepreneurial posts in this series.)

Public Beta
For our original service (computer backup), we did not have a public beta; but when we launched Backblaze B2, we had a private and then a public beta. The private beta allowed us to work out early kinks, while the public beta brought us a more varied set of use cases. In public beta, there is no cap on the number of users that may try the service.

While this is a first-class problem to have, if your service is flooded and stops working, it’s still a problem. Think through what you will do if that happens. In our early days, when our system could get overwhelmed by volume, we had a static web page hosted with a different registrar that wouldn’t let customers sign up but would tell them when our service would be open again. When we reached a critical volume level we would redirect to it in order to at least provide status for when we could accept more customers.

Collect Feedback
Since one of the goals of betas is to get feedback, we made sure that we had our email addresses clearly presented on the site so users could send us thoughts. We were most interested in broad qualitative feedback on users’ experience, so all emails went to an internal mailing list that would be read by everyone at Backblaze.

For our B2 public and private betas, we also added an optional short survey to the sign-up process. In order to be considered for the private beta you had to fill the survey out, though we found that 80% of users continued to fill out the survey even when it was not required. This survey had both closed-end questions (“how much data do you have”) and open-ended ones (“what do you want to use cloud storage for?”).

BTW, despite us getting a lot of feedback now via our support team, Twitter, and marketing surveys, we are always open to more – you can email me directly at gleb.budman {at} backblaze.com.

Don’t Throw Away Users
Initially our backup service was available only on Windows, but we had an email sign-up list for people who wanted it for their Mac. This provided us with a sense of market demand and a ready list of folks who could be beta users and early adopters when we had a Mac version. Have a service targeted at doctors but lawyers are expressing interest? Capture that.

Product Launch

When
The first question is “when” to launch. Presuming your service is in ‘public beta’, what is the advantage of moving out of beta and into a “version 1.0”, “gold”, or “public availability”? That depends on your service and customer base. Some services fly through public beta. Gmail, on the other hand, was (in)famous for being in beta for 5 years, despite having over 100 million users.

The term beta says to users, “give us some leeway, but feel free to use the service”. That’s fine for many consumer apps and will have near zero impact on them. However, services aimed at businesses and government will often not be adopted with a beta label as the enterprise customers want to know the company feels the service is ‘ready’. While Backblaze started out as a purely consumer service, because it was a data backup service, it was important for customers to trust that the service was ready.

No product is bug-free. But from a product readiness perspective, the nomenclature should also be a reflection of the quality of the product. You can launch a product with one feature that works well out of beta. But a product with fifty features on which half the users will bump into problems should likely stay in beta. The customer feedback, surveys, and your own internal testing should guide you in determining this quality during the beta. Be careful about “we’ve only seen that one time” or “I haven’t been able to reproduce that on my machine”; those issues are likely to scale with customers when you launch.

How
Launching out of beta can be as simple as removing the beta label from the website/product. However, this can be a great time to reach out to press, write a blog post, and send an email announcement to your customers.

Consider thanking your beta testers somehow; can they get some feature turned out for free, an extension of their trial, or premium support? If nothing else, remember to thank them for their feedback. Users that signed up during your beta are likely the ones who will propel your service. They had the need and interest to both be early adopters and deal with bugs. They are likely the key to getting 1,000 true fans.

The Beginning
The title of this post was “Getting your first customers”, because getting to launch may feel like the peak of your journey when you’re pre-launch, but it really is just the beginning. It’s a step along the journey of building your business. If your launch is wildly successful, enjoy it, work to build on the momentum, but don’t lose track of building your business. If your launch is a dud, go out for a coffee with your team, say “well that sucks”, and then get back to building your business. You can learn a tremendous amount from your early customers, and they can become your biggest fans, but the success of your business will depend on what you continue to do the months and years after your launch.

The post From Idea to Launch: Getting Your First Customers appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

HiveMQ 3.2.5 released

Post Syndicated from The HiveMQ Team original http://www.hivemq.com/blog/hivemq-3-2-5-released/

The HiveMQ team is pleased to announce the availability of HiveMQ 3.2.5. This is a maintenance release for the 3.2 series and brings the following improvements:

  • Fixed an issue that caused cluster nodes to not be operational for a long time after start up
  • Fixed an issue that could cause wildcard (+ operator) subscriptions to get lost
  • Fixed an issue that could cause QoS=1 messages to get lost when using cleanSession=false and shared subscription groups
  • Fixed an issue that could cause the current session count metric to be incorrect
  • Fixed an issue that could lead to QoS=0 message to be resent incorrectly when using shared subscriptions
  • Fixed various issues that could cause false Exceptions to be logged
  • Fixed an issue that could lead to an increase in memory usage when using retained messages
  • Improved documentation
  • Improved logging
  • Performance improvements

You can download the new HiveMQ version here.

We strongly recommend to upgrade if you are an HiveMQ 3.2.x user.

Have a great day,
The HiveMQ Team

DynamoDB Accelerator (DAX) Now Generally Available

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/dynamodb-accelerator-dax-now-generally-available/

Earlier this year I told you about Amazon DynamoDB Accelerator (DAX), a fully-managed caching service that sits in front of (logically speaking) your Amazon DynamoDB tables. DAX returns cached responses in microseconds, making it a great fit for eventually-consistent read-intensive workloads. DAX supports the DynamoDB API, and is seamless and easy to use. As a managed service, you simply create your DAX cluster and use it as the target for your existing reads and writes. You don’t have to worry about patching, cluster maintenance, replication, or fault management.

Now Generally Available
Today I am pleased to announce that DAX is now generally available. We have expanded DAX into additional AWS Regions and used the preview time to fine-tune performance and availability:

Now in Five Regions – DAX is now available in the US East (Northern Virginia), EU (Ireland), US West (Oregon), Asia Pacific (Tokyo), and US West (Northern California) Regions.

In Production – Our preview customers are reporting that they are using DAX in production, that they loved how easy it was to add DAX to their application, and have told us that their apps are now running 10x faster.

Getting Started with DAX
As I outlined in my earlier post, it is easy to use DAX to accelerate your existing DynamoDB applications. You simply create a DAX cluster in the desired region, update your application to reference the DAX SDK for Java (the calls are the same; this is a drop-in replacement), and configure the SDK to use the endpoint to your cluster. As a read-through/write-through cache, DAX seamlessly handles all of the DynamoDB read/write APIs.

We are working on SDK support for other languages, and I will share additional information as it becomes available.

DAX Pricing
You pay for each node in the cluster (see the DynamoDB Pricing page for more information) on a per-hour basis, with prices starting at $0.269 per hour in the US East (Northern Virginia) and US West (Oregon) regions. With DAX, each of the nodes in your cluster serves as a read target and as a failover target for high availability. The DAX SDK is cluster aware and will issue round-robin requests to all nodes in the cluster so that you get to make full use of the cluster’s cache resources.

Because DAX can easily handle sudden spikes in read traffic, you may be able to reduce the amount of provisioned throughput for your tables, resulting in an overall cost savings while still returning results in microseconds.

Jeff;

 

In the Works – AWS Region in Hong Kong

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/in-the-works-aws-region-in-hong-kong/

Last year we launched new AWS Regions in Canada, India, Korea, the UK (London), and the United States (Ohio), and announced that new regions are coming to France (Paris), China (Ningxia), and Sweden (Stockholm).

Coming to Hong Kong in 2018
Today, I am happy to be able to tell you that we are planning to open up an AWS Region in Hong Kong, in 2018. Hong Kong is a leading international financial center, well known for its service oriented economy. It is rated highly on innovation and for ease of doing business. As an evangelist, I get to visit many great cities in the world, and was lucky to have spent some time in Hong Kong back in 2014 and met a number of awesome customers there. Many of these customers have given us feedback that they wanted a local AWS Region.

This will be the eighth AWS Region in Asia Pacific joining six other Regions there — Singapore, Tokyo, Sydney, Beijing, Seoul, and Mumbai, and an additional Region in China (Ningxia) expected to launch in the coming months. Together, these Regions will provide our customers with a total of 19 Availability Zones (AZs) and allow them to architect highly fault tolerant applications.

Today, our infrastructure comprises 43 Availability Zones across 16 geographic regions worldwide, with another three AWS Regions (and eight Availability Zones) in France, China, and Sweden coming online throughout 2017 and 2018, (see the AWS Global Infrastructure page for more info).

We are looking forward to serving new and existing customers in Hong Kong and working with partners across Asia-Pacific. Of course, the new region will also be open to existing AWS customers who would like to process and store data in Hong Kong. Public sector organizations such as government agencies, educational institutions, and nonprofits in Hong Kong will be able to use this region to store sensitive data locally (the AWS in the Public Sector page has plenty of success stories drawn from our worldwide customer base).

If you are a customer or a partner and have specific questions about this Region, you can contact our Hong Kong team.

Help Wanted
If you are interested in learning more about AWS positions in Hong Kong, please visit the Amazon Jobs site and set the location to Hong Kong.

Jeff;

 

Building Loosely Coupled, Scalable, C# Applications with Amazon SQS and Amazon SNS

Post Syndicated from Tara Van Unen original https://aws.amazon.com/blogs/compute/building-loosely-coupled-scalable-c-applications-with-amazon-sqs-and-amazon-sns/

 
Stephen Liedig, Solutions Architect

 

One of the many challenges professional software architects and developers face is how to make cloud-native applications scalable, fault-tolerant, and highly available.

Fundamental to your project success is understanding the importance of making systems highly cohesive and loosely coupled. That means considering the multi-dimensional facets of system coupling to support the distributed nature of the applications that you are building for the cloud.

By that, I mean addressing not only the application-level coupling (managing incoming and outgoing dependencies), but also considering the impacts of of platform, spatial, and temporal coupling of your systems. Platform coupling relates to the interoperability, or lack thereof, of heterogeneous systems components. Spatial coupling deals with managing components at a network topology level or protocol level. Temporal, or runtime coupling, refers to the ability of a component within your system to do any kind of meaningful work while it is performing a synchronous, blocking operation.

The AWS messaging services, Amazon SQS and Amazon SNS, help you deal with these forms of coupling by providing mechanisms for:

  • Reliable, durable, and fault-tolerant delivery of messages between application components
  • Logical decomposition of systems and increased autonomy of components
  • Creating unidirectional, non-blocking operations, temporarily decoupling system components at runtime
  • Decreasing the dependencies that components have on each other through standard communication and network channels

Following on the recent topic, Building Scalable Applications and Microservices: Adding Messaging to Your Toolbox, in this post, I look at some of the ways you can introduce SQS and SNS into your architectures to decouple your components, and show how you can implement them using C#.

Walkthrough

To illustrate some of these concepts, consider a web application that processes customer orders. As good architects and developers, you have followed best practices and made your application scalable and highly available. Your solution included implementing load balancing, dynamic scaling across multiple Availability Zones, and persisting orders in a Multi-AZ Amazon RDS database instance, as in the following diagram.


In this example, the application is responsible for handling and persisting the order data, as well as dealing with increases in traffic for popular items.

One potential point of vulnerability in the order processing workflow is in saving the order in the database. The business expects that every order has been persisted into the database. However, any potential deadlock, race condition, or network issue could cause the persistence of the order to fail. Then, the order is lost with no recourse to restore the order.

With good logging capability, you may be able to identify when an error occurred and which customer’s order failed. This wouldn’t allow you to “restore” the transaction, and by that stage, your customer is no longer your customer.

As illustrated in the following diagram, introducing an SQS queue helps improve your ordering application. Using the queue isolates the processing logic into its own component and runs it in a separate process from the web application. This, in turn, allows the system to be more resilient to spikes in traffic, while allowing work to be performed only as fast as necessary in order to manage costs.


In addition, you now have a mechanism for persisting orders as messages (with the queue acting as a temporary database), and have moved the scope of your transaction with your database further down the stack. In the event of an application exception or transaction failure, this ensures that the order processing can be retired or redirected to the Amazon SQS Dead Letter Queue (DLQ), for re-processing at a later stage. (See the recent post, Using Amazon SQS Dead-Letter Queues to Control Message Failure, for more information on dead-letter queues.)

Scaling the order processing nodes

This change allows you now to scale the web application frontend independently from the processing nodes. The frontend application can continue to scale based on metrics such as CPU usage, or the number of requests hitting the load balancer. Processing nodes can scale based on the number of orders in the queue. Here is an example of scale-in and scale-out alarms that you would associate with the scaling policy.

Scale-out Alarm

aws cloudwatch put-metric-alarm --alarm-name AddCapacityToCustomerOrderQueue --metric-name ApproximateNumberOfMessagesVisible --namespace "AWS/SQS" 
--statistic Average --period 300 --threshold 3 --comparison-operator GreaterThanOrEqualToThreshold --dimensions Name=QueueName,Value=customer-orders
--evaluation-periods 2 --alarm-actions <arn of the scale-out autoscaling policy>

Scale-in Alarm

aws cloudwatch put-metric-alarm --alarm-name RemoveCapacityFromCustomerOrderQueue --metric-name ApproximateNumberOfMessagesVisible --namespace "AWS/SQS" 
 --statistic Average --period 300 --threshold 1 --comparison-operator LessThanOrEqualToThreshold --dimensions Name=QueueName,Value=customer-orders
 --evaluation-periods 2 --alarm-actions <arn of the scale-in autoscaling policy>

In the above example, use the ApproximateNumberOfMessagesVisible metric to discover the queue length and drive the scaling policy of the Auto Scaling group. Another useful metric is ApproximateAgeOfOldestMessage, when applications have time-sensitive messages and developers need to ensure that messages are processed within a specific time period.

Scaling the order processing implementation

On top of scaling at an infrastructure level using Auto Scaling, make sure to take advantage of the processing power of your Amazon EC2 instances by using as many of the available threads as possible. There are several ways to implement this. In this post, we build a Windows service that uses the BackgroundWorker class to process the messages from the queue.

Here’s a closer look at the implementation. In the first section of the consuming application, use a loop to continually poll the queue for new messages, and construct a ReceiveMessageRequest variable.

public static void PollQueue()
{
    while (_running)
    {
        Task<ReceiveMessageResponse> receiveMessageResponse;

        // Pull messages off the queue
        using (var sqs = new AmazonSQSClient())
        {
            const int maxMessages = 10;  // 1-10

            //Receiving a message
            var receiveMessageRequest = new ReceiveMessageRequest
            {
                // Get URL from Configuration
                QueueUrl = _queueUrl, 
                // The maximum number of messages to return. 
                // Fewer messages might be returned. 
                MaxNumberOfMessages = maxMessages, 
                // A list of attributes that need to be returned with message.
                AttributeNames = new List<string> { "All" },
                // Enable long polling. 
                // Time to wait for message to arrive on queue.
                WaitTimeSeconds = 5 
            };

            receiveMessageResponse = sqs.ReceiveMessageAsync(receiveMessageRequest);
        }

The WaitTimeSeconds property of the ReceiveMessageRequest specifies the duration (in seconds) that the call waits for a message to arrive in the queue before returning a response to the calling application. There are a few benefits to using long polling:

  • It reduces the number of empty responses by allowing SQS to wait until a message is available in the queue before sending a response.
  • It eliminates false empty responses by querying all (rather than a limited number) of the servers.
  • It returns messages as soon any message becomes available.

For more information, see Amazon SQS Long Polling.

After you have returned messages from the queue, you can start to process them by looping through each message in the response and invoking a new BackgroundWorker thread.

// Process messages
if (receiveMessageResponse.Result.Messages != null)
{
    foreach (var message in receiveMessageResponse.Result.Messages)
    {
        Console.WriteLine("Received SQS message, starting worker thread");

        // Create background worker to process message
        BackgroundWorker worker = new BackgroundWorker();
        worker.DoWork += (obj, e) => ProcessMessage(message);
        worker.RunWorkerAsync();
    }
}
else
{
    Console.WriteLine("No messages on queue");
}

The event handler, ProcessMessage, is where you implement business logic for processing orders. It is important to have a good understanding of how long a typical transaction takes so you can set a message VisibilityTimeout that is long enough to complete your operation. If order processing takes longer than the specified timeout period, the message becomes visible on the queue. Other nodes may pick it and process the same order twice, leading to unintended consequences.

Handling Duplicate Messages

In order to manage duplicate messages, seek to make your processing application idempotent. In mathematics, idempotent describes a function that produces the same result if it is applied to itself:

f(x) = f(f(x))

No matter how many times you process the same message, the end result is the same (definition from Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions, Hohpe and Wolf, 2004).

There are several strategies you could apply to achieve this:

  • Create messages that have inherent idempotent characteristics. That is, they are non-transactional in nature and are unique at a specified point in time. Rather than saying “place new order for Customer A,” which adds a duplicate order to the customer, use “place order <orderid> on <timestamp> for Customer A,” which creates a single order no matter how often it is persisted.
  • Deliver your messages via an Amazon SQS FIFO queue, which provides the benefits of message sequencing, but also mechanisms for content-based deduplication. You can deduplicate using the MessageDeduplicationId property on the SendMessage request or by enabling content-based deduplication on the queue, which generates a hash for MessageDeduplicationId, based on the content of the message, not the attributes.
var sendMessageRequest = new SendMessageRequest
{
    QueueUrl = _queueUrl,
    MessageBody = JsonConvert.SerializeObject(order),
    MessageGroupId = Guid.NewGuid().ToString("N"),
    MessageDeduplicationId = Guid.NewGuid().ToString("N")
};
  • If using SQS FIFO queues is not an option, keep a message log of all messages attributes processed for a specified period of time, as an alternative to message deduplication on the receiving end. Verifying the existence of the message in the log before processing the message adds additional computational overhead to your processing. This can be minimized through low latency persistence solutions such as Amazon DynamoDB. Bear in mind that this solution is dependent on the successful, distributed transaction of the message and the message log.

Handling exceptions

Because of the distributed nature of SQS queues, it does not automatically delete the message. Therefore, you must explicitly delete the message from the queue after processing it, using the message ReceiptHandle property (see the following code example).

However, if at any stage you have an exception, avoid handling it as you normally would. The intention is to make sure that the message ends back on the queue, so that you can gracefully deal with intermittent failures. Instead, log the exception to capture diagnostic information, and swallow it.

By not explicitly deleting the message from the queue, you can take advantage of the VisibilityTimeout behavior described earlier. Gracefully handle the message processing failure and make the unprocessed message available to other nodes to process.

In the event that subsequent retries fail, SQS automatically moves the message to the configured DLQ after the configured number of receives has been reached. You can further investigate why the order process failed. Most importantly, the order has not been lost, and your customer is still your customer.

private static void ProcessMessage(Message message)
{
    using (var sqs = new AmazonSQSClient())
    {
        try
        {
            Console.WriteLine("Processing message id: {0}", message.MessageId);

            // Implement messaging processing here
            // Ensure no downstream resource contention (parallel processing)
            // <your order processing logic in here…>
            Console.WriteLine("{0} Thread {1}: {2}", DateTime.Now.ToString("s"), Thread.CurrentThread.ManagedThreadId, message.MessageId);
            
            // Delete the message off the queue. 
            // Receipt handle is the identifier you must provide 
            // when deleting the message.
            var deleteRequest = new DeleteMessageRequest(_queueName, message.ReceiptHandle);
            sqs.DeleteMessageAsync(deleteRequest);
            Console.WriteLine("Processed message id: {0}", message.MessageId);

        }
        catch (Exception ex)
        {
            // Do nothing.
            // Swallow exception, message will return to the queue when 
            // visibility timeout has been exceeded.
            Console.WriteLine("Could not process message due to error. Exception: {0}", ex.Message);
        }
    }
}

Using SQS to adapt to changing business requirements

One of the benefits of introducing a message queue is that you can accommodate new business requirements without dramatically affecting your application.

If, for example, the business decided that all orders placed over $5000 are to be handled as a priority, you could introduce a new “priority order” queue. The way the orders are processed does not change. The only significant change to the processing application is to ensure that messages from the “priority order” queue are processed before the “standard order” queue.

The following diagram shows how this logic could be isolated in an “order dispatcher,” whose only purpose is to route order messages to the appropriate queue based on whether the order exceeds $5000. Nothing on the web application or the processing nodes changes other than the target queue to which the order is sent. The rates at which orders are processed can be achieved by modifying the poll rates and scalability settings that I have already discussed.

Extending the design pattern with Amazon SNS

Amazon SNS supports reliable publish-subscribe (pub-sub) scenarios and push notifications to known endpoints across a wide variety of protocols. It eliminates the need to periodically check or poll for new information and updates. SNS supports:

  • Reliable storage of messages for immediate or delayed processing
  • Publish / subscribe – direct, broadcast, targeted “push” messaging
  • Multiple subscriber protocols
  • Amazon SQS, HTTP, HTTPS, email, SMS, mobile push, AWS Lambda

With these capabilities, you can provide parallel asynchronous processing of orders in the system and extend it to support any number of different business use cases without affecting the production environment. This is commonly referred to as a “fanout” scenario.

Rather than your web application pushing orders to a queue for processing, send a notification via SNS. The SNS messages are sent to a topic and then replicated and pushed to multiple SQS queues and Lambda functions for processing.

As the diagram above shows, you have the development team consuming “live” data as they work on the next version of the processing application, or potentially using the messages to troubleshoot issues in production.

Marketing is consuming all order information, via a Lambda function that has subscribed to the SNS topic, inserting the records into an Amazon Redshift warehouse for analysis.

All of this, of course, is happening without affecting your order processing application.

Summary

While I haven’t dived deep into the specifics of each service, I have discussed how these services can be applied at an architectural level to build loosely coupled systems that facilitate multiple business use cases. I’ve also shown you how to use infrastructure and application-level scaling techniques, so you can get the most out of your EC2 instances.

One of the many benefits of using these managed services is how quickly and easily you can implement powerful messaging capabilities in your systems, and lower the capital and operational costs of managing your own messaging middleware.

Using Amazon SQS and Amazon SNS together can provide you with a powerful mechanism for decoupling application components. This should be part of design considerations as you architect for the cloud.

For more information, see the Amazon SQS Developer Guide and Amazon SNS Developer Guide. You’ll find tutorials on all the concepts covered in this post, and more. To can get started using the AWS console or SDK of your choice visit:

Happy messaging!

Sync vs. Backup vs. Storage

Post Syndicated from Yev original https://www.backblaze.com/blog/sync-vs-backup-vs-storage/

Cloud Sync vs. Cloud Backup vs. Cloud Storage

Google Drive recently announced their new Backup and Sync feature for Google Drive, which allows users to select folders on their computer that they want to back up to their Google Drive account (note: these files count against your Google Drive storage limit). Whenever new backup services are announced, we get a lot of questions so I thought we should take a minute to review the differences in cloud based services.

What is the Cloud? Sync Vs Backup Vs Storage

There is still a lot of confusion in the space about what exactly the “cloud” is and how different services interact with it. When folks use a syncing and sharing service like Dropbox, Box, Google Drive, OneDrive or any of the others, they often assume those are acting as a cloud backup solution as well. Adding to the confusion, cloud storage services are often the backend for backup and sync services as well as standalone services. To help sort this out, we’ll define some of the terms below as they apply to a traditional computer set-up with a bunch of apps and data.

Cloud Sync (ex. Dropbox, iCloud Drive, OneDrive, Box, Google Drive) – these services sync folders on your computer to folders on other machines or to the cloud – allowing users to work from a folder or directory across devices. Typically these services have tiered pricing, meaning you pay for the amount of data you store with the service. If there is data loss, sometimes these services even have a rollback feature, of course only files that are in the synced folders are available to be recovered.

Cloud Backup (ex. Backblaze Cloud Backup, Mozy, Carbonite) – these services work in the background automatically. The user does not need to take any action like setting up specific folders. Backup services typically back up any new or changed data on your computer to another location. Before the cloud took off, that location was primarily a CD or an external hard drive – but as cloud storage became more readily available it became the most popular storage medium. Typically these services have fixed pricing, and if there is a system crash or data loss, all backed up data is available for restore. In addition, these services have rollback features in case there is data loss / accidental file deletion.

Cloud Storage (ex. Backblaze B2, Amazon S3, Microsoft Azure) – these services are where many online backup and syncing and sharing services store data. Cloud storage providers typically serve as the endpoint for data storage. These services typically provide APIs, CLIs, and access points for individuals and developers to tie in their cloud storage offerings directly. These services are priced “per GB” meaning you pay for the amount of storage that you use. Since these services are designed for high-availability and durability, data can live solely on these services – though we still recommend having multiple copies of your data, just in case.

What Should You Use?

Backblaze strongly believes in a 3-2-1 Backup Strategy. A 3-2-1 strategy means having at least 3 total copies of your data, 2 of which are local but on different mediums (e.g. an external hard drive in addition to your computer’s local drive), and at least 1 copy offsite. The best setup is data on your computer, a copy on a hard drive that lives somewhere not inside your computer, and another copy with a cloud backup provider. Backblaze Cloud Backup is a great compliment to other services, like Time Machine, Dropbox, and even the free-tiers of cloud storage services.

What is The Difference Between Cloud Sync and Backup?

Let’s take a look at some sync setups that we see fairly frequently.

Example 1) Users have one folder on their computer that is designated for Dropbox, Google Drive, OneDrive, or one of the other syncing/sharing services. Users save or place data into those directories when they want them to appear on other devices. Often these users are using the free-tier of those syncing and sharing services and only have a few GB of data uploaded in them.

Example 2) Users are paying for extended storage for Dropbox, Google Drive, OneDrive, etc… and use those folders as the “Documents” folder – essentially working out of those directories. Files in that folder are available across devices, however, files outside of that folder (e.g. living on the computer’s desktop or anywhere else) are not synced or stored by the service.

What both examples are missing however is the backup of photos, movies, videos, and the rest of the data on their computer. That’s where cloud backup providers excel, by automatically backing up user data with little or no set-up, and no need for the dragging-and-dropping of files. Backblaze actually scans your hard drive to find all the data, regardless of where it might be hiding. The results are, all the user’s data is kept in the Backblaze cloud and the portion of the data that is synced is also kept in that provider’s cloud – giving the user another layer of redundancy. Best of all, Backblaze will actually back up your Dropbox, iCloud Drive, Google Drive, and OneDrive folders.

Data Recovery

The most important feature to think about is how easy it is to get your data back from all of these services. With sync and share services, retrieving a lot of data, especially if you are in a high-data tier, can be cumbersome and take awhile. Generally, the sync and share services only allow customers to download files over the Internet. If you are trying to download more than a couple gigabytes of data, the process can take time and can be fraught with errors.

With cloud storage services, you can usually only retrieve data over the Internet as well, and you pay for both the storage and the egress of the data, so retrieving a large amount of data can be both expensive and time consuming.

Cloud backup services will enable you to download files over the internet too and can also suffer from long download times. At Backblaze we never want our customers to feel like we’re holding their data hostage, which is why we have a lot of restore options, including our Restore Return Refund policy, which allows people to restore their data via a USB Hard Drive, and then return that drive to us for a refund. Cloud sync providers do not provide this capability.

One popular data recovery use case we’ve seen when a person has a lot of data to restore is to download just the files that are needed immediately, and then order a USB Hard Drive restore for the remaining files that are not as time sensitive. The user gets all their files back in a few days, and their network is spared the download charges.

The bottom line is that all of these services have merit for different use-cases. Have questions about which is best for you? Sound off in the comments below!

The post Sync vs. Backup vs. Storage appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Roku Sales Banned in Mexico Over Piracy Concerns

Post Syndicated from Ernesto original https://torrentfreak.com/roku-sales-banned-in-mexico-over-piracy-concerns-170619/

Online streaming piracy is on the rise and many people use dedicated media players to watch it through their regular TV.

While a lot of attention has been on Kodi, there are other players on the market that allow people to do the same. Roku, for example, has been doing very well too.

Like Kodi, Roku media players don’t offer any pirated content out of the box. In fact, they can be hooked up to a wide variety of legal streaming options including HBO Go, Hulu, and Netflix. Still, there is also a market for third-party pirate channels, outside the Roku Channel Store, which turn the boxes into pirate tools.

This pirate angle has now resulted in a ban on Roku sales in Mexico, according to a report in Milenio.

The ban was issued by the Superior Court of Justice of the City of Mexico, following a complaint from Cablevision. The order in question prohibits stores such as Amazon, Liverpool, El Palacio de Hierro, and Sears from importing and selling the devices.

In addition, the court also instructs banks including Banorte and BBVA Bancomer to stop processing payments from a long list of accounts linked to pirated services on Roku.

The main reason for the order is the availability of pirated content through Roku, but banning the device itself is utterly comprehensive. It would be similar to banning all Android-based devices because certain apps allow users to stream copyrighted content without permission.

Roku

Roku has yet to release an official statement on the court order. TorrentFreak reached out to the company but hadn’t heard back at the time of publication.

It’s clear, however, that streaming players are among the top concerns for copyright holders. Motion Picture Association boss Stan McCoy recently characterized the use of streaming players to access infringing content as “Piracy 3.0.

“If you think of old-fashioned peer-to-peer piracy as 1.0, and then online illegal streaming websites as 2.0, in the audio-visual sector, in particular, we now face challenge number 3.0, which is what I’ll call the challenge of illegal streaming devices,” McCoy said earlier this month.

Unlike the court order in Mexico, however, McCoy stressed that the devices themselves, and software such as Kodi, are ‘probably’ not illegal. However, copyright-infringing pirate add-ons have the capability to turn them into an unprecedented piracy threat.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

The Pirate Bay Isn’t Affected By Adverse Court Rulings – Everyone Else Is

Post Syndicated from Andy original https://torrentfreak.com/the-pirate-bay-isnt-affected-by-adverse-court-rulings-everyone-else-is-170618/

For more than a decade The Pirate Bay has been the world’s most controversial site. Delivering huge quantities of copyrighted content to the masses, the platform is revered and reviled across the copyright spectrum.

Its reputation is one of a defiant Internet swashbuckler, but due to changes in how the site has been run in more recent times, its current philosophy is more difficult to gauge. What has never been in doubt, however, is the site’s original intent to be as provocative as possible.

Through endless publicity stunts, some real, some just for the ‘lulz’, The Pirate Bay managed to attract a massive audience, all while incurring the wrath of every major copyright holder in the world.

Make no mistake, they all queued up to strike back, but every subsequent rightsholder action was met by a Pirate Bay middle finger, two fingers, or chin flick, depending on the mood of the day. This only served to further delight the masses, who happily spread the word while keeping their torrents flowing.

This vicious circle of being targeted by the entertainment industries, mocking them, and then reaping the traffic benefits, developed into the cheapest long-term marketing campaign the Internet had ever seen. But nothing is ever truly for free and there have been consequences.

After taunting Hollywood and the music industry with its refusals to capitulate, endless legal action that the site would have ordinarily been forced to participate in largely took place without The Pirate Bay being present. It doesn’t take a law degree to work out what happened in each and every one of those cases, whatever complex route they took through the legal system. No defense, no win.

For example, the web-blocking phenomenon across the UK, Europe, Asia and Australia was driven by the site’s absolute resilience and although there would clearly have been other scapegoats had The Pirate Bay disappeared, the site was the ideal bogeyman the copyright lobby required to move forward.

Filing blocking lawsuits while bringing hosts, advertisers, and ISPs on board for anti-piracy initiatives were also made easier with the ‘evil’ Pirate Bay still online. Immune from every anti-piracy technique under the sun, the existence of the platform in the face of all onslaughts only strengthened the cases of those arguing for even more drastic measures.

Over a decade, this has meant a significant tightening of the sharing and streaming climate. Without any big legislative changes but plenty of case law against The Pirate Bay, web-blocking is now a walk in the park, ad hoc domain seizures are a fairly regular occurrence, and few companies want to host sharing sites. Advertisers and brands are also hesitant over where they place their ads. It’s a very different world to the one of 10 years ago.

While it would be wrong to attribute every tightening of the noose to the actions of The Pirate Bay, there’s little doubt that the site and its chaotic image played a huge role in where copyright enforcement is today. The platform set out to provoke and succeeded in every way possible, gaining supporters in their millions. It could also be argued it kicked a hole in a hornets’ nest, releasing the hell inside.

But perhaps the site’s most amazing achievement is the way it has managed to stay online, despite all the turmoil.

This week yet another ruling, this time from the powerful European Court of Justice, found that by offering links in the manner it does, The Pirate Bay and other sites are liable for communicating copyright works to the public. Of course, this prompted the usual swathe of articles claiming that this could be the final nail in the site’s coffin.

Wrong.

In common with every ruling, legal defeat, and legislative restriction put in place due to the site’s activities, this week’s decision from the ECJ will have zero effect on the Pirate Bay’s availability. For right or wrong, the site was breaking the law long before this ruling and will continue to do so until it decides otherwise.

What we have instead is a further tightened legal landscape that will have a lasting effect on everything BUT the site, including weaker torrent sites, Internet users, and user-uploaded content sites such as YouTube.

With The Pirate Bay carrying on regardless, that is nothing short of remarkable.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

New – Auto Scaling for Amazon DynamoDB

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-auto-scaling-for-amazon-dynamodb/

Amazon DynamoDB has more than one hundred thousand customers, spanning a wide range of industries and use cases. These customers depend on DynamoDB’s consistent performance at any scale and presence in 16 geographic regions around the world. A recent trend we’ve been observing is customers using DynamoDB to power their serverless applications. This is a good match: with DynamoDB, you don’t have to think about things like provisioning servers, performing OS and database software patching, or configuring replication across availability zones to ensure high availability – you can simply create tables and start adding data, and let DynamoDB handle the rest.

DynamoDB provides a provisioned capacity model that lets you set the amount of read and write capacity required by your applications. While this frees you from thinking about servers and enables you to change provisioning for your table with a simple API call or button click in the AWS Management Console, customers have asked us how we can make managing capacity for DynamoDB even easier.

Today we are introducing Auto Scaling for DynamoDB to help automate capacity management for your tables and global secondary indexes. You simply specify the desired target utilization and provide upper and lower bounds for read and write capacity. DynamoDB will then monitor throughput consumption using Amazon CloudWatch alarms and then will adjust provisioned capacity up or down as needed. Auto Scaling will be on by default for all new tables and indexes, and you can also configure it for existing ones.

Even if you’re not around, DynamoDB Auto Scaling will be monitoring your tables and indexes to automatically adjust throughput in response to changes in application traffic. This can make it easier to administer your DynamoDB data, help you maximize availability for your applications, and help you reduce your DynamoDB costs.

Let’s see how it works…

Using Auto Scaling
The DynamoDB Console now proposes a comfortable set of default parameters when you create a new table. You can accept them as-is or you can uncheck Use default settings and enter your own parameters:

Here’s how you enter your own parameters:

Target utilization is expressed in terms of the ratio of consumed capacity to provisioned capacity. The parameters above would allow for sufficient headroom to allow consumed capacity to double due to a burst in read or write requests (read Capacity Unit Calculations to learn more about the relationship between DynamoDB read and write operations and provisioned capacity). Changes in provisioned capacity take place in the background.

Auto Scaling in Action
In order to see this important new feature in action, I followed the directions in the Getting Started Guide. I launched a fresh EC2 instance, installed (sudo pip install boto3) and configured (aws configure) the AWS SDK for Python. Then I used the code in the Python and DynamoDB section to create and populate a table with some data, and manually configured the table for 5 units each of read and write capacity.

I took a quick break in order to have clean, straight lines for the CloudWatch metrics so that I could show the effect of Auto Scaling. Here’s what the metrics look like before I started to apply a load:

I modified the code in Step 3 to continually issue queries for random years in the range of 1920 to 2007, ran a single copy of the code, and checked the read metrics a minute or two later:

The consumed capacity is higher than the provisioned capacity, resulting in a large number of throttled reads. Time for Auto Scaling!

I returned to the console and clicked on the Capacity tab for my table. Then I clicked on Read capacity, accepted the default values, and clicked on Save:

DynamoDB created a new IAM role (DynamoDBAutoscaleRole) and a pair of CloudWatch alarms to manage the Auto Scaling of read capacity:

DynamoDB Auto Scaling will manage the thresholds for the alarms, moving them up and down as part of the scaling process. The first alarm was triggered and the table state changed to Updating while additional read capacity was provisioned:

The change was visible in the read metrics within minutes:

I started a couple of additional copies of my modified query script and watched as additional capacity was provisioned, as indicated by the red line:

I killed all of the scripts and turned my attention to other things while waiting for the scale-down alarm to trigger. Here’s what I saw when I came back:

The next morning I checked my Scaling activities and saw that the alarm had triggered several more times overnight:

This was also visible in the metrics:

Until now, you would prepare for this situation by setting your read capacity well about your expected usage, and pay for the excess capacity (the space between the blue line and the red line). Or, you might set it too low, forget to monitor it, and run out of capacity when traffic picked up. With Auto Scaling you can get the best of both worlds: an automatic response when an increase in demand suggests that more capacity is needed, and another automated response when the capacity is no longer needed.

Things to Know
DynamoDB Auto Scaling is designed to accommodate request rates that vary in a somewhat predictable, generally periodic fashion. If you need to accommodate unpredictable bursts of read activity, you should use Auto Scaling in combination with DAX (read Amazon DynamoDB Accelerator (DAX) – In-Memory Caching for Read-Intensive Workloads to learn more). Also, the AWS SDKs will detect throttled read and write requests and retry them after a suitable delay.

I mentioned the DynamoDBAutoscaleRole earlier. This role provides Auto Scaling with the privileges that it needs to have in order for it to be able to scale your tables and indexes up and down. To learn more about this role and the permissions that it uses, read Grant User Permissions for DynamoDB Auto Scaling.

Auto Scaling has complete CLI and API support, including the ability to enable and disable the Auto Scaling policies. If you have some predictable, time-bound spikes in traffic, you can programmatically disable an Auto Scaling policy, provision higher throughput for a set period of time, and then enable Auto Scaling again later.

As noted on the Limits in DynamoDB page, you can increase provisioned capacity as often as you would like and as high as you need (subject to per-account limits that we can increase on request). You can decrease capacity up to nine times per day for each table or global secondary index.

You pay for the capacity that you provision, at the regular DynamoDB prices. You can also purchase DynamoDB Reserved Capacity to further savings.

Available Now
This feature is available now in all regions and you can start using it today!

Jeff;

How to Deploy Local Administrator Password Solution with AWS Microsoft AD

Post Syndicated from Dragos Madarasan original https://aws.amazon.com/blogs/security/how-to-deploy-local-administrator-password-solution-with-aws-microsoft-ad/

Local Administrator Password Solution (LAPS) from Microsoft simplifies password management by allowing organizations to use Active Directory (AD) to store unique passwords for computers. Typically, an organization might reuse the same local administrator password across the computers in an AD domain. However, this approach represents a security risk because it can be exploited during lateral escalation attacks. LAPS solves this problem by creating unique, randomized passwords for the Administrator account on each computer and storing it encrypted in AD.

Deploying LAPS with AWS Microsoft AD requires the following steps:

  1. Install the LAPS binaries on instances joined to your AWS Microsoft AD domain. The binaries add additional client-side extension (CSE) functionality to the Group Policy client.
  2. Extend the AWS Microsoft AD schema. LAPS requires new AD attributes to store an encrypted password and its expiration time.
  3. Configure AD permissions and delegate the ability to retrieve the local administrator password for IT staff in your organization.
  4. Configure Group Policy on instances joined to your AWS Microsoft AD domain to enable LAPS. This configures the Group Policy client to process LAPS settings and uses the binaries installed in Step 1.

The following diagram illustrates the setup that I will be using throughout this post and the associated tasks to set up LAPS. Note that the AWS Directory Service directory is deployed across multiple Availability Zones, and monitoring automatically detects and replaces domain controllers that fail.

Diagram illustrating this blog post's solution

In this blog post, I explain the prerequisites to set up Local Administrator Password Solution, demonstrate the steps involved to update the AD schema on your AWS Microsoft AD domain, show how to delegate permissions to IT staff and configure LAPS via Group Policy, and demonstrate how to retrieve the password using the graphical user interface or with Windows PowerShell.

This post assumes you are familiar with Lightweight Directory Access Protocol Data Interchange Format (LDIF) files and AWS Microsoft AD. If you need more of an introduction to Directory Service and AWS Microsoft AD, see How to Move More Custom Applications to the AWS Cloud with AWS Directory Service, which introduces working with schema changes in AWS Microsoft AD.

Prerequisites

In order to implement LAPS, you must use AWS Directory Service for Microsoft Active Directory (Enterprise Edition), also known as AWS Microsoft AD. Any instance on which you want to configure LAPS must be joined to your AWS Microsoft AD domain. You also need a Management instance on which you install the LAPS management tools.

In this post, I use an AWS Microsoft AD domain called example.com that I have launched in the EU (London) region. To see which the regions in which Directory Service is available, see AWS Regions and Endpoints.

Screenshot showing the AWS Microsoft AD domain example.com used in this blog post

In addition, you must have at least two instances launched in the same region as the AWS Microsoft AD domain. To join the instances to your AWS Microsoft AD domain, you have two options:

  1. Use the Amazon EC2 Systems Manager (SSM) domain join feature. To learn more about how to set up domain join for EC2 instances, see joining a Windows Instance to an AWS Directory Service Domain.
  2. Manually configure the DNS server addresses in the Internet Protocol version 4 (TCP/IPv4) settings of the network card to use the AWS Microsoft AD DNS addresses (172.31.9.64 and 172.31.16.191, for this blog post) and perform a manual domain join.

For the purpose of this post, my two instances are:

  1. A Management instance on which I will install the management tools that I have tagged as Management.
  2. A Web Server instance on which I will be deploying the LAPS binary.

Screenshot showing the two EC2 instances used in this post

Implementing the solution

 

1. Install the LAPS binaries on instances joined to your AWS Microsoft AD domain by using EC2 Run Command

LAPS binaries come in the form of an MSI installer and can be downloaded from the Microsoft Download Center. You can install the LAPS binaries manually, with an automation service such as EC2 Run Command, or with your existing software deployment solution.

For this post, I will deploy the LAPS binaries on my Web Server instance (i-0b7563d0f89d3453a) by using EC2 Run Command:

  1. While signed in to the AWS Management Console, choose EC2. In the Systems Manager Services section of the navigation pane, choose Run Command.
  2. Choose Run a command, and from the Command document list, choose AWS-InstallApplication.
  3. From Target instances, choose the instance on which you want to deploy the LAPS binaries. In my case, I will be selecting the instance tagged as Web Server. If you do not see any instances listed, make sure you have met the prerequisites for Amazon EC2 Systems Manager (SSM) by reviewing the Systems Manager Prerequisites.
  4. For Action, choose Install, and then stipulate the following values:
    • Parameters: /quiet
    • Source: https://download.microsoft.com/download/C/7/A/C7AAD914-A8A6-4904-88A1-29E657445D03/LAPS.x64.msi
    • Source Hash: f63ebbc45e2d080630bd62a195cd225de734131a56bb7b453c84336e37abd766
    • Comment: LAPS deployment

Leave the other options with the default values and choose Run. The AWS Management Console will return a Command ID, which will initially have a status of In Progress. It should take less than 5 minutes to download and install the binaries, after which the Command ID will update its status to Success.

Status showing the binaries have been installed successfully

If the Command ID runs for more than 5 minutes or returns an error, it might indicate a problem with the installer. To troubleshoot, review the steps in Troubleshooting Systems Manager Run Command.

To verify the binaries have been installed successfully, open Control Panel and review the recently installed applications in Programs and Features.

Screenshot of Control Panel that confirms LAPS has been installed successfully

You should see an entry for Local Administrator Password Solution with a version of 6.2.0.0 or newer.

2. Extend the AWS Microsoft AD schema

In the previous section, I used EC2 Run Command to install the LAPS binaries on an EC2 instance. Now, I am ready to extend the schema in an AWS Microsoft AD domain. Extending the schema is a requirement because LAPS relies on new AD attributes to store the encrypted password and its expiration time.

In an on-premises AD environment, you would update the schema by running the Update-AdmPwdADSchema Windows PowerShell cmdlet with schema administrator credentials. Because AWS Microsoft AD is a managed service, I do not have permissions to update the schema directly. Instead, I will update the AD schema from the Directory Service console by importing an LDIF file. If you are unfamiliar with schema updates or LDIF files, see How to Move More Custom Applications to the AWS Cloud with AWS Directory Service.

To make things easier for you, I am providing you with a sample LDIF file that contains the required AD schema changes. Using Notepad or a similar text editor, open the SchemaChanges-0517.ldif file and update the values of dc=example,dc=com with your own AWS Microsoft AD domain and suffix.

After I update the LDIF file with my AWS Microsoft AD details, I import it by using the AWS Management Console:

  1. On the Directory Service console, select from the list of directories in the Microsoft AD directory by choosing its identifier (it will look something like d-534373570ea).
  2. On the Directory details page, choose the Schema extensions tab and choose Upload and update schema.
    Screenshot showing the "Upload and update schema" option
  3. When prompted for the LDIF file that contains the changes, choose the sample LDIF file.
  4. In the background, the LDIF file is validated for errors and a backup of the directory is created for recovery purposes. Updating the schema might take a few minutes and the status will change to Updating Schema. When the process has completed, the status of Completed will be displayed, as shown in the following screenshot.

Screenshot showing the schema updates in progress
When the process has completed, the status of Completed will be displayed, as shown in the following screenshot.

Screenshot showing the process has completed

If the LDIF file contains errors or the schema extension fails, the Directory Service console will generate an error code and additional debug information. To help troubleshoot error messages, see Schema Extension Errors.

The sample LDIF file triggers AWS Microsoft AD to perform the following actions:

  1. Create the ms-Mcs-AdmPwd attribute, which stores the encrypted password.
  2. Create the ms-Mcs-AdmPwdExpirationTime attribute, which stores the time of the password’s expiration.
  3. Add both attributes to the Computer class.

3. Configure AD permissions

In the previous section, I updated the AWS Microsoft AD schema with the required attributes for LAPS. I am now ready to configure the permissions for administrators to retrieve the password and for computer accounts to update their password attribute.

As part of configuring AD permissions, I grant computers the ability to update their own password attribute and specify which security groups have permissions to retrieve the password from AD. As part of this process, I run Windows PowerShell cmdlets that are not installed by default on Windows Server.

Note: To learn more about Windows PowerShell and the concept of a cmdlet (pronounced “command-let”), go to Getting Started with Windows PowerShell.

Before getting started, I need to set up the required tools for LAPS on my Management instance, which must be joined to the AWS Microsoft AD domain. I will be using the same LAPS installer that I downloaded from the Microsoft LAPS website. In my Management instance, I have manually run the installer by clicking the LAPS.x64.msi file. On the Custom Setup page of the installer, under Management Tools, for each option I have selected Install on local hard drive.

Screenshot showing the required management tools

In the preceding screenshot, the features are:

  • The fat client UI – A simple user interface for retrieving the password (I will use it at the end of this post).
  • The Windows PowerShell module – Needed to run the commands in the next sections.
  • The GPO Editor templates – Used to configure Group Policy objects.

The next step is to grant computers in the Computers OU the permission to update their own attributes. While connected to my Management instance, I go to the Start menu and type PowerShell. In the list of results, right-click Windows PowerShell and choose Run as administrator and then Yes when prompted by User Account Control.

In the Windows PowerShell prompt, I type the following command.

Import-module AdmPwd.PS

Set-AdmPwdComputerSelfPermission –OrgUnit “OU=Computers,OU=MyMicrosoftAD,DC=example,DC=com

To grant the administrator group called Admins the permission to retrieve the computer password, I run the following command in the Windows PowerShell prompt I previously started.

Import-module AdmPwd.PS

Set-AdmPwdReadPasswordPermission –OrgUnit “OU=Computers, OU=MyMicrosoftAD,DC=example,DC=com” –AllowedPrincipals “Admins”

4. Configure Group Policy to enable LAPS

In the previous section, I deployed the LAPS management tools on my management instance, granted the computer accounts the permission to self-update their local administrator password attribute, and granted my Admins group permissions to retrieve the password.

Note: The following section addresses the Group Policy Management Console and Group Policy objects. If you are unfamiliar with or wish to learn more about these concepts, go to Get Started Using the GPMC and Group Policy for Beginners.

I am now ready to enable LAPS via Group Policy:

  1. On my Management instance (i-03b2c5d5b1113c7ac), I have installed the Group Policy Management Console (GPMC) by running the following command in Windows PowerShell.
Install-WindowsFeature –Name GPMC
  1. Next, I have opened the GPMC and created a new Group Policy object (GPO) called LAPS GPO.
  2. In the Local Group Policy Editor, I navigate to Computer Configuration > Policies > Administrative Templates > LAPS. I have configured the settings using the values in the following table.

Setting

State

Options

Password Settings

Enabled

Complexity: large letters, small letters, numbers, specials

Do not allow password expiration time longer than required by policy

Enabled

N/A

Enable local admin password management

Enabled

N/A

  1. Next, I need to link the GPO to an organizational unit (OU) in which my machine accounts sit. In your environment, I recommend testing the new settings on a test OU and then deploying the GPO to production OUs.

Note: If you choose to create a new test organizational unit, you must create it in the OU that AWS Microsoft AD delegates to you to manage. For example, if your AWS Microsoft AD directory name were example.com, the test OU path would be example.com/example/Computers/Test.

  1. To test that LAPS works, I need to make sure the computer has received the new policy by forcing a Group Policy update. While connected to the Web Server instance (i-0b7563d0f89d3453a) using Remote Desktop, I open an elevated administrative command prompt and run the following command: gpupdate /force. I can check if the policy is applied by running the command: gpresult /r | findstr LAPS GPO, where LAPS GPO is the name of the GPO created in the second step.
  2. Back on my Management instance, I can then launch the LAPS interface from the Start menu and use it to retrieve the password (as shown in the following screenshot). Alternatively, I can run the Get-ADComputer Windows PowerShell cmdlet to retrieve the password.
Get-ADComputer [YourComputerName] -Properties ms-Mcs-AdmPwd | select name, ms-Mcs-AdmPwd

Screenshot of the LAPS UI, which you can use to retrieve the password

Summary

In this blog post, I demonstrated how you can deploy LAPS with an AWS Microsoft AD directory. I then showed how to install the LAPS binaries by using EC2 Run Command. Using the sample LDIF file I provided, I showed you how to extend the schema, which is a requirement because LAPS relies on new AD attributes to store the encrypted password and its expiration time. Finally, I showed how to complete the LAPS setup by configuring the necessary AD permissions and creating the GPO that starts the LAPS password change.

If you have comments about this post, submit them in the “Comments” section below. If you have questions about or issues implementing this solution, please start a new thread on the Directory Service forum.

– Dragos

Dish Network Sues ‘ZemTV’ and ‘TV Addons’ For Copyright Infringement

Post Syndicated from Ernesto original https://torrentfreak.com/dish-network-sues-zemtv-and-tv-addons-for-copyright-infringement-170605/

More and more people are starting to use Kodi-powered set-top boxes to stream video content to their TVs.

While Kodi itself is a neutral platform, third-party add-ons can turn it into the ultimate pirate machine, providing access to movies, TV-shows and IPTV channels.

These add-ons are direct competition for traditional broadcast providers, such as Dish Network in the United States, which filed a lawsuit in a Texas federal court late last week.

The complaint lists the add-on ZemTV as the prime target. The service in question allows users to watch a variety of Dish channels, without permission.

“The ZemTV service is retransmitting these channels over the Internet to end-users that download the ZemTV add-on for the Kodi media player, which is available for download at the websites www.tvaddons.ag and www.tvaddons.org,” Dish’s lawyers write.

The TVAddons platform, which hosts hundreds of unofficial Kodi add-ons including ZemTV, is also listed as a defendant. According to Dish, TVAddons plays an important role in the distribution of the infringing add-on.

The ZemTV operator, who is only known as “Shani” and “Shani_08,” used the TVAddons platform to share and promote its service while asking for donations, the complaint alleges.

“Website Operators have actual or constructive knowledge of this infringing activity and materially contribute to that activity by providing the forum where the ZemTV add-on can be downloaded and soliciting and accepting donations from ZemTV users,” Dish writes.

“But for the availability of the ZemTV add-on at www.tvaddons.ag or www.tvaddons.org, most if not all of Developer’s distribution and/or public performance would not occur,” the complaint adds.

Dish claims that it sent numerous takedown requests to Internet service providers associated with the ZemTV service, but the developer has continued to offer the add-on, circumventing any countermeasures.

With the lawsuit, the broadcast provider holds ZemTV accountable for direct copyright infringement, demanding $150,000 per infringement in damages. TVAddons is accused of contributory and vicarious copyright infringement and also faces statutory damages.

TorrentFreak spoke to a representative from TVAddons, who wasn’t aware of the lawsuit. Dish has not contacted them directly with any takedown requests, he says.

“This is the first we’ve heard of this lawsuit. No one ever sent us any type of takedown or DMCA notice or even tried to contact us prior, they could have easily done so through our contact page or site emails,” TVAddons informs us.

TVAddons says that the ZemTV add-on was already removed prior to the lawsuit due to a technical issue, and it won’t return.

“The Zem addon was actually removed from our addon library and community tools weeks ago due to a completely unrelated technical issue. I have already spoken to the developer, and he has since deleted the Zem addon entirely,” the TVAddons representative says.

Also, shortly after we started to inquire about the lawsuit, the ZemTV add-on appears to have shut down completely. According to Kodi Tips, developer “Shani” said it became too popular to maintain, but the legal threat likely played a role as well.

The lawsuit against ZemTV and TVAddons is the first of its kind in the United States. As such, it will be closely watched by other rightsholders, add-on developers, and platforms similar to TVAddons that distribute software.

The full complaint Dish Network filed is available here (pdf).

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

How NAGRA Fights Kodi and IPTV Piracy

Post Syndicated from Andy original https://torrentfreak.com/how-nagra-fights-kodi-and-iptv-piracy-170603/

Nagravision or NAGRA is one of the best known companies operating in the digital cable and satellite television content security space. Due to successes spanning several decades, the company has often proven unpopular with pirates.

In particular, Nagravision encryption systems have regularly been a hot topic for discussion on cable and satellite hacking forums, frustrating those looking to receive pay TV services without paying the high prices associated with them. However, the rise of the Internet is now presenting new challenges.

NAGRA still protects traditional cable and satellite pay TV services in 2017; Virgin Media in the UK is a long-standing customer, for example. But the rise of Internet streaming means that pirate content can now be delivered to the home with ease, completely bypassing the entire pay TV provider infrastructure. And, by extension, NAGRA’s encryption.

This means that NAGRA has been required to spread its wings.

As reported in April, NAGRA is establishing a lab to monitor and detect unauthorized consumption of content via set-top boxes, websites and other streaming platforms. That covers the now omnipresent Kodi phenomenon, alongside premium illicit IPTV services. TorrentFreak caught up with the company this week to find out more.

“NAGRA has an automated monitoring platform that scans all live channels and VOD assets available on Kodi,” NAGRA’s Ivan Schnider informs TF.

“The service we offer to our customers automatically finds illegal distribution of their content on Kodi and removes infringing streams.”

In the first instance, NAGRA sends standard takedown notices to hosting services to terminate illicit streams. The company says that while some companies are very cooperative, others are less so. When meeting resistance, NAGRA switches to more coercive methods, described here by Christopher Schouten, NAGRA Senior Director Product Marketing.

“Takedowns are generally sent to streaming platforms and hosting servers. When those don’t work, Advanced Takedowns allow us to use both technical and legal means to get results,” Schouten says.

“Numerous stories in recent days show how for instance popular Kodi plug-ins have been removed by their authors because of the mere threat of legal actions like this.”

At the center of operations is NAGRA’s Piracy Intelligence Portal, which offers customers a real-time view of worldwide online piracy trends, information on the infrastructure behind illegal services, as well as statistics and status of takedown requests.

“We measure takedown compliance very carefully using our Piracy Intelligence Portal, so we can usually predict the results we will get. We work on a daily basis to improve relationships and interfaces with those who are less compliant,” Schouten says.

The Piracy Intelligence Portal

While persuasion is probably the best solution, some hosts inevitably refuse to cooperate. However, NAGRA also offers the NexGuard system, which is able to determine the original source of the content.

“Using forensic watermarking to trace the source of the leak, we will be able to completely shut down the ‘leak’ at the source, independently and within minutes of detection,” Schouten says.

Whatever route is taken, NAGRA says that the aim is to take down streams as quickly as possible, something which hopefully undermines confidence in pirate services and encourages users to re-enter the legal market. Interestingly, the company also says it uses “technical means” to degrade pirate services to the point that consumers lose faith in them.

But while augmented Kodi setups and illicit IPTV are certainly considered a major threat in 2017, they are not the only problem faced by content companies.

While the Apple platform is quite tight, the open nature of Android means that there are a rising number of apps that can be sideloaded from the web. These allow pirate content to be consumed quickly and conveniently within a glossy interface.

Apps like Showbox, MovieHD and Terrarium TV have the movie and TV show sector wrapped up, while the popular Mobdro achieves the same with live TV, including premium sports. Schnider says NAGRA can handle apps like these and other emerging threats in a variety of ways.

“In addition to Kodi-related anti-piracy activities, NAGRA offers a service that automatically finds illegal distribution of content on Android applications, fully loaded STBs, M3U playlist and other platforms that provide plug-and-play solutions for the big TV screen; this service also includes the removal of infringing streams,” he explains.

M3U playlist piracy doesn’t get a lot of press. An M3U file is a text file that specifies locations where content (such as streams) can be found online.

In its basic ‘free’ form, it’s simply a case of finding an M3U file on an indexing site or blog and loading it into VLC. It’s not as flashy as any of the above apps, and unless one knows where to get the free M3Us quickly, many channels may already be offline. Premium M3U files are widely available, however, and tend to be pretty reliable.

But while attacking sources of infringing content is clearly a big part of NAGRA’s mission, the company also deploys softer strategies for dealing with pirates.

“Beyond disrupting pirate streams, raising awareness amongst users that these services are illegal and helping service providers deliver competing legitimate services, are also key areas in the fight against premium IPTV piracy where NAGRA can help,” Schnider says.

“Converting users of such services to legitimate paying subscribers represents a significant opportunity for content owners and distributors.”

For this to succeed, Schouten says there needs to be an understanding of the different motivators that lead an individual to commit piracy.

“Is it price? Is it availability? Is it functionality?” he asks.

Interestingly, he also reveals that lots of people are spending large sums of money on IPTV services they believe are legal but are not. Rather than the high prices putting them off, they actually add to their air of legitimacy.

“These consumers can relatively easily be converted into paying subscribers if they can be convinced that pay-TV services offer superior quality, reliability, and convenience because let’s face it, most IPTV services are still a little dodgy to use,” he says.

“Education is also important; done through working with service providers to inform consumers through social media platforms of the risks linked to the use of illegitimate streaming devices / IPTV devices, e.g. purchasing boxes that may no longer work after a short period of time.”

And so the battle over content continues.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

EU Piracy Filter Proposals Being Sabotaged Says MEP Julia Reda

Post Syndicated from Andy original https://torrentfreak.com/eu-piracy-filter-proposals-being-sabotaged-says-mep-julia-reda-170601/

After complaining about “rogue” sites and services for more than 15 years, the music business is now concentrating on the so-called “value gap”.

The theory is that platforms like YouTube are able to avoid paying expensive licensing fees for music by exploiting the safe harbor protections of the DMCA and similar legislation. Effectively, pirate music uploaded by site users becomes available to the public at no cost to the platform and due to safe harbor rules, there is no legal recourse for the labels.

To close this loophole, the EU is currently moving forward with reforms that could limit the protections currently enjoyed by platforms like YouTube. In short, sites that allow users to upload content will be forced to partner up with content providers to aggressively filter all user uploads for infringing content, thus limiting the number of infringing works eventually communicated to the public.

Even as they stand the proposals are being heavily protested (1,2,3) but according to Member of the European Parliament Julia Reda, a new threat has appeared on the horizon.

Ahead of a crucial June 8 vote on how to move forward, Reda says that some in the corridors of power are now “resorting to dirty tactics” to defend and extend the already “disastrous plans” by any means.

Specifically, Reda accuses MEP Pascal Arimont from the European People’s Party (EPP) of trying to sabotage the Parliamentary process, by going behind negotiators’ backs and pushing a new filtering proposal text that makes the “original bad proposal look tame in comparison.”

Reda says that in the face of other MEPs’ efforts to come up with a compromise text upon which all of them are agreed, Arimont has been encouraging some MEPs to rebel against their negotiators. He wants them to support his own super-aggressive “alternative compromise” text that shows disregard for the Charter of Fundamental Rights and principles of EU law.

Arimont’s text is certainly an interesting read and a document that could have been formulated by the record labels themselves. It tightens just about every aspect of the text proposed by the Commission while running all over the compromise text put together by Reda and other MEPs.

For example, where others are agreed on the phrase “Where information society
service providers store and provide access to the public to copyright protected works or other subject-matter uploaded by their users”, Arimont’s text removes the key word “store”.

This means that his filtering demands go beyond sites like YouTube that actually host content, to encompass those that merely carry links. It doesn’t take much imagination to see the potential for chaos there.

Also, where the Commission is happy with the proposed rules only affecting sites that store and provide access to “large amounts” of copyright protected works uploaded by users, Arimont wants the “store” part removed and “large” changed to “significant”.

“[Arimont] doesn’t want [filtering rules] to just apply to services hosting ‘large amounts’ of copyrighted content, as proposed by the Commission, but to any service facilitating the availability of such content, even if the service is not actually hosting anything at all,” Reda explains.

The text also ignores proposals by MEPs that anti-piracy measures to be taken by platforms should be proportionate to their profit and size. That being said, Arimont does accept that start-ups would probably face “insurmountable financial obstacles” if required to deploy filtering technologies, so he proposes they should be exempt.

While that sounds reasonable, any business that’s over five years old would need to comply and Reda warns that the threshold could be set particularly low.

“So if you’ve been self-employed for more than 5 years, rules the Commission wrote with the likes of YouTube and Facebook in mind would suddenly also apply to your personal website,” she warns.

But Arimont’s proposal goes further still and has the potential to have privacy advocates up in arms.

In order to check that all user uploaded content is non-infringing, platforms would necessarily be required to check every single piece of data uploaded by users. This raises considerable privacy concerns and potential conflicts with EU law, for instance with Article 15 of the E-Commerce Directive, which prohibits general monitoring obligations for service providers.

Indeed, during the Netlog filtering case that went before the EU Court of Justice (CJEU) in 2012, the Court held that requiring an online platform to install broad piracy filters is incompatible with EU law.

Nevertheless, Arimont sees bridging the “value gap” as somehow different.

“The use of technical measures is essential for the functioning of online licensing and rights management purposes. Such technical measures therefore do not require the identity of uploaders and hence do not pose any risk for privacy of individual end users,” his proposal reads.

“Furthermore, those technical measures involve a highly targeted technical cooperation of rightholders and information society service providers based on the data provided by rightholders, and therefore do not lead to general obligation to monitor and find facts about the content.”

But what should really raise alarm bells for user-uploaded content platforms is how Arimont proposes to strip them of their safe harbor protections, if they optimize the presentation of that content to users. That, as Reda points out, could be something as benign as listing content in alphabetical order.

Julia Reda’s article has some information at the end for those who want to protest Arimont’s proposals (pdf).

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

AWS Hot Startups – May 2017

Post Syndicated from Tina Barr original https://aws.amazon.com/blogs/aws/aws-hot-startups-may-2017/

April showers bring May startups! This month we have three hot startups for you to check out. Keep reading to find out what they’re up to, and how they’re using AWS to do it.

Today’s post features the following startups:

  • Lobster – an AI-powered platform connecting creative social media users to professionals.
  • Visii – helping consumers find the perfect product using visual search.
  • Tiqets – a curated marketplace for culture and entertainment.

Lobster (London, England)

Every day, social media users generate billions of authentic images and videos to rival typical stock photography. Powered by Artificial Intelligence, Lobster enables brands, agencies, and the press to license visual content directly from social media users so they can find that piece of content that perfectly fits their brand or story. Lobster does the work of sorting through major social networks (Instagram, Flickr, Facebook, Vk, YouTube, and Vimeo) and cloud storage providers (Dropbox, Google Photos, and Verizon) to find media, saving brands and agencies time and energy. Using filters like gender, color, age, and geolocation can help customers find the unique content they’re looking for, while Lobster’s AI and visual recognition finds images instantly. Lobster also runs photo challenges to help customers discover the perfect image to fit their needs.

Lobster is an excellent platform for creative people to get their work discovered while also protecting their content. Users are treated as copyright holders and earn 75% of the final price of every sale. The platform is easy to use: new users simply sign in with an existing social media or cloud account and can start showcasing their artistic talent right away. Lobster allows users to connect to any number of photo storage sources so they’re able to choose which items to share and which to keep private. Once users have selected their favorite photos and videos to share, they can sit back and watch as their work is picked to become the signature for a new campaign or featured on a cool website – and start earning money for their work.

Lobster is using a variety of AWS services to keep everything running smoothly. The company uses Amazon S3 to store photography that was previously ordered by customers. When a customer purchases content, the respective piece of content must be available at any given moment, independent from the original source. Lobster is also using Amazon EC2 for its application servers and Elastic Load Balancing to monitor the state of each server.

To learn more about Lobster, check them out here!

Visii (London, England)

In today’s vast web, a growing number of products are being sold online and searching for something specific can be difficult. Visii was created to cater to businesses and help them extract value from an asset they already have – their images. Their SaaS platform allows clients to leverage an intelligent visual search on their websites and apps to help consumers find the perfect product for them. With Visii, consumers can choose an image and immediately discover more based on their tastes and preferences. Whether it’s clothing, artwork, or home decor, Visii will make recommendations to get consumers to search visually and subsequently help businesses increase their conversion rates.

There are multiple ways for businesses to integrate Visii on their website or app. Many of Visii’s clients choose to build against their API, but Visii also work closely with many clients to figure out the most effective way to do this for each unique case. This has led Visii to help build innovative user interfaces and figure out the best integration points to get consumers to search visually. Businesses can also integrate Visii on their website with a widget – they just need to provide a list of links to their products and Visii does the rest.

Visii runs their entire infrastructure on AWS. Their APIs and pipeline all sit in auto-scaling groups, with ELBs in front of them, sending things across into Amazon Simple Queue Service and Amazon Aurora. Recently, Visii moved from Amazon RDS to Aurora and noted that the process was incredibly quick and easy. Because they make heavy use of machine learning, it is crucial that their pipeline only runs when required and that they maximize the efficiency of their uptime.

To see how companies are using Visii, check out Style Picker and Saatchi Art.

Tiqets (Amsterdam, Netherlands)

Tiqets is making the ticket-buying experience faster and easier for travelers around the world.  Founded in 2013, Tiqets is one of the leading curated marketplaces for admission tickets to museums, zoos, and attractions. Their mission is to help travelers get the most out of their trips by helping them find and experience a city’s culture and entertainment. Tiqets partners directly with vendors to adapt to a customer’s specific needs, and is now active in over 30 cities in the US, Europe, and the Middle East.

With Tiqets, travelers can book tickets either ahead of time or at their destination for a wide range of attractions. The Tiqets app provides real-time availability and delivers tickets straight to customer’s phones via email, direct download, or in the app. Customers save time skipping long lines (a perk of the app!), save trees (don’t need to physically print tickets), and most importantly, they can make the most out of their leisure time. For each attraction featured on Tiqets, there is a lot of helpful information including best modes of transportation, hours, commonly asked questions, and reviews from other customers.

The Tiqets platform consists of the consumer-facing website, the internal and external-facing APIs, and the partner self-service portals. For the app hosting and infrastructure, Tiqets uses AWS services such as Elastic Load Balancing, Amazon EC2, Amazon RDS, Amazon CloudFront, Amazon Route 53, and Amazon ElastiCache. Through the infrastructure orchestration of their AWS configuration, they can easily set up separate development or test environments while staying close to the production environment as well.

Tiqets is hiring! Be sure to check out their jobs page if you are interested in joining the Tiqets team.

Thanks for reading and don’t forget to check out April’s Hot Startups if you missed it.

-Tina Barr

 

 

Huge Coalition Protests EU Mandatory Piracy Filter Proposals

Post Syndicated from Andy original https://torrentfreak.com/huge-coalition-protests-eu-mandatory-piracy-filter-proposals-170530/

Last September, EU Commission President Jean-Claude Juncker announced plans to modernize copyright law in Europe.

The proposals (pdf) are part of the Digital Single Market reforms, which have been under development for the past several years.

The proposals cover a broad range of copyright-related issues, but one stands out as being particularly controversial. Article 13 requires certain online service providers to become deeply involved in the detection and policing of allegedly infringing copyright works, uploaded to their platforms by users.

Although its effects will likely be more broad, the proposal is targeted at the so-called “value gap” (1,2,3), i.e the notion that platforms like YouTube are able to avoid paying expensive licensing fees (for music in particular) by exploiting the safe harbor protections of the DMCA and similar legislation.

To close this loophole using Article 13, services that provide access to “large amounts” of user-uploaded content would be required to cooperate with rightsholders to prevent infringing works being communicated to the public.

This means that platforms like YouTube would be forced to take measures to ensure that their deals with content providers to distribute official content are protected by aggressive anti-piracy mechanisms.

The legislation would see platforms forced to deploy content-recognition, filtering and blocking mechanisms, to ensure that only non-infringing content is uploaded in the first place, thus limiting the chances that unauthorized copyrighted content will be made available to end users.

Supporters argue that the resulting decrease in availability of infringing content will effectively close the “value gap” but critics see the measures as disproportionate, likely to result in censorship (no provision for fair use), and a restriction of fundamental freedoms. Indeed, there are already warnings that such a system would severely “restrict the way Europeans create, share, and communicate online.”

The proposals have predictably received widespread support from entertainment industry companies across the EU and the United States, but there are now clear signs that the battle lines are being drawn.

On one side are the major recording labels, movie studios, and other producers. On the other, companies and platforms that will suddenly become more liable for infringing content, accompanied by citizens and scholars who feel that freedoms will be restricted.

The latest sign of the scale of opposition to Article 13 manifests itself in an open letter to the European Parliament. Under the Copyright for Creativity (C4C) banner and signed by the EFF, Creative Commons, Wikimedia, Mozilla, EDRi, Open Rights Group plus sixty other organizations, the letter warns that the proposals will cause more problems than they solve.

“The European Commission’s proposal on copyright in the Digital Single Market failed to meet the expectations of European citizens and businesses. Instead of supporting Europeans in the digital economy, it is backward looking,” the groups say.

“We need European lawmakers to oppose the most damaging aspects of the proposal, but also to embrace a more ambitious agenda for positive reform.”

In addition to opposing Article 11 (the proposed Press Publishers’ Right), the groups ask the EU Parliament not to impose private censorship on EU citizens via Article 13.

“The provision on the so-called ‘value gap’ is designed to provoke such legal uncertainty that online services will have no other option than to monitor, filter and block EU citizens’ communications if they want to have any chance of staying in business,” the groups write.

“The Commission’s proposal misrepresents some European Court rulings and seeks to impose contradictory obligations on Member States. This is simply bad regulation.”

Calling for the wholesale removal of Article 13 from the copyright negotiations, the groups argue that the reforms should be handled in the appropriate contexts.

“We strenuously oppose such ill thought through experimentation with intermediary liability, which will hinder innovation and competition and will reduce the opportunities available to all European businesses and citizens,” they add.

C4C concludes by calling on lawmakers to oppose Article 13 while seeking avenues for positive reform.

The full letter can be found here (pdf)

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

To Torrent or To Stream? That is The Big Piracy Question

Post Syndicated from Andy original https://torrentfreak.com/to-torrent-or-to-stream-that-is-the-big-piracy-question-170529/

As recently as perhaps six years ago, there was no question over where the majority of Internet video pirates would be getting their online fix. Just as they had done for at least as many years before, BitTorrent was the go-to protocol.

While still massively popular today to the tune of scores of millions of daily users, torrent consumption has been tempered in recent years by the rise of streaming platforms. These sites, with their glossy indexes and YouTube-like interfaces, grant easy access to a wide range of movies and TV shows, almost as quickly as their torrent counterparts.

So why, when these streaming services are so easy to use, would anyone bother with relatively cumbersome torrent downloads? The answer isn’t immediately obvious but for those with intimate experience of both, it’s a pretty serious question.

First of all, we have the important question of content ‘ownership’.

While people have easy access to the latest movies on streamingmovies123 or whatever .com, users never ‘own’ those streams. They are absolutely transient and once the stop button is pressed, that movie or TV show instance is gone forever. The user downloads the file of course, but it is almost immediately dumped into the ether.

For the same download bandwidth expenditure, the user can visit a torrent site and obtain exactly the same content. This time, however, there are two key differences. One, they help to distribute that content among other downloaders and two, they get to keep that content for as long as they choose.

Storing content locally is important to many pirates. Not only can that content be played on any device of the users’ choosing, it can also be played offline. Sure, it takes up some drive space, but it doesn’t require streamingmovies123 to stay online to be enjoyed. It can be played again, potentially forever, and certainly long after the streaming site has disappeared, which they often do.

But while maintaining control of content rarely has a downside for the consumer, the issue of whether distribution (uploading) via BitTorrent is ‘good’ depends on perspective.

Users of streaming sites will correctly argue that with no uploading, they are much safer than their torrenting counterparts. Torrent users, on the other hand, note that their participation in uploading helps to provide content to others. Torrent users are effectively a plus to the piracy ecosystem, while streamers (if we refer to them in torrent terms) are merely leechers.

There’s a whole generation of streaming consumers coming through today who literally have no idea about the concept of sharing. They do not understand where the content comes from, nor do they care. This lack of ‘pirate education’ could eventually present a negative for content availability.

While we’re on that topic, there’s the important question of how and why pirated content travels through the online ecosystem.

There are long-established routes for content from so-called ‘top sites’ to be shifted quickly to torrent sites. Furthermore, torrent sites provide platforms for non-scene P2P releasers to distribute their offerings to the public. In this respect, torrent sites contribute much more to the overall piracy ecosystem than most streaming sites.

Also, there’s the not inconsiderable issue of where streaming sites obtain their content. Of course, many people involved in that area of piracy will have either direct or indirect top-site access, but many simply choose to grab their content from either public or private torrents like the average user might. It’s not hard to see who relies on who here.

This brings us to how each kind of piracy is perceived by Hollywood interests. It doesn’t take Einstein to reveal that both torrents and streaming are the enemy, but since streaming platforms are closest to legitimate offerings such as Netflix and Amazon, the threat they pose is often portrayed as being the greatest.

Indeed, the rise of modified Kodi setups (and the aggressive response to them) seems to support that, with piracy shifting from the relatively geeky torrent environment to the point-and-click living room domain, occupied by the general public.

So the question of what is best – torrents or streaming – is largely down to consumer preference. However, for those with an interest in the piracy ecosystem, it’s a question of whether streaming can improve or even survive without torrents, and whether exclusively supporting the former is a potential road to nowhere.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

Build a Serverless Architecture to Analyze Amazon CloudFront Access Logs Using AWS Lambda, Amazon Athena, and Amazon Kinesis Analytics

Post Syndicated from Rajeev Srinivasan original https://aws.amazon.com/blogs/big-data/build-a-serverless-architecture-to-analyze-amazon-cloudfront-access-logs-using-aws-lambda-amazon-athena-and-amazon-kinesis-analytics/

Nowadays, it’s common for a web server to be fronted by a global content delivery service, like Amazon CloudFront. This type of front end accelerates delivery of websites, APIs, media content, and other web assets to provide a better experience to users across the globe.

The insights gained by analysis of Amazon CloudFront access logs helps improve website availability through bot detection and mitigation, optimizing web content based on the devices and browser used to view your webpages, reducing perceived latency by caching of popular object closer to its viewer, and so on. This results in a significant improvement in the overall perceived experience for the user.

This blog post provides a way to build a serverless architecture to generate some of these insights. To do so, we analyze Amazon CloudFront access logs both at rest and in transit through the stream. This serverless architecture uses Amazon Athena to analyze large volumes of CloudFront access logs (on the scale of terabytes per day), and Amazon Kinesis Analytics for streaming analysis.

The analytic queries in this blog post focus on three common use cases:

  1. Detection of common bots using the user agent string
  2. Calculation of current bandwidth usage per Amazon CloudFront distribution per edge location
  3. Determination of the current top 50 viewers

However, you can easily extend the architecture described to power dashboards for monitoring, reporting, and trigger alarms based on deeper insights gained by processing and analyzing the logs. Some examples are dashboards for cache performance, usage and viewer patterns, and so on.

Following we show a diagram of this architecture.

Prerequisites

Before you set up this architecture, install the AWS Command Line Interface (AWS CLI) tool on your local machine, if you don’t have it already.

Setup summary

The following steps are involved in setting up the serverless architecture on the AWS platform:

  1. Create an Amazon S3 bucket for your Amazon CloudFront access logs to be delivered to and stored in.
  2. Create a second Amazon S3 bucket to receive processed logs and store the partitioned data for interactive analysis.
  3. Create an Amazon Kinesis Firehose delivery stream to batch, compress, and deliver the preprocessed logs for analysis.
  4. Create an AWS Lambda function to preprocess the logs for analysis.
  5. Configure Amazon S3 event notification on the CloudFront access logs bucket, which contains the raw logs, to trigger the Lambda preprocessing function.
  6. Create an Amazon DynamoDB table to look up partition details, such as partition specification and partition location.
  7. Create an Amazon Athena table for interactive analysis.
  8. Create a second AWS Lambda function to add new partitions to the Athena table based on the log delivered to the processed logs bucket.
  9. Configure Amazon S3 event notification on the processed logs bucket to trigger the Lambda partitioning function.
  10. Configure Amazon Kinesis Analytics application for analysis of the logs directly from the stream.

ETL and preprocessing

In this section, we parse the CloudFront access logs as they are delivered, which occurs multiple times in an hour. We filter out commented records and use the user agent string to decipher the browser name, the name of the operating system, and whether the request has been made by a bot. For more details on how to decipher the preceding information based on the user agent string, see user-agents 1.1.0 in the Python documentation.

We use the Lambda preprocessing function to perform these tasks on individual rows of the access log. On successful completion, the rows are pushed to an Amazon Kinesis Firehose delivery stream to be persistently stored in an Amazon S3 bucket, the processed logs bucket.

To create a Firehose delivery stream with a new or existing S3 bucket as the destination, follow the steps described in Create a Firehose Delivery Stream to Amazon S3 in the S3 documentation. Keep most of the default settings, but select an AWS Identity and Access Management (IAM) role that has write access to your S3 bucket and specify GZIP compression. Name the delivery stream CloudFrontLogsToS3.

Another pre-requisite for this setup is to create an IAM role that provides the necessary permissions our AWS Lambda function to get the data from S3, process it, and deliver it to the CloudFrontLogsToS3 delivery stream.

Let’s use the AWS CLI to create the IAM role using the following the steps:

  1. Create the IAM policy (lambda-exec-policy) for the Lambda execution role to use.
  2. Create the Lambda execution role (lambda-cflogs-exec-role) and assign the service to use this role.
  3. Attach the policy created in step 1 to the Lambda execution role.

To download the policy document to your local machine, type the following command.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/preprocessiong-lambda/lambda-exec-policy.json  <path_on_your_local_machine>

To download the assume policy document to your local machine, type the following command.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/preprocessiong-lambda/assume-lambda-policy.json  <path_on_your_local_machine>

Following is the lambda-exec-policy.json file, which is the IAM policy used by the Lambda execution role.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "CloudWatchAccess",
            "Effect": "Allow",
            "Action": [
                "logs:CreateLogGroup",
                "logs:CreateLogStream",
                "logs:PutLogEvents"
            ],
            "Resource": "arn:aws:logs:*:*:*"
        },
        {
            "Sid": "S3Access",
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:PutObject"
            ],
            "Resource": [
                "arn:aws:s3:::*"
            ]
        },
        {
            "Sid": "FirehoseAccess",
            "Effect": "Allow",
            "Action": [
                "firehose:ListDeliveryStreams",
                "firehose:PutRecord",
                "firehose:PutRecordBatch"
            ],
            "Resource": [
                "arn:aws:firehose:*:*:deliverystream/CloudFrontLogsToS3"
            ]
        }
    ]
}

To create the IAM policy used by Lambda execution role, type the following command.

aws iam create-policy --policy-name lambda-exec-policy --policy-document file://<path>/lambda-exec-policy.json

To create the AWS Lambda execution role and assign the service to use this role, type the following command.

aws iam create-role --role-name lambda-cflogs-exec-role --assume-role-policy-document file://<path>/assume-lambda-policy.json

Following is the assume-lambda-policy.json file, to grant Lambda permission to assume a role.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "lambda.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

To attach the policy (lambda-exec-policy) created to the AWS Lambda execution role (lambda-cflogs-exec-role), type the following command.

aws iam attach-role-policy --role-name lambda-cflogs-exec-role --policy-arn arn:aws:iam::<your-account-id>:policy/lambda-exec-policy

Now that we have created the CloudFrontLogsToS3 Firehose delivery stream and the lambda-cflogs-exec-role IAM role for Lambda, the next step is to create a Lambda preprocessing function.

This Lambda preprocessing function parses the CloudFront access logs delivered into the S3 bucket and performs a few transformation and mapping operations on the data. The Lambda function adds descriptive information, such as the browser and the operating system that were used to make this request based on the user agent string found in the logs. The Lambda function also adds information about the web distribution to support scenarios where CloudFront access logs are delivered to a centralized S3 bucket from multiple distributions. With the solution in this blog post, you can get insights across distributions and their edge locations.

Use the Lambda Management Console to create a new Lambda function with a Python 2.7 runtime and the s3-get-object-python blueprint. Open the console, and on the Configure triggers page, choose the name of the S3 bucket where the CloudFront access logs are delivered. Choose Put for Event type. For Prefix, type the name of the prefix, if any, for the folder where CloudFront access logs are delivered, for example cloudfront-logs/. To invoke Lambda to retrieve the logs from the S3 bucket as they are delivered, select Enable trigger.

Choose Next and provide a function name to identify this Lambda preprocessing function.

For Code entry type, choose Upload a file from Amazon S3. For S3 link URL, type https.amazonaws.com//preprocessing-lambda/pre-data.zip. In the section, also create an environment variable with the key KINESIS_FIREHOSE_STREAM and a value with the name of the Firehose delivery stream as CloudFrontLogsToS3.

Choose lambda-cflogs-exec-role as the IAM role for the Lambda function, and type prep-data.lambda_handler for the value for Handler.

Choose Next, and then choose Create Lambda.

Table creation in Amazon Athena

In this step, we will build the Athena table. Use the Athena console in the same region and create the table using the query editor.

CREATE EXTERNAL TABLE IF NOT EXISTS cf_logs (
  logdate date,
  logtime string,
  location string,
  bytes bigint,
  requestip string,
  method string,
  host string,
  uri string,
  status bigint,
  referrer string,
  useragent string,
  uriquery string,
  cookie string,
  resulttype string,
  requestid string,
  header string,
  csprotocol string,
  csbytes string,
  timetaken bigint,
  forwardedfor string,
  sslprotocol string,
  sslcipher string,
  responseresulttype string,
  protocolversion string,
  browserfamily string,
  osfamily string,
  isbot string,
  filename string,
  distribution string
)
PARTITIONED BY(year string, month string, day string, hour string)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t'
LOCATION 's3://<pre-processing-log-bucket>/prefix/';

Creation of the Athena partition

A popular website with millions of requests each day routed using Amazon CloudFront can generate a large volume of logs, on the order of a few terabytes a day. We strongly recommend that you partition your data to effectively restrict the amount of data scanned by each query. Partitioning significantly improves query performance and substantially reduces cost. The Lambda partitioning function adds the partition information to the Athena table for the data delivered to the preprocessed logs bucket.

Before delivering the preprocessed Amazon CloudFront logs file into the preprocessed logs bucket, Amazon Kinesis Firehose adds a UTC time prefix in the format YYYY/MM/DD/HH. This approach supports multilevel partitioning of the data by year, month, date, and hour. You can invoke the Lambda partitioning function every time a new processed Amazon CloudFront log is delivered to the preprocessed logs bucket. To do so, configure the Lambda partitioning function to be triggered by an S3 Put event.

For a website with millions of requests, a large number of preprocessed logs can be delivered multiple times in an hour—for example, at the interval of one each second. To avoid querying the Athena table for partition information every time a preprocessed log file is delivered, you can create an Amazon DynamoDB table for fast lookup.

Based on the year, month, data and hour in the prefix of the delivered log, the Lambda partitioning function checks if the partition specification exists in the Amazon DynamoDB table. If it doesn’t, it’s added to the table using an atomic operation, and then the Athena table is updated.

Type the following command to create the Amazon DynamoDB table.

aws dynamodb create-table --table-name athenapartitiondetails \
--attribute-definitions AttributeName=PartitionSpec,AttributeType=S \
--key-schema AttributeName=PartitionSpec,KeyType=HASH \
--provisioned-throughput ReadCapacityUnits=100,WriteCapacityUnits=100

Here the following is true:

  • PartitionSpec is the hash key and is a representation of the partition signature—for example, year=”2017”; month=”05”; day=”15”; hour=”10”.
  • Depending on the rate at which the processed log files are delivered to the processed log bucket, you might have to increase the ReadCapacityUnits and WriteCapacityUnits values, if these are throttled.

The other attributes besides PartitionSpec are the following:

  • PartitionPath – The S3 path associated with the partition.
  • PartitionType – The type of partition used (Hour, Month, Date, Year, or ALL). In this case, ALL is used.

Next step is to create the IAM role to provide permissions for the Lambda partitioning function. You require permissions to do the following:

  1. Look up and write partition information to DynamoDB.
  2. Alter the Athena table with new partition information.
  3. Perform Amazon CloudWatch logs operations.
  4. Perform Amazon S3 operations.

To download the policy document to your local machine, type following command.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/partitioning-lambda/lambda-partition-function-execution-policy.json  <path_on_your_local_machine>

To download the assume policy document to your local machine, type the following command.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/partitioning-lambda/assume-lambda-policy.json <path_on_your_local_machine>

To create the Lambda execution role and assign the service to use this role, type the following command.

aws iam create-role --role-name lambda-cflogs-exec-role --assume-role-policy-document file://<path>/assume-lambda-policy.json

Let’s use the AWS CLI to create the IAM role using the following three steps:

  1. Create the IAM policy(lambda-partition-exec-policy) used by the Lambda execution role.
  2. Create the Lambda execution role (lambda-partition-execution-role)and assign the service to use this role.
  3. Attach the policy created in step 1 to the Lambda execution role.

To create the IAM policy used by Lambda execution role, type the following command.

aws iam create-policy --policy-name lambda-partition-exec-policy --policy-document file://<path>/lambda-partition-function-execution-policy.json

To create the Lambda execution role and assign the service to use this role, type the following command.

aws iam create-role --role-name lambda-partition-execution-role --assume-role-policy-document file://<path>/assume-lambda-policy.json

To attach the policy (lambda-partition-exec-policy) created to the AWS Lambda execution role (lambda-partition-execution-role), type the following command.

aws iam attach-role-policy --role-name lambda-partition-execution-role --policy-arn arn:aws:iam::<your-account-id>:policy/lambda-partition-exec-policy

Following is the lambda-partition-function-execution-policy.json file, which is the IAM policy used by the Lambda execution role.

{
    "Version": "2012-10-17",
    "Statement": [
      	{
            	"Sid": "DDBTableAccess",
            	"Effect": "Allow",
            	"Action": "dynamodb:PutItem"
            	"Resource": "arn:aws:dynamodb*:*:table/athenapartitiondetails"
        	},
        	{
            	"Sid": "S3Access",
            	"Effect": "Allow",
            	"Action": [
                		"s3:GetBucketLocation",
                		"s3:GetObject",
                		"s3:ListBucket",
                		"s3:ListBucketMultipartUploads",
                		"s3:ListMultipartUploadParts",
                		"s3:AbortMultipartUpload",
                		"s3:PutObject"
            	],
          		"Resource":"arn:aws:s3:::*"
		},
	              {
		      "Sid": "AthenaAccess",
      		"Effect": "Allow",
      		"Action": [ "athena:*" ],
      		"Resource": [ "*" ]
	      },
        	{
            	"Sid": "CloudWatchLogsAccess",
            	"Effect": "Allow",
            	"Action": [
                		"logs:CreateLogGroup",
                		"logs:CreateLogStream",
             	   	"logs:PutLogEvents"
            	],
            	"Resource": "arn:aws:logs:*:*:*"
        	}
    ]
}

Download the .jar file containing the Java deployment package to your local machine.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/partitioning-lambda/aws-lambda-athena-1.0.0.jar <path_on_your_local_machine>

From the AWS Management Console, create a new Lambda function with Java8 as the runtime. Select the Blank Function blueprint.

On the Configure triggers page, choose the name of the S3 bucket where the preprocessed logs are delivered. Choose Put for the Event Type. For Prefix, type the name of the prefix folder, if any, where preprocessed logs are delivered by Firehose—for example, out/. For Suffix, type the name of the compression format that the Firehose stream (CloudFrontLogToS3) delivers the preprocessed logs —for example, gz. To invoke Lambda to retrieve the logs from the S3 bucket as they are delivered, select Enable Trigger.

Choose Next and provide a function name to identify this Lambda partitioning function.

Choose Java8 for Runtime for the AWS Lambda function. Choose Upload a .ZIP or .JAR file for the Code entry type, and choose Upload to upload the downloaded aws-lambda-athena-1.0.0.jar file.

Next, create the following environment variables for the Lambda function:

  • TABLE_NAME – The name of the Athena table (for example, cf_logs).
  • PARTITION_TYPE – The partition to be created based on the Athena table for the logs delivered to the sub folders in S3 bucket based on Year, Month, Date, Hour, or Set this to ALL to use Year, Month, Date, and Hour.
  • DDB_TABLE_NAME – The name of the DynamoDB table holding partition information (for example, athenapartitiondetails).
  • ATHENA_REGION – The current AWS Region for the Athena table to construct the JDBC connection string.
  • S3_STAGING_DIR – The Amazon S3 location where your query output is written. The JDBC driver asks Athena to read the results and provide rows of data back to the user (for example, s3://<bucketname>/<folder>/).

To configure the function handler and IAM, for Handler copy and paste the name of the handler: com.amazonaws.services.lambda.CreateAthenaPartitionsBasedOnS3EventWithDDB::handleRequest. Choose the existing IAM role, lambda-partition-execution-role.

Choose Next and then Create Lambda.

Interactive analysis using Amazon Athena

In this section, we analyze the historical data that’s been collected since we added the partitions to the Amazon Athena table for data delivered to the preprocessing logs bucket.

Scenario 1 is robot traffic by edge location.

SELECT COUNT(*) AS ct, requestip, location FROM cf_logs
WHERE isbot='True'
GROUP BY requestip, location
ORDER BY ct DESC;

Scenario 2 is total bytes transferred per distribution for each edge location for your website.

SELECT distribution, location, SUM(bytes) as totalBytes
FROM cf_logs
GROUP BY location, distribution;

Scenario 3 is the top 50 viewers of your website.

SELECT requestip, COUNT(*) AS ct  FROM cf_logs
GROUP BY requestip
ORDER BY ct DESC;

Streaming analysis using Amazon Kinesis Analytics

In this section, you deploy a stream processing application using Amazon Kinesis Analytics to analyze the preprocessed Amazon CloudFront log streams. This application analyzes directly from the Amazon Kinesis Stream as it is delivered to the preprocessing logs bucket. The stream queries in section are focused on gaining the following insights:

  • The IP address of the bot, identified by its Amazon CloudFront edge location, that is currently sending requests to your website. The query also includes the total bytes transferred as part of the response.
  • The total bytes served per distribution per population for your website.
  • The top 10 viewers of your website.

To download the firehose-access-policy.json file, type the following.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/kinesisanalytics/firehose-access-policy.json  <path_on_your_local_machine>

To download the kinesisanalytics-policy.json file, type the following.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/kinesisanalytics/assume-kinesisanalytics-policy.json <path_on_your_local_machine>

Before we create the Amazon Kinesis Analytics application, we need to create the IAM role to provide permission for the analytics application to access Amazon Kinesis Firehose stream.

Let’s use the AWS CLI to create the IAM role using the following three steps:

  1. Create the IAM policy(firehose-access-policy) for the Lambda execution role to use.
  2. Create the Lambda execution role (ka-execution-role) and assign the service to use this role.
  3. Attach the policy created in step 1 to the Lambda execution role.

Following is the firehose-access-policy.json file, which is the IAM policy used by Kinesis Analytics to read Firehose delivery stream.

{
    "Version": "2012-10-17",
    "Statement": [
      	{
    	"Sid": "AmazonFirehoseAccess",
    	"Effect": "Allow",
    	"Action": [
       	"firehose:DescribeDeliveryStream",
        	"firehose:Get*"
    	],
    	"Resource": [
              "arn:aws:firehose:*:*:deliverystream/CloudFrontLogsToS3”
       ]
     }
}

Following is the assume-kinesisanalytics-policy.json file, to grant Amazon Kinesis Analytics permissions to assume a role.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "kinesisanalytics.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

To create the IAM policy used by Analytics access role, type the following command.

aws iam create-policy --policy-name firehose-access-policy --policy-document file://<path>/firehose-access-policy.json

To create the Analytics execution role and assign the service to use this role, type the following command.

aws iam attach-role-policy --role-name ka-execution-role --policy-arn arn:aws:iam::<your-account-id>:policy/firehose-access-policy

To attach the policy (irehose-access-policy) created to the Analytics execution role (ka-execution-role), type the following command.

aws iam attach-role-policy --role-name ka-execution-role --policy-arn arn:aws:iam::<your-account-id>:policy/firehose-access-policy

To deploy the Analytics application, first download the configuration file and then modify ResourceARN and RoleARN for the Amazon Kinesis Firehose input configuration.

"KinesisFirehoseInput": { 
    "ResourceARN": "arn:aws:firehose:<region>:<account-id>:deliverystream/CloudFrontLogsToS3", 
    "RoleARN": "arn:aws:iam:<account-id>:role/ka-execution-role"
}

To download the Analytics application configuration file, type the following command.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis//kinesisanalytics/kinesis-analytics-app-configuration.json <path_on_your_local_machine>

To deploy the application, type the following command.

aws kinesisanalytics create-application --application-name "cf-log-analysis" --cli-input-json file://<path>/kinesis-analytics-app-configuration.json

To start the application, type the following command.

aws kinesisanalytics start-application --application-name "cf-log-analysis" --input-configuration Id="1.1",InputStartingPositionConfiguration={InputStartingPosition="NOW"}

SQL queries using Amazon Kinesis Analytics

Scenario 1 is a query for detecting bots for sending request to your website detection for your website.

-- Create output stream, which can be used to send to a destination
CREATE OR REPLACE STREAM "BOT_DETECTION" (requesttime TIME, destribution VARCHAR(16), requestip VARCHAR(64), edgelocation VARCHAR(64), totalBytes BIGINT);
-- Create pump to insert into output 
CREATE OR REPLACE PUMP "BOT_DETECTION_PUMP" AS INSERT INTO "BOT_DETECTION"
--
SELECT STREAM 
    STEP("CF_LOG_STREAM_001"."request_time" BY INTERVAL '1' SECOND) as requesttime,
    "distribution_name" as distribution,
    "request_ip" as requestip, 
    "edge_location" as edgelocation, 
    SUM("bytes") as totalBytes
FROM "CF_LOG_STREAM_001"
WHERE "is_bot" = true
GROUP BY "request_ip", "edge_location", "distribution_name",
STEP("CF_LOG_STREAM_001"."request_time" BY INTERVAL '1' SECOND),
STEP("CF_LOG_STREAM_001".ROWTIME BY INTERVAL '1' SECOND);

Scenario 2 is a query for total bytes transferred per distribution for each edge location for your website.

-- Create output stream, which can be used to send to a destination
CREATE OR REPLACE STREAM "BYTES_TRANSFFERED" (requesttime TIME, destribution VARCHAR(16), edgelocation VARCHAR(64), totalBytes BIGINT);
-- Create pump to insert into output 
CREATE OR REPLACE PUMP "BYTES_TRANSFFERED_PUMP" AS INSERT INTO "BYTES_TRANSFFERED"
-- Bytes Transffered per second per web destribution by edge location
SELECT STREAM 
    STEP("CF_LOG_STREAM_001"."request_time" BY INTERVAL '1' SECOND) as requesttime,
    "distribution_name" as distribution,
    "edge_location" as edgelocation, 
    SUM("bytes") as totalBytes
FROM "CF_LOG_STREAM_001"
GROUP BY "distribution_name", "edge_location", "request_date",
STEP("CF_LOG_STREAM_001"."request_time" BY INTERVAL '1' SECOND),
STEP("CF_LOG_STREAM_001".ROWTIME BY INTERVAL '1' SECOND);

Scenario 3 is a query for the top 50 viewers for your website.

-- Create output stream, which can be used to send to a destination
CREATE OR REPLACE STREAM "TOP_TALKERS" (requestip VARCHAR(64), requestcount DOUBLE);
-- Create pump to insert into output 
CREATE OR REPLACE PUMP "TOP_TALKERS_PUMP" AS INSERT INTO "TOP_TALKERS"
-- Top Ten Talker
SELECT STREAM ITEM as requestip, ITEM_COUNT as requestcount FROM TABLE(TOP_K_ITEMS_TUMBLING(
  CURSOR(SELECT STREAM * FROM "CF_LOG_STREAM_001"),
  'request_ip', -- name of column in single quotes
  50, -- number of top items
  60 -- tumbling window size in seconds
  )
);

Conclusion

Following the steps in this blog post, you just built an end-to-end serverless architecture to analyze Amazon CloudFront access logs. You analyzed these both in interactive and streaming mode, using Amazon Athena and Amazon Kinesis Analytics respectively.

By creating a partition in Athena for the logs delivered to a centralized bucket, this architecture is optimized for performance and cost when analyzing large volumes of logs for popular websites that receive millions of requests. Here, we have focused on just three common use cases for analysis, sharing the analytic queries as part of the post. However, you can extend this architecture to gain deeper insights and generate usage reports to reduce latency and increase availability. This way, you can provide a better experience on your websites fronted with Amazon CloudFront.

In this blog post, we focused on building serverless architecture to analyze Amazon CloudFront access logs. Our plan is to extend the solution to provide rich visualization as part of our next blog post.


About the Authors

Rajeev Srinivasan is a Senior Solution Architect for AWS. He works very close with our customers to provide big data and NoSQL solution leveraging the AWS platform and enjoys coding . In his spare time he enjoys riding his motorcycle and reading books.

 

Sai Sriparasa is a consultant with AWS Professional Services. He works with our customers to provide strategic and tactical big data solutions with an emphasis on automation, operations & security on AWS. In his spare time, he follows sports and current affairs.

 

 


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Facebook Bans Sale of Piracy-Enabling Products & Devices

Post Syndicated from Andy original https://torrentfreak.com/facebook-bans-sale-of-piracy-enabling-products-devices-170525/

Riding the crest of a wave made possible by the rise of Internet streaming, piracy-enabled set-top boxes and similar devices have been hitting the homes of millions around the globe.

Often given the broad title of ‘Kodi Boxes’ after the legal open source software that commonly comes pre-installed, these devices are regularly configured for piracy with the aid of third-party addons.

Easy to use, set-top devices have opened up piracy to a whole new audience, normalizing it during the process. It’s a problem now being grappled with by anti-piracy outfits in a number of ways, including putting pressure on services where the boxes are being sold.

Now there are signs that Facebook has decided – or more likely been persuaded – to ban the sale of these devices from its platform. The latest addition to its Commerce Policy carries a new rule (13) which targets infringing set-top boxes almost perfectly.

“Items, products or services sold on Facebook must comply with our Community Standards, as well as the Commerce Policies,” the page reads.

“Sale of the following is prohibited on Facebook: Products or items that facilitate or encourage unauthorized access to digital media.”

The move by Facebook follows similar overtures from Amazon back in March. In a change to its policies, the company said that devices that promote or facilitate infringement would not be tolerated.

“Products offered for sale on Amazon should not promote, suggest the facilitation of, or actively enable the infringement of or unauthorized access to digital media or other protected content,” Amazon said.

“Any streaming media player or other device that violates this policy is prohibited from sale on Amazon,” the company added.

The recent move by Facebook was welcomed by Federation Against Copyright Theft chief, Kieron Sharp.

“It is great to see Facebook follow the likes of Amazon and eBay in making changes to their policies to prohibit the sale of illicit streaming devices on their platforms,” Sharpe said.

“These days social media sites are more than just a place to share photos and comments with friends and family. Unfortunately, the fast-paced development of these sites are being exploited by opportunists for criminal activity which needs to be disrupted.”

The sale of infringing devices on social media does indeed pose a challenge to the likes of FACT.

While most piracy devices have traditionally needed an expert touch to configure and then sell, in 2017 almost anyone can buy a standard Android device and set it up for piracy in a matter of minutes. This means that every interested citizen is a potential seller and Facebook provides a perfect platform that people are already familiar with.

Nevertheless, recent rulings from the EU Court of Justice have clarified two key issues, both of which will help in the fight to reduce the availability of ‘pirate’ boxes, wherever they appear.

In April, the ECJ declared such devices illegal to sell while clarifying that users who stream pirate content to their homes are also breaking the law.

It’s unlikely that any end users will be punished (particularly to the ridiculous extent erroneously reported by some media), but it certainly helps to demonstrate illegality across the board when outfits like FACT are considering prosecutions.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

Girl Busted For Pirating ‘Chicken Run’ Provides Food For Thought

Post Syndicated from Andy original https://torrentfreak.com/girl-busted-for-pirating-chicken-run-provides-food-for-thought-170521/

This past Thursday the BBC published an article about Gianna Mulville-Zanetta, a first year Social Policy student at Bristol University in the UK.

After getting caught downloading the stop-motion comedy-drama film Chicken Run using BitTorrent, the 18-year-old reportedly felt the wrath of the university’s IT department.

“I completely forgot I had downloaded it,” Gianna told the BBC.

“I got an email the day after I watched it on Netflix with my friend saying I had been removed from Eduroam – which is our wifi. It took about a day or more to download and that’s why I forgot I had it, it took forever.”

For her sins, Gianna was blocked from using the university’s wifi for 20 days, a period that coincided with her exams. With access to a 4G connection she says the ban didn’t affect her studies but of course, the potential for chaos was certainly there.

There appears to be no doubt that Gianna committed an infringement. However, that someone who prefers to watch something legally on Netflix gets caught up in something like this is pretty disappointing. But not a complete surprise.

Chicken Run was released in 2000 but only 12 years later did it appear on UK Netflix. According to New on Netflix, it was withdrawn from Netflix during November 2013, put back on two years later in 2015, removed a year later in 2016, and was only re-added on May 1 this year.

Considering the BBC states that the Chicken Run affair “has ruined much of May for Gianna”, the ban must’ve kicked in early this month. That means that Chicken Run was either not on UK Netflix when Gianna decided on her download, or had only been there for a day or two. Either way, if there had been less yo-yo’ing of its availability on Netflix, it’s possible this whole affair could’ve been completely avoided.

Moving on, the BBC article states that Gianna was “caught out by the university’s IT department.” Student newspaper The Tab makes a similar assumption, claiming that Gianna was “busted by an elite team of University IT technicians.”

However, those familiar with these issues will know that the ‘blame’ should be placed elsewhere, i.e., on rightsholders who are filing complaints directly with the university. The tactic is certainly an interesting one.

Despite there being dozens of residential ISPs the copyright holders could focus on, they choose not to do so outside the limited scope of the Get it Right campaign instead. Knowing that universities come down hard on students seems like a motivating factor here, one that students should be aware of.

The Tab went on to publish a screenshot of the complaint received by Gianna. It’s incomplete, but it contains information that allows us to investigate further.

The note that Gianna’s connection had been suspended to prevent the IT department from “receiving further complaints” is a dead giveaway of rightsholder involvement. But, further down is an even clearer clue that the complaint was made by someone outside the university.

The format used in the complaint is identical to that used by US and Australia-based anti-piracy outfit IP-Echelon. The company is known to work with Paramount Pictures who own the rights to Chicken Run.

In fact, if one searches the filesize referenced in the infringement notice (572,221,548), it’s possible to find an identical complaint processed by VPN service Proxy.sh.

Another Chicken Run complaint

Given the file size, we can further deduce that Gianna downloaded a 720p BrRip of Chicken Run that was placed online by now defunct release team/torrent site YIFY, which has also been referenced in a number of complaints sent to Google.

So what can we conclude from these series of events?

First of all, with less messing around by Paramount and/or Netflix, Gianna might have gone to Netflix first, having seen it previously in the listings on the platform. As it goes, it had been absent for months, having been pulled from the service at least twice before.

Second, we know that at least one person who chose to pirate Chicken Run avoided Gianna’s predicament by using a VPN service. While Gianna found herself disconnected, the VPN user walked away completely unscathed, with Paramount and IP-Echelon complaining to the VPN service and that being the end of the matter.

Third, allowing your real name and a copy of a copyright infringement complaint to be published alongside a confession is a risky business. While IP-Echelon isn’t known for pressuring people to pay settlements in the UK, the situation could have been very different if a copyright troll was involved.

Fourth, we can also conclude that while it’s believed that older content is safer to download, this story suggests otherwise. Chicken Run was released 17 years ago and is still being monitored by rightsholders.

Finally, stories of students getting banned from university Internet access are relatively commonplace in the United States, but the same out of the UK is extremely rare.

In fact, we’re not aware of such exclusions happening on a regular basis anywhere in the region, although Gianna told the BBC that she knows another person who is still being denied access to the Internet for downloading Shrek, another relatively ancient film.

That raises the possibility that some copyright holders have seriously begun targeting universities in the UK. If that’s the case, one has to question what has more value – uninterrupted Internet access while on campus or a movie download.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.