Tag Archives: Cloud applications

Server vs Endpoint Backup — Which is Best?

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/endpoint-backup-for-distributed-computing/

server and computer backup to the cloud

How common are these statements in your organization?

  • I know I saved that file. The application must have put it somewhere outside of my documents folder.” — Mike in Marketing
  • I was on the road and couldn’t get a reliable VPN connection. I guess that’s why my laptop wasn’t backed up.” — Sally in Sales
  • I try to follow file policies, but I had a deadline this week and didn’t have time to copy my files to the server.” — Felicia in Finance
  • I just did a commit of my code changes and that was when the coffee mug was knocked over onto the laptop.” — Erin in Engineering
  • If you need a file restored from backup, contact the help desk at [email protected] The IT department will get back to you.” — XYZ corporate intranet
  • Why don’t employees save files on the network drive like they’re supposed to?” — Isaac in IT

If these statements are familiar, most likely you rely on file server backups to safeguard your valuable endpoint data.

The problem is, the workplace has changed. Where server backups might have fit how offices worked at one time in the past, relying solely on server backups today means you could be missing valuable endpoint data from your backups. On top of that, you likely are unnecessarily expending valuable user and IT time in attempting to secure and restore endpoint data.

Times Have Changed, and so have Effective Enterprise Backup Strategies

The ways we use computers and handle files today are vastly different from just five or ten years ago. Employees are mobile, and we no longer are limited to monolithic PC and Mac-based office suites. Cloud applications are everywhere. Company-mandated network drive policies are difficult to enforce as office practices change, devices proliferate, and organizational culture evolves. Besides, your IT staff has other things to do than babysit your employees to make sure they follow your organization’s policies for managing files.

Server Backup has its Place, but Does it Support How People Work Today?

Many organizations still rely on server backup. If your organization works primarily in centralized offices with all endpoints — likely desktops — connected directly to your network, and you maintain tight control of how employees manage their files, it still might work for you.

Your IT department probably has set network drive policies that require employees to save files in standard places that are regularly backed up to your file server. Turns out, though, that even standard applications don’t always save files where IT would like them to be. They could be in a directory or folder that’s not regularly backed up.

As employees have become more mobile, they have adopted practices that enable them to access files from different places, but these practices might not fit in with your organization’s server policies. An employee saving a file to Dropbox might be planning to copy it to an “official” location later, but whether that ever happens could be doubtful. Often people don’t realize until it’s too late that accidentally deleting a file in one sync service directory means that all copies in all locations — even the cloud — are also deleted.

Employees are under increasing demands to produce, which means that network drive policies aren’t always followed; time constraints and deadlines can cause best practices to go out the window. Users will attempt to comply with policies as best they can — and you might get 70% or even 75% effective compliance — but getting even to that level requires training, monitoring, and repeatedly reminding employees of policies they need to follow — none of which leads to a good work environment.

Even if you get to 75% compliance with network file policies, what happens if the critical file needed to close out an end-of-year financial summary isn’t one of the files backed up? The effort required for IT to get from 70% to 80% or 90% of an endpoint’s files effectively backed up could require multiple hours from your IT department, and you still might not have backed up the one critical file you need later.

Your Organization Operates on its Data — And Today That Data Exists in Multiple Locations

Users are no longer tied to one endpoint, and may use different computers in the office, at home, or traveling. The greater the number of endpoints used, the greater the chance of an accidental or malicious device loss or data corruption. The loss of the Sales VP’s laptop at the airport on her way back from meeting with major customers can affect an entire organization and require weeks to resolve.

Even with the best intentions and efforts, following policies when out of the office can be difficult or impossible. Connecting to your private network when remote most likely requires a VPN, and VPN connectivity can be challenging from the lobby Wi-Fi at the Radisson. Server restores require time from the IT staff, which can mean taking resources away from other IT priorities and a growing backlog of requests from users to need their files as soon as possible. When users are dependent on IT to get back files critical to their work, employee productivity and often deadlines are affected.

Managing Finite Server Storage Is an Ongoing Challenge

Network drive backup usually requires on-premises data storage for endpoint backups. Since it is a finite resource, allocating that storage is another burden on your IT staff. To make sure that storage isn’t exceeded, IT departments often ration storage by department and/or user — another oversight duty for IT, and even more choices required by your IT department and department heads who have to decide which files to prioritize for backing up.

Adding Backblaze Endpoint Backup Improves Business Continuity and Productivity

Having an endpoint backup strategy in place can mitigate these problems and improve user productivity, as well. A good endpoint backup service, such as Backblaze Cloud Backup, will ensure that all devices are backed up securely, automatically, without requiring any action by the user or by your IT department.

For 99% of users, no configuration is required for Backblaze Backup. Everything on the endpoint is encrypted and securely backed up to the cloud, including program configuration files and files outside of standard document folders. Even temp files are backed up, which can prove invaluable when recovering a file after a crash or other program interruption. Cloud storage is unlimited with Backblaze Backup, so there are no worries about running out of storage or rationing file backups.

The Backblaze client can be silently and remotely installed to both Macintosh and Windows clients with no user interaction. And, with Backblaze Groups, your IT staff has complete visibility into when files were last backed up. IT staff can recover any backed up file, folder, or entire computer from the admin panel, and even give file restore capability to the user, if desired, which reduces dependency on IT and time spent waiting for restores.

With over 500 petabytes of customer data stored and one million files restored every hour of every day by Backblaze customers, you know that Backblaze Backup works for its users.

You Need Data Security That Matches the Way People Work Today

Both file server and endpoint backup have their places in an organization’s data security plan, but their use and value differ. If you already are using file server backup, adding endpoint backup will make a valuable contribution to your organization by reducing workload, improving productivity, and increasing confidence that all critical files are backed up.

By guaranteeing fast and automatic backup of all endpoint data, and matching the current way organizations and people work with data, Backblaze Backup will enable you to effectively and affordably meet the data security demands of your organization.

The post Server vs Endpoint Backup — Which is Best? appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

New AWS Auto Scaling – Unified Scaling For Your Cloud Applications

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-auto-scaling-unified-scaling-for-your-cloud-applications/

I’ve been talking about scalability for servers and other cloud resources for a very long time! Back in 2006, I wrote “This is the new world of scalable, on-demand web services. Pay for what you need and use, and not a byte more.” Shortly after we launched Amazon Elastic Compute Cloud (EC2), we made it easy for you to do this with the simultaneous launch of Elastic Load Balancing, EC2 Auto Scaling, and Amazon CloudWatch. Since then we have added Auto Scaling to other AWS services including ECS, Spot Fleets, DynamoDB, Aurora, AppStream 2.0, and EMR. We have also added features such as target tracking to make it easier for you to scale based on the metric that is most appropriate for your application.

Introducing AWS Auto Scaling
Today we are making it easier for you to use the Auto Scaling features of multiple AWS services from a single user interface with the introduction of AWS Auto Scaling. This new service unifies and builds on our existing, service-specific, scaling features. It operates on any desired EC2 Auto Scaling groups, EC2 Spot Fleets, ECS tasks, DynamoDB tables, DynamoDB Global Secondary Indexes, and Aurora Replicas that are part of your application, as described by an AWS CloudFormation stack or in AWS Elastic Beanstalk (we’re also exploring some other ways to flag a set of resources as an application for use with AWS Auto Scaling).

You no longer need to set up alarms and scaling actions for each resource and each service. Instead, you simply point AWS Auto Scaling at your application and select the services and resources of interest. Then you select the desired scaling option for each one, and AWS Auto Scaling will do the rest, helping you to discover the scalable resources and then creating a scaling plan that addresses the resources of interest.

If you have tried to use any of our Auto Scaling options in the past, you undoubtedly understand the trade-offs involved in choosing scaling thresholds. AWS Auto Scaling gives you a variety of scaling options: You can optimize for availability, keeping plenty of resources in reserve in order to meet sudden spikes in demand. You can optimize for costs, running close to the line and accepting the possibility that you will tax your resources if that spike arrives. Alternatively, you can aim for the middle, with a generous but not excessive level of spare capacity. In addition to optimizing for availability, cost, or a blend of both, you can also set a custom scaling threshold. In each case, AWS Auto Scaling will create scaling policies on your behalf, including appropriate upper and lower bounds for each resource.

AWS Auto Scaling in Action
I will use AWS Auto Scaling on a simple CloudFormation stack consisting of an Auto Scaling group of EC2 instances and a pair of DynamoDB tables. I start by removing the existing Scaling Policies from my Auto Scaling group:

Then I open up the new Auto Scaling Console and selecting the stack:

Behind the scenes, Elastic Beanstalk applications are always launched via a CloudFormation stack. In the screen shot above, awseb-e-sdwttqizbp-stack is an Elastic Beanstalk application that I launched.

I can click on any stack to learn more about it before proceeding:

I select the desired stack and click on Next to proceed. Then I enter a name for my scaling plan and choose the resources that I’d like it to include:

I choose the scaling strategy for each type of resource:

After I have selected the desired strategies, I click Next to proceed. Then I review the proposed scaling plan, and click Create scaling plan to move ahead:

The scaling plan is created and in effect within a few minutes:

I can click on the plan to learn more:

I can also inspect each scaling policy:

I tested my new policy by applying a load to the initial EC2 instance, and watched the scale out activity take place:

I also took a look at the CloudWatch metrics for the EC2 Auto Scaling group:

Available Now
We are launching AWS Auto Scaling today in the US East (Northern Virginia), US East (Ohio), US West (Oregon), EU (Ireland), and Asia Pacific (Singapore) Regions today, with more to follow. There’s no charge for AWS Auto Scaling; you pay only for the CloudWatch Alarms that it creates and any AWS resources that you consume.

As is often the case with our new services, this is just the first step on what we hope to be a long and interesting journey! We have a long roadmap, and we’ll be adding new features and options throughout 2018 in response to your feedback.


Spectre and Meltdown Attacks Against Microprocessors

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/01/spectre_and_mel_1.html

The security of pretty much every computer on the planet has just gotten a lot worse, and the only real solution — which of course is not a solution — is to throw them all away and buy new ones.

On Wednesday, researchers just announced a series of major security vulnerabilities in the microprocessors at the heart of the world’s computers for the past 15-20 years. They’ve been named Spectre and Meltdown, and they have to do with manipulating different ways processors optimize performance by rearranging the order of instructions or performing different instructions in parallel. An attacker who controls one process on a system can use the vulnerabilities to steal secrets elsewhere on the computer. (The research papers are here and here.)

This means that a malicious app on your phone could steal data from your other apps. Or a malicious program on your computer — maybe one running in a browser window from that sketchy site you’re visiting, or as a result of a phishing attack — can steal data elsewhere on your machine. Cloud services, which often share machines amongst several customers, are especially vulnerable. This affects corporate applications running on cloud infrastructure, and end-user cloud applications like Google Drive. Someone can run a process in the cloud and steal data from every other users on the same hardware.

Information about these flaws has been secretly circulating amongst the major IT companies for months as they researched the ramifications and coordinated updates. The details were supposed to be released next week, but the story broke early and everyone is scrambling. By now all the major cloud vendors have patched their systems against the vulnerabilities that can be patched against.

“Throw it away and buy a new one” is ridiculous security advice, but it’s what US-CERT recommends. It is also unworkable. The problem is that there isn’t anything to buy that isn’t vulnerable. Pretty much every major processor made in the past 20 years is vulnerable to some flavor of these vulnerabilities. Patching against Meltdown can degrade performance by almost a third. And there’s no patch for Spectre; the microprocessors have to be redesigned to prevent the attack, and that will take years. (Here’s a running list of who’s patched what.)

This is bad, but expect it more and more. Several trends are converging in a way that makes our current system of patching security vulnerabilities harder to implement.

The first is that these vulnerabilities affect embedded computers in consumer devices. Unlike our computer and phones, these systems are designed and produced at a lower profit margin with less engineering expertise. There aren’t security teams on call to write patches, and there often aren’t mechanisms to push patches onto the devices. We’re already seeing this with home routers, digital video recorders, and webcams. The vulnerability that allowed them to be taken over by the Mirai botnet last August simply can’t be fixed.

The second is that some of the patches require updating the computer’s firmware. This is much harder to walk consumers through, and is more likely to permanently brick the device if something goes wrong. It also requires more coordination. In November, Intel released a firmware update to fix a vulnerability in its Management Engine (ME): another flaw in its microprocessors. But it couldn’t get that update directly to users; it had to work with the individual hardware companies, and some of them just weren’t capable of getting the update to their customers.

We’re already seeing this. Some patches require users to disable the computer’s password, which means organizations can’t automate the patch. Some antivirus software blocks the patch, or — worse — crashes the computer. This results in a three-step process: patch your antivirus software, patch your operating system, and then patch the computer’s firmware.

The final reason is the nature of these vulnerabilities themselves. These aren’t normal software vulnerabilities, where a patch fixes the problem and everyone can move on. These vulnerabilities are in the fundamentals of how the microprocessor operates.

It shouldn’t be surprising that microprocessor designers have been building insecure hardware for 20 years. What’s surprising is that it took 20 years to discover it. In their rush to make computers faster, they weren’t thinking about security. They didn’t have the expertise to find these vulnerabilities. And those who did were too busy finding normal software vulnerabilities to examine microprocessors. Security researchers are starting to look more closely at these systems, so expect to hear about more vulnerabilities along these lines.

Spectre and Meltdown are pretty catastrophic vulnerabilities, but they only affect the confidentiality of data. Now that they — and the research into the Intel ME vulnerability — have shown researchers where to look, more is coming — and what they’ll find will be worse than either Spectre or Meltdown. There will be vulnerabilities that will allow attackers to manipulate or delete data across processes, potentially fatal in the computers controlling our cars or implanted medical devices. These will be similarly impossible to fix, and the only strategy will be to throw our devices away and buy new ones.

This isn’t to say you should immediately turn your computers and phones off and not use them for a few years. For the average user, this is just another attack method amongst many. All the major vendors are working on patches and workarounds for the attacks they can mitigate. All the normal security advice still applies: watch for phishing attacks, don’t click on strange e-mail attachments, don’t visit sketchy websites that might run malware on your browser, patch your systems regularly, and generally be careful on the Internet.

You probably won’t notice that performance hit once Meltdown is patched, except maybe in backup programs and networking applications. Embedded systems that do only one task, like your programmable thermostat or the computer in your refrigerator, are unaffected. Small microprocessors that don’t do all of the vulnerable fancy performance tricks are unaffected. Browsers will figure out how to mitigate this in software. Overall, the security of the average Internet-of-Things device is so bad that this attack is in the noise compared to the previously known risks.

It’s a much bigger problem for cloud vendors; the performance hit will be expensive, but I expect that they’ll figure out some clever way of detecting and blocking the attacks. All in all, as bad as Spectre and Meltdown are, I think we got lucky.

But more are coming, and they’ll be worse. 2018 will be the year of microprocessor vulnerabilities, and it’s going to be a wild ride.

Note: A shorter version of this essay previously appeared on CNN.com. My previous blog post on this topic contains additional links.

Serverless @ re:Invent 2017

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/serverless-reinvent-2017/

At re:Invent 2014, we announced AWS Lambda, what is now the center of the serverless platform at AWS, and helped ignite the trend of companies building serverless applications.

This year, at re:Invent 2017, the topic of serverless was everywhere. We were incredibly excited to see the energy from everyone attending 7 workshops, 15 chalk talks, 20 skills sessions and 27 breakout sessions. Many of these sessions were repeated due to high demand, so we are happy to summarize and provide links to the recordings and slides of these sessions.

Over the course of the week leading up to and then the week of re:Invent, we also had over 15 new features and capabilities across a number of serverless services, including AWS Lambda, Amazon API Gateway, AWS [email protected], AWS SAM, and the newly announced AWS Serverless Application Repository!

AWS Lambda

Amazon API Gateway

  • Amazon API Gateway Supports Endpoint Integrations with Private VPCs – You can now provide access to HTTP(S) resources within your VPC without exposing them directly to the public internet. This includes resources available over a VPN or Direct Connect connection!
  • Amazon API Gateway Supports Canary Release Deployments – You can now use canary release deployments to gradually roll out new APIs. This helps you more safely roll out API changes and limit the blast radius of new deployments.
  • Amazon API Gateway Supports Access Logging – The access logging feature lets you generate access logs in different formats such as CLF (Common Log Format), JSON, XML, and CSV. The access logs can be fed into your existing analytics or log processing tools so you can perform more in-depth analysis or take action in response to the log data.
  • Amazon API Gateway Customize Integration Timeouts – You can now set a custom timeout for your API calls as low as 50ms and as high as 29 seconds (the default is 30 seconds).
  • Amazon API Gateway Supports Generating SDK in Ruby – This is in addition to support for SDKs in Java, JavaScript, Android and iOS (Swift and Objective-C). The SDKs that Amazon API Gateway generates save you development time and come with a number of prebuilt capabilities, such as working with API keys, exponential back, and exception handling.

AWS Serverless Application Repository

Serverless Application Repository is a new service (currently in preview) that aids in the publication, discovery, and deployment of serverless applications. With it you’ll be able to find shared serverless applications that you can launch in your account, while also sharing ones that you’ve created for others to do the same.

AWS [email protected]

[email protected] now supports content-based dynamic origin selection, network calls from viewer events, and advanced response generation. This combination of capabilities greatly increases the use cases for [email protected], such as allowing you to send requests to different origins based on request information, showing selective content based on authentication, and dynamically watermarking images for each viewer.


Twitch Launchpad live announcements

Other service announcements

Here are some of the other highlights that you might have missed. We think these could help you make great applications:

AWS re:Invent 2017 sessions

Coming up with the right mix of talks for an event like this can be quite a challenge. The Product, Marketing, and Developer Advocacy teams for Serverless at AWS spent weeks reading through dozens of talk ideas to boil it down to the final list.

From feedback at other AWS events and webinars, we knew that customers were looking for talks that focused on concrete examples of solving problems with serverless, how to perform common tasks such as deployment, CI/CD, monitoring, and troubleshooting, and to see customer and partner examples solving real world problems. To that extent we tried to settle on a good mix based on attendee experience and provide a track full of rich content.

Below are the recordings and slides of breakout sessions from re:Invent 2017. We’ve organized them for those getting started, those who are already beginning to build serverless applications, and the experts out there already running them at scale. Some of the videos and slides haven’t been posted yet, and so we will update this list as they become available.

Find the entire Serverless Track playlist on YouTube.

Talks for people new to Serverless

Advanced topics

Expert mode

Talks for specific use cases

Talks from AWS customers & partners

Looking to get hands-on with Serverless?

At re:Invent, we delivered instructor-led skills sessions to help attendees new to serverless applications get started quickly. The content from these sessions is already online and you can do the hands-on labs yourself!
Build a Serverless web application

Still looking for more?

We also recently completely overhauled the main Serverless landing page for AWS. This includes a new Resources page containing case studies, webinars, whitepapers, customer stories, reference architectures, and even more Getting Started tutorials. Check it out!

Power data ingestion into Splunk using Amazon Kinesis Data Firehose

Post Syndicated from Tarik Makota original https://aws.amazon.com/blogs/big-data/power-data-ingestion-into-splunk-using-amazon-kinesis-data-firehose/

In late September, during the annual Splunk .conf, Splunk and Amazon Web Services (AWS) jointly announced that Amazon Kinesis Data Firehose now supports Splunk Enterprise and Splunk Cloud as a delivery destination. This native integration between Splunk Enterprise, Splunk Cloud, and Amazon Kinesis Data Firehose is designed to make AWS data ingestion setup seamless, while offering a secure and fault-tolerant delivery mechanism. We want to enable customers to monitor and analyze machine data from any source and use it to deliver operational intelligence and optimize IT, security, and business performance.

With Kinesis Data Firehose, customers can use a fully managed, reliable, and scalable data streaming solution to Splunk. In this post, we tell you a bit more about the Kinesis Data Firehose and Splunk integration. We also show you how to ingest large amounts of data into Splunk using Kinesis Data Firehose.

Push vs. Pull data ingestion

Presently, customers use a combination of two ingestion patterns, primarily based on data source and volume, in addition to existing company infrastructure and expertise:

  1. Pull-based approach: Using dedicated pollers running the popular Splunk Add-on for AWS to pull data from various AWS services such as Amazon CloudWatch or Amazon S3.
  2. Push-based approach: Streaming data directly from AWS to Splunk HTTP Event Collector (HEC) by using AWS Lambda. Examples of applicable data sources include CloudWatch Logs and Amazon Kinesis Data Streams.

The pull-based approach offers data delivery guarantees such as retries and checkpointing out of the box. However, it requires more ops to manage and orchestrate the dedicated pollers, which are commonly running on Amazon EC2 instances. With this setup, you pay for the infrastructure even when it’s idle.

On the other hand, the push-based approach offers a low-latency scalable data pipeline made up of serverless resources like AWS Lambda sending directly to Splunk indexers (by using Splunk HEC). This approach translates into lower operational complexity and cost. However, if you need guaranteed data delivery then you have to design your solution to handle issues such as a Splunk connection failure or Lambda execution failure. To do so, you might use, for example, AWS Lambda Dead Letter Queues.

How about getting the best of both worlds?

Let’s go over the new integration’s end-to-end solution and examine how Kinesis Data Firehose and Splunk together expand the push-based approach into a native AWS solution for applicable data sources.

By using a managed service like Kinesis Data Firehose for data ingestion into Splunk, we provide out-of-the-box reliability and scalability. One of the pain points of the old approach was the overhead of managing the data collection nodes (Splunk heavy forwarders). With the new Kinesis Data Firehose to Splunk integration, there are no forwarders to manage or set up. Data producers (1) are configured through the AWS Management Console to drop data into Kinesis Data Firehose.

You can also create your own data producers. For example, you can drop data into a Firehose delivery stream by using Amazon Kinesis Agent, or by using the Firehose API (PutRecord(), PutRecordBatch()), or by writing to a Kinesis Data Stream configured to be the data source of a Firehose delivery stream. For more details, refer to Sending Data to an Amazon Kinesis Data Firehose Delivery Stream.

You might need to transform the data before it goes into Splunk for analysis. For example, you might want to enrich it or filter or anonymize sensitive data. You can do so using AWS Lambda. In this scenario, Kinesis Data Firehose buffers data from the incoming source data, sends it to the specified Lambda function (2), and then rebuffers the transformed data to the Splunk Cluster. Kinesis Data Firehose provides the Lambda blueprints that you can use to create a Lambda function for data transformation.

Systems fail all the time. Let’s see how this integration handles outside failures to guarantee data durability. In cases when Kinesis Data Firehose can’t deliver data to the Splunk Cluster, data is automatically backed up to an S3 bucket. You can configure this feature while creating the Firehose delivery stream (3). You can choose to back up all data or only the data that’s failed during delivery to Splunk.

In addition to using S3 for data backup, this Firehose integration with Splunk supports Splunk Indexer Acknowledgments to guarantee event delivery. This feature is configured on Splunk’s HTTP Event Collector (HEC) (4). It ensures that HEC returns an acknowledgment to Kinesis Data Firehose only after data has been indexed and is available in the Splunk cluster (5).

Now let’s look at a hands-on exercise that shows how to forward VPC flow logs to Splunk.

How-to guide

To process VPC flow logs, we implement the following architecture.

Amazon Virtual Private Cloud (Amazon VPC) delivers flow log files into an Amazon CloudWatch Logs group. Using a CloudWatch Logs subscription filter, we set up real-time delivery of CloudWatch Logs to an Kinesis Data Firehose stream.

Data coming from CloudWatch Logs is compressed with gzip compression. To work with this compression, we need to configure a Lambda-based data transformation in Kinesis Data Firehose to decompress the data and deposit it back into the stream. Firehose then delivers the raw logs to the Splunk Http Event Collector (HEC).

If delivery to the Splunk HEC fails, Firehose deposits the logs into an Amazon S3 bucket. You can then ingest the events from S3 using an alternate mechanism such as a Lambda function.

When data reaches Splunk (Enterprise or Cloud), Splunk parsing configurations (packaged in the Splunk Add-on for Kinesis Data Firehose) extract and parse all fields. They make data ready for querying and visualization using Splunk Enterprise and Splunk Cloud.


Install the Splunk Add-on for Amazon Kinesis Data Firehose

The Splunk Add-on for Amazon Kinesis Data Firehose enables Splunk (be it Splunk Enterprise, Splunk App for AWS, or Splunk Enterprise Security) to use data ingested from Amazon Kinesis Data Firehose. Install the Add-on on all the indexers with an HTTP Event Collector (HEC). The Add-on is available for download from Splunkbase.

HTTP Event Collector (HEC)

Before you can use Kinesis Data Firehose to deliver data to Splunk, set up the Splunk HEC to receive the data. From Splunk web, go to the Setting menu, choose Data Inputs, and choose HTTP Event Collector. Choose Global Settings, ensure All tokens is enabled, and then choose Save. Then choose New Token to create a new HEC endpoint and token. When you create a new token, make sure that Enable indexer acknowledgment is checked.

When prompted to select a source type, select aws:cloudwatch:vpcflow.

Create an S3 backsplash bucket

To provide for situations in which Kinesis Data Firehose can’t deliver data to the Splunk Cluster, we use an S3 bucket to back up the data. You can configure this feature to back up all data or only the data that’s failed during delivery to Splunk.

Note: Bucket names are unique. Thus, you can’t use tmak-backsplash-bucket.

aws s3 create-bucket --bucket tmak-backsplash-bucket --create-bucket-configuration LocationConstraint=ap-northeast-1

Create an IAM role for the Lambda transform function

Firehose triggers an AWS Lambda function that transforms the data in the delivery stream. Let’s first create a role for the Lambda function called LambdaBasicRole.

Note: You can also set this role up when creating your Lambda function.

$ aws iam create-role --role-name LambdaBasicRole --assume-role-policy-document file://TrustPolicyForLambda.json

Here is TrustPolicyForLambda.json.

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


After the role is created, attach the managed Lambda basic execution policy to it.

$ aws iam attach-role-policy 
  --policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole 
  --role-name LambdaBasicRole


Create a Firehose Stream

On the AWS console, open the Amazon Kinesis service, go to the Firehose console, and choose Create Delivery Stream.

In the next section, you can specify whether you want to use an inline Lambda function for transformation. Because incoming CloudWatch Logs are gzip compressed, choose Enabled for Record transformation, and then choose Create new.

From the list of the available blueprint functions, choose Kinesis Data Firehose CloudWatch Logs Processor. This function unzips data and place it back into the Firehose stream in compliance with the record transformation output model.

Enter a name for the Lambda function, choose Choose an existing role, and then choose the role you created earlier. Then choose Create Function.

Go back to the Firehose Stream wizard, choose the Lambda function you just created, and then choose Next.

Select Splunk as the destination, and enter your Splunk Http Event Collector information.

Note: Amazon Kinesis Data Firehose requires the Splunk HTTP Event Collector (HEC) endpoint to be terminated with a valid CA-signed certificate matching the DNS hostname used to connect to your HEC endpoint. You receive delivery errors if you are using a self-signed certificate.

In this example, we only back up logs that fail during delivery.

To monitor your Firehose delivery stream, enable error logging. Doing this means that you can monitor record delivery errors.

Create an IAM role for the Firehose stream by choosing Create new, or Choose. Doing this brings you to a new screen. Choose Create a new IAM role, give the role a name, and then choose Allow.

If you look at the policy document, you can see that the role gives Kinesis Data Firehose permission to publish error logs to CloudWatch, execute your Lambda function, and put records into your S3 backup bucket.

You now get a chance to review and adjust the Firehose stream settings. When you are satisfied, choose Create Stream. You get a confirmation once the stream is created and active.

Create a VPC Flow Log

To send events from Amazon VPC, you need to set up a VPC flow log. If you already have a VPC flow log you want to use, you can skip to the “Publish CloudWatch to Kinesis Data Firehose” section.

On the AWS console, open the Amazon VPC service. Then choose VPC, Your VPC, and choose the VPC you want to send flow logs from. Choose Flow Logs, and then choose Create Flow Log. If you don’t have an IAM role that allows your VPC to publish logs to CloudWatch, choose Set Up Permissions and Create new role. Use the defaults when presented with the screen to create the new IAM role.

Once active, your VPC flow log should look like the following.

Publish CloudWatch to Kinesis Data Firehose

When you generate traffic to or from your VPC, the log group is created in Amazon CloudWatch. The new log group has no subscription filter, so set up a subscription filter. Setting this up establishes a real-time data feed from the log group to your Firehose delivery stream.

At present, you have to use the AWS Command Line Interface (AWS CLI) to create a CloudWatch Logs subscription to a Kinesis Data Firehose stream. However, you can use the AWS console to create subscriptions to Lambda and Amazon Elasticsearch Service.

To allow CloudWatch to publish to your Firehose stream, you need to give it permissions.

$ aws iam create-role --role-name CWLtoKinesisFirehoseRole --assume-role-policy-document file://TrustPolicyForCWLToFireHose.json

Here is the content for TrustPolicyForCWLToFireHose.json.

  "Statement": {
    "Effect": "Allow",
    "Principal": { "Service": "logs.us-east-1.amazonaws.com" },
    "Action": "sts:AssumeRole"


Attach the policy to the newly created role.

$ aws iam put-role-policy 
    --role-name CWLtoKinesisFirehoseRole 
    --policy-name Permissions-Policy-For-CWL 
    --policy-document file://PermissionPolicyForCWLToFireHose.json

Here is the content for PermissionPolicyForCWLToFireHose.json.

        "Resource":["arn:aws:firehose:us-east-1:YOUR-AWS-ACCT-NUM:deliverystream/ FirehoseSplunkDeliveryStream"]

Finally, create a subscription filter.

$ aws logs put-subscription-filter 
   --log-group-name " /vpc/flowlog/FirehoseSplunkDemo" 
   --filter-name "Destination" 
   --filter-pattern "" 
   --destination-arn "arn:aws:firehose:us-east-1:YOUR-AWS-ACCT-NUM:deliverystream/FirehoseSplunkDeliveryStream" 
   --role-arn "arn:aws:iam::YOUR-AWS-ACCT-NUM:role/CWLtoKinesisFirehoseRole"

When you run the AWS CLI command preceding, you don’t get any acknowledgment. To validate that your CloudWatch Log Group is subscribed to your Firehose stream, check the CloudWatch console.

As soon as the subscription filter is created, the real-time log data from the log group goes into your Firehose delivery stream. Your stream then delivers it to your Splunk Enterprise or Splunk Cloud environment for querying and visualization. The screenshot following is from Splunk Enterprise.

In addition, you can monitor and view metrics associated with your delivery stream using the AWS console.


Although our walkthrough uses VPC Flow Logs, the pattern can be used in many other scenarios. These include ingesting data from AWS IoT, other CloudWatch logs and events, Kinesis Streams or other data sources using the Kinesis Agent or Kinesis Producer Library. We also used Lambda blueprint Kinesis Data Firehose CloudWatch Logs Processor to transform streaming records from Kinesis Data Firehose. However, you might need to use a different Lambda blueprint or disable record transformation entirely depending on your use case. For an additional use case using Kinesis Data Firehose, check out This is My Architecture Video, which discusses how to securely centralize cross-account data analytics using Kinesis and Splunk.


Additional Reading

If you found this post useful, be sure to check out Integrating Splunk with Amazon Kinesis Streams and Using Amazon EMR and Hunk for Rapid Response Log Analysis and Review.

About the Authors

Tarik Makota is a solutions architect with the Amazon Web Services Partner Network. He provides technical guidance, design advice and thought leadership to AWS’ most strategic software partners. His career includes work in an extremely broad software development and architecture roles across ERP, financial printing, benefit delivery and administration and financial services. He holds an M.S. in Software Development and Management from Rochester Institute of Technology.




Roy Arsan is a solutions architect in the Splunk Partner Integrations team. He has a background in product development, cloud architecture, and building consumer and enterprise cloud applications. More recently, he has architected Splunk solutions on major cloud providers, including an AWS Quick Start for Splunk that enables AWS users to easily deploy distributed Splunk Enterprise straight from their AWS console. He’s also the co-author of the AWS Lambda blueprints for Splunk. He holds an M.S. in Computer Science Engineering from the University of Michigan.