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Harmonize, Query, and Visualize Data from Various Providers using AWS Glue, Amazon Athena, and Amazon QuickSight

Post Syndicated from Ben Snively original https://aws.amazon.com/blogs/big-data/harmonize-query-and-visualize-data-from-various-providers-using-aws-glue-amazon-athena-and-amazon-quicksight/

Have you ever been faced with many different data sources in different formats that need to be analyzed together to drive value and insights?  You need to be able to query, analyze, process, and visualize all your data as one canonical dataset, regardless of the data source or original format.

In this post, I walk through using AWS Glue to create a query optimized, canonical dataset on Amazon S3 from three different datasets and formats. Then, I use Amazon Athena and Amazon QuickSight to query against that data quickly and easily.

AWS Glue overview

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for query and analytics. You can create and run an ETL job with a few clicks in the AWS Management Console. Point AWS Glue to your data stored on AWS, and a crawler discovers your data, classifies it, and stores the associated metadata (such as table definitions) in the AWS Glue Data Catalog. After it’s cataloged, your data is immediately searchable, queryable, and available for ETL. AWS Glue generates the ETL code for data transformation, and loads the transformed data into a target data store for analytics.

The AWS Glue ETL engine generates Python code that is entirely customizable, reusable, and portable. You can edit the code using your favorite IDE or notebook and share it with others using GitHub. After your ETL job is ready, you can schedule it to run on the AWS Glue fully managed, scale-out Spark environment, using its flexible scheduler with dependency resolution, job monitoring, and alerting.

AWS Glue is serverless. It automatically provisions the environment needed to complete the job, and customers pay only for the compute resources consumed while running ETL jobs. With AWS Glue, data can be available for analytics in minutes.

Walkthrough

During this post, I step through an example using the New York City Taxi Records dataset. I focus on one month of data, but you could easily do this for the entire eight years of data. At the time of this post, AWS Glue is available in US-East-1 (N. Virginia).

As you crawl the unknown dataset, you discover that the data is in different formats, depending on the type of taxi. You then convert the data to a canonical form, start to analyze it, and build a set of visualizations… all without launching a single server.

Discover the data

To analyze all the taxi rides for January 2016, you start with a set of data in S3. First, create a database for this project within AWS Glue. A database is a set of associated table definitions, organized into a logical group. In Athena, database names are all lowercase, no matter what you type.

Next, add a new crawler and have it infer the schemas, structure, and other properties of the data.

  1. Under Crawlers, choose Add crawler.
  2. For Name, type “NYCityTaxiCrawler”.
  3. Choose an IAM role that AWS Glue can use for the crawler. For more information, see the AWS Glue Developer Guide.
  4. For Data store, type S3.
  5. For Crawl data in, choose Specified path in another account.
  6. For Include path, type “s3://serverless-analytics/glue-blog”.
  7. For Add Another data store, choose No.
  8. For Frequency, choose On demand. This allows you to customize when the crawler runs.
  9. Configure the crawler output database and prefix:
    1. For Database, select the database created earlier, “nycitytaxianalysis”.
    2. For Prefix added to tables (optional), type “blog_”.
    3. Choose Finish and Run it now.

The crawler runs and indicates that it found three tables.

If you look under Tables, you can see the three new tables that were created under the database “nycitytaxianalysis”.

The crawler used the built-in classifiers and identified the tables as CSV, inferred the columns/data types, and collected a set of properties for each table. If you look in each of those table definitions, you see the number of rows for each dataset found and that the columns don’t match between tables. As an example, bringing up the blog_yellow table, you can see the yellow dataset for January 2017 with 8.7 million rows, the location on S3, and the various columns found.

Optimize queries and get data into a canonical form

Create an ETL job to move this data into a query-optimized form. In a future post, I’ll cover how to partition the query-optimized data. You convert the data into a column format, changing the storage type to Parquet, and writing the data to a bucket that you own.

  1. Create a new ETL job and name it NYCityTaxiYellow.
    1. Select an IAM role that has permissions to write to a new S3 location in a bucket that you own.
    2. Specify a location for the script and tmp space on S3.
  2. Choose the blog_yellow table as a data source.
  3. Specify a new location (a new prefix location without any existing objects) to store the results.
  4. In the transform, rename the pickup and dropoff dates to be generic fields. The raw data actually lists these fields differently and you should use a common name.  To do this, choose the target column name and retype the new field name.

    Also, change the data types to be time stamps rather than strings. The following table outlines the updates.

    Old Name Target Name Target Data Type
    tpep_pickup_datetime pickup_date timestamp
    tpep_dropoff_datetime dropoff_date timestamp
  5. Choose Next and Finish.

In this case, AWS Glue creates a script. You could also start with a blank script or provide your own.

Because AWS Glue uses Apache Spark behind the scenes, you can easily switch from an AWS Glue DynamicFrame to a Spark DataFrame and do advanced operations within Apache Spark.  Just as easily, you can switch back and continue to use the transforms and tables from the catalog.

To show this, convert to a DataFrame and add a new field column indicating the type of taxi. Make the following custom modifications in PySpark.

Add the following header:

from pyspark.sql.functions import lit
from awsglue.dynamicframe import DynamicFrame

Find the last call before the the line that start with the datasink.  This is the dynamic frame that is being used to write out the data.

Let’s now convert that to a DataFrame.  Please replace the <DYNAMIC_FRAME_NAME> with the name generated in the script.

##----------------------------------
#convert to a Spark DataFrame...
customDF = <DYNAMIC_FRAME_NAME>.toDF()
 
#add a new column for "type"
customDF = customDF.withColumn("type", lit('yellow'))
 
# Convert back to a DynamicFrame for further processing.
customDynamicFrame = DynamicFrame.fromDF(customDF, glueContext, "customDF_df")
##----------------------------------

In the last datasink call, change the dynamic frame to point to the new custom dynamic frame created from the Spark DataFrame:

datasink4 = glueContext.write_dynamic_frame.from_options(frame = customDynamicFrame, connection_type = "s3", connection_options = {"path": "s3://<YOURBUCKET/ AND PREFIX/"}, format = "parquet", transformation_ctx = "datasink4")

Save and run the ETL.

 

To use AWS Glue with Athena, you must upgrade your Athena data catalog to the AWS Glue Data Catalog. Without the upgrade, tables and partitions created by AWS Glue cannot be queried with Athena. As of August 14, 2017, the AWS Glue Data Catalog is only available in US-East-1 (N. Virginia). Start the upgrade in the Athena console. For more information, see Integration with AWS Glue in the Amazon Athena User Guide.

Next, we’ll go into Athena and create a table there for the new data format.  This shows how you can interact with the data catalog either through Glue or the Athena.

  • Go into the Athena Web Console
  • Select the nycitytaxianalysis database
  • Run the following create statement:
CREATE EXTERNAL TABLE IF NOT EXISTS nycitytaxianalysis.CanonicalData (
  VendorID INT,
  pickup_datetime TIMESTAMP,
  dropoff_datetime TIMESTAMP,
  passenger_count BIGINT,
  trip_distance DOUBLE,
  pickup_longitude DOUBLE,
  pickup_latitude DOUBLE,
  RatecodeID BIGINT,
  store_and_fwd_flag STRING,
  dropoff_longitude DOUBLE,
  dropoff_latitude DOUBLE,
  payment_type INT,
  fare_amount DOUBLE,
  extra DOUBLE,
  mta_tax DOUBLE,
  tip_amount DOUBLE,
  tolls_amount DOUBLE,
  improvement_surcharge DOUBLE,
  total_amount DOUBLE,
  type STRING
)
STORED AS PARQUET
LOCATION 's3://<YOUR_ETL_TARGET_SCRIPT_LOCATION>'
TBLPROPERTIES ('classification'='parquet')

Note: The table properties are important for Glue ETL to know the destination format.  More details can be found here: http://docs.aws.amazon.com/athena/latest/ug/glue-athena.html#creating-tables-using-ate-for-aws-glue-etl-jobs

Within the Glue Data Catalog, select the new table that you created through Athena under Actions, select “View data”.


If you do a query on the count per type of taxi, you notice that there is only “yellow” taxi data in the canonical location.  Convert “green” and “fhv” data:

SELECT type, count(*) FROM <OUTPUT_TABLE_NAME> group by type

In the AWS Glue console, create a new ETL job named “NYCityTaxiGreen”. Choose blog_green as the source and the new canonical location as the target. Choose the new pickup_date and dropoff_date fields for the target field types for the date variables, as shown below.

In the script, insert the following two lines at the end of the top imports:

from pyspark.sql.functions import lit
from awsglue.dynamicframe import DynamicFrame

Find the last call before the the line that start with the datasink.  This is the dynamic frame that is being used to write out the data.

Let’s now convert that to a DataFrame.  Please replace the <DYNAMIC_FRAME_NAME> with the name generated in the script.

Type this line before the last sink calls:
##----------------------------------
#convert to a Spark DataFrame...
customDF = <DYNAMIC_FRAME_NAME>.toDF()
 
#add a new column for "type"
customDF = customDF.withColumn("type", lit('green'))
 
# Convert back to a DynamicFrame for further processing.
customDynamicFrame = DynamicFrame.fromDF(customDF, glueContext, "customDF_df")

In the last datasink call, change the dynamic frame to point to the new custom dynamic frame created from the Spark DataFrame:

datasink4 = glueContext.write_dynamic_frame.from_catalog(frame = customDynamicFrame, database = "nycitytaxianalysis", table_name = "glue_blog", transformation_ctx = "datasink4")

Notice that it matches the last line in the commands that you pasted earlier. After the ETL job finishes, you can require the count per type through Athena and see the sizes:

SELECT type, count(*) FROM <OUTPUT_TABLE_NAME> group by type

What you have done is taken two datasets that contained taxi data in different formats and converted them into a single, query-optimized format. That format can easily be consumed by various analytical tools, including Athena, EMR, and Redshift Spectrum. You’ve done all of this without launching a single server.

You have one more dataset to convert, so create a new ETL job for that. Name the job “NYCityTaxiFHV”.  This dataset is limited as it only has three fields, one of which you are converting. Choose “–” (dash) for the dispatcher and location, and map the pickup date to the canonical field.

In the script, insert the following two lines at the end of the top imports:

from pyspark.sql.functions import lit
from awsglue.dynamicframe import DynamicFrame

Type the following line before the last sink calls:

##----------------------------------
#convert to a Spark DataFrame...
customDF = resolvechoice3.toDF()

#add a new column for "type"
customDF = customDF.withColumn("type", lit('fhv'))

# Convert back to a DynamicFrame for further processing.
customDynamicFrame = DynamicFrame.fromDF(customDF, glueContext, "customDF_df")

In the last datasink call, change the dynamic frame to point to the new custom dynamic frame created from the Spark DataFrame:

datasink4 = glueContext.write_dynamic_frame.from_catalog(frame = customDynamicFrame, database = "nycitytaxianalysis", table_name = "glue_blog", transformation_ctx = "datasink4")

After the third ETL job completes, you can query the single dataset, in a query-optimized form on S3:

SELECT type, count(*) FROM <OUTPUT_TABLE_NAME> group by type

Query across the data with Athena

Here’s a query:

SELECT min(trip_distance) as minDistance,
         max(trip_distance) as maxDistance,
         min(total_amount) as minTotal,
         max(total_amount) as maxTotal
FROM <OUTPUT_TABLE_NAME>

Within a second or two, you can quickly see the ranges for these values.

You can see some outliers in the total amount and distances that are = 0.  In the next query, don’t include those when calculating things like average cost per mile:

SELECT type,
         avg(trip_distance) AS avgDist,
         avg(total_amount/trip_distance) AS avgCostPerMile
FROM <OUTPUT_TABLE_NAME>
WHERE trip_distance > 0
        AND total_amount > 0
GROUP BY  type

Interesting note: you aren’t seeing FHV broken out yet, because that dataset didn’t have a mapping to these fields. Here’s a query that’s a little more complicated, to calculate a 99th percentile:

SELECT TYPE, 
       avg(trip_distance) avgDistance, 
       avg(total_amount/trip_distance) avgCostPerMile, 
       avg(total_amount) avgCost,
       approx_percentile(total_amount, .99) percentile99
FROM <OUTPUT_TABLE_NAME> 
WHERE trip_distance > 0 and total_amount > 0
GROUP BY TYPE

Visualizing the data in QuickSight

Amazon QuickSight is a data visualization service you can use to analyze the data that you just combined. For more detailed instructions, see the Amazon QuickSight User Guide.

  1. Create a new Athena data source, if you haven’t created one yet.
  2. Create a new dataset for NY taxi data, and point it to the canonical taxi data.
  3. Choose Edit/Preview data.
  4. Add a new calculated field named HourOfDay.

  1. Choose Save and Visualize.

You can now visualize this dataset and query through Athena the canonical dataset on S3 that has been converted by AWS Glue. Selecting HourOfDay allows you to see how many taxi rides there are based on the hour of the day. This is a calculated field that is being derived by the time stamp in the raw dataset.

You can also deselect that and choose type to see the total counts again by type.

Now choose the pickup_date field in addition to the type field and notice that the plot type automatically changed. Because time is being used, it’s showing a time chart defaulting to the year. Choose a day interval instead.

Expand the x axis to zoom out. You can clearly see a large drop in all rides on January 23, 2016.

Summary

In the post, you went from data investigation to analyzing and visualizing a canonical dataset, without starting a single server. You started by crawling a dataset you didn’t know anything about and the crawler told you the structure, columns, and counts of records.

From there, you saw that all three datasets were in different formats, but represented the same thing: NY City Taxi rides. You then converted them into a canonical (or normalized) form that is easily queried through Athena and QuickSight, in addition to a wide number of different tools not covered in this post.
If you have questions or suggestions, please comment below.

Additional Reading

Learn how to harmonize, search, and analyze loosely coupled datasets on AWS.

 

About the Author

Ben Snively is a Public Sector Specialist Solutions Architect. He works with government, non-profit and education customers on big data and analytical projects, helping them build solutions using AWS. In his spare time he adds IoT sensors throughout his house and runs analytics on it.

 

 

 

Showtime Seeks Injunction to Stop Mayweather v McGregor Piracy

Post Syndicated from Andy original https://torrentfreak.com/showtime-seeks-injunction-to-stop-mayweather-v-mcgregor-piracy-170816/

It’s the fight that few believed would become reality but on August 26, at the T-Mobile Arena in Las Vegas, Floyd Mayweather Jr. will duke it out with UFC lightweight champion Conor McGregor.

Despite being labeled a freak show by boxing purists, it is set to become the biggest combat sports event of all time. Mayweather, undefeated in his professional career, will face brash Irishman McGregor, who has gained a reputation for accepting fights with anyone – as long as there’s a lot of money involved. Big money is definitely the theme of the Mayweather bout.

Dubbed “The Money Fight”, some predict it could pull in a billion dollars, with McGregor pocketing $100m and Mayweather almost certainly more. Many of those lucky enough to gain entrance on the night will have spent thousands on their tickets but for the millions watching around the world….iiiiiiiit’s Showtimmme….with hefty PPV prices attached.

Of course, not everyone will be handing over $89.95 to $99.99 to watch the event officially on Showtime. Large numbers will turn to the many hundreds of websites set to stream the fight for free online, which has the potential to reduce revenues for all involved. With that in mind, Showtime Networks has filed a lawsuit in California which attempts to preemptively tackle this piracy threat.

The suit targets a number of John Does said to be behind a network of dozens of sites planning to stream the fight online for free. Defendant 1, using the alias “Kopa Mayweather”, is allegedly the operator of LiveStreamHDQ, a site that Showtime has grappled with previously.

“Plaintiff has had extensive experience trying to prevent live streaming websites from engaging in the unauthorized reproduction and distribution of Plaintiff’s copyrighted works in the past,” the lawsuit reads.

“In addition to bringing litigation, this experience includes sending cease and desist demands to LiveStreamHDQ in response to its unauthorized live streaming of the record-breaking fight between Floyd Mayweather, Jr. and Manny Pacquiao.”

Showtime says that LiveStreamHDQ is involved in the operations of at least 41 other sites that have been set up to specifically target people seeking to watch the fight without paying. Each site uses a .US ccTLD domain name.

Sample of the sites targeted by the lawsuit

Showtime informs the court that the registrant email and IP addresses of the domains overlap, which provides further proof that they’re all part of the same operation. The TV network also highlights various statements on the sites in question which demonstrate intent to show the fight without permission, including the highly dubious “Watch From Here Mayweather vs Mcgregor Live with 4k Display.”

In addition, the lawsuit is highly critical of efforts by the sites’ operator(s) to stuff the pages with fight-related keywords in order to draw in as much search engine traffic as they can.

“Plaintiff alleges that Defendants have engaged in such keyword stuffing as a form of search engine optimization in an effort to attract as much web traffic as possible in the form of Internet users searching for a way to access a live stream of the Fight,” it reads.

While site operators are expected to engage in such behavior, Showtime says that these SEO efforts have been particularly successful, obtaining high-ranking positions in major search engines for the would-be pirate sites.

For instance, Showtime says that a Google search for “Mayweather McGregor Live” results in four of the target websites appearing in the first 100 results, i.e the first 10 pages. Interestingly, however, to get that result searchers would need to put the search in quotes as shown above, since a plain search fails to turn anything up in hundreds of results.

At this stage, the important thing to note is that none of the sites are currently carrying links to the fight, because the fight is yet to happen. Nevertheless, Showtime is convinced that come fight night, all of the target websites will be populated with pirate links, accessible for free or after paying a fee. This needs to be stopped, it argues.

“Defendants’ anticipated unlawful distribution will impair the marketability and profitability of the Coverage, and interfere with Plaintiff’s own authorized distribution of the Coverage, because Defendants will provide consumers with an opportunity to view the Coverage in its entirety for free, rather than paying for the Coverage provided through Plaintiff’s authorized channels.

“This is especially true where, as here, the work at issue is live coverage of a one-time live sporting event whose outcome is unknown,” the network writes.

Showtime informs the court that it made efforts to contact the sites in question but had just a single response from an individual who claimed to be sports blogger who doesn’t offer streaming services. The undertone is one of disbelief.

In closing, Showtime demands a temporary restraining order, preliminary injunction, and permanent injunction, prohibiting the defendants from making the fight available in any way, and/or “forming new entities” in order to circumvent any subsequent court order. Compensation for suspected damages is also requested.

Showtime previously applied for and obtained a similar injunction to cover the (hugely disappointing) Mayweather v Pacquiao fight in 2015. In that case, websites were ordered to be taken down on the day before the fight.

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

What’s the Diff: Programs, Processes, and Threads

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/whats-the-diff-programs-processes-and-threads/

let's talk about Threads

How often have you heard the term threading in relation to a computer program, but you weren’t exactly sure what it meant? How about processes? You likely understand that a thread is somehow closely related to a program and a process, but if you’re not a computer science major, maybe that’s as far as your understanding goes.

Knowing what these terms mean is absolutely essential if you are a programmer, but an understanding of them also can be useful to the average computer user. Being able to look at and understand the Activity Monitor on the Macintosh, the Task Manager on Windows, or Top on Linux can help you troubleshoot which programs are causing problems on your computer, or whether you might need to install more memory to make your system run better.

Let’s take a few minutes to delve into the world of computer programs and sort out what these terms mean. We’ll simplify and generalize some of the ideas, but the general concepts we cover should help clarify the difference between the terms.

Programs

First of all, you probably are aware that a program is the code that is stored on your computer that is intended to fulfill a certain task. There are many types of programs, including programs that help your computer function and are part of the operating system, and other programs that fulfill a particular job. These task-specific programs are also known as “applications,” and can include programs such as word processing, web browsing, or emailing a message to another computer.

Program

Programs are typically stored on disk or in non-volatile memory in a form that can be executed by your computer. Prior to that, they are created using a programming language such as C, Lisp, Pascal, or many others using instructions that involve logic, data and device manipulation, recurrence, and user interaction. The end result is a text file of code that is compiled into binary form (1’s and 0’s) in order to run on the computer. Another type of program is called “interpreted,” and instead of being compiled in advance in order to run, is interpreted into executable code at the time it is run. Some common, typically interpreted programming languages, are Python, PHP, JavaScript, and Ruby.

The end result is the same, however, in that when a program is run, it is loaded into memory in binary form. The computer’s CPU (Central Processing Unit) understands only binary instructions, so that’s the form the program needs to be in when it runs.

Perhaps you’ve heard the programmer’s joke, “There are only 10 types of people in the world, those who understand binary, and those who don’t.”

Binary is the native language of computers because an electrical circuit at its basic level has two states, on or off, represented by a one or a zero. In the common numbering system we use every day, base 10, each digit position can be anything from 0 to 9. In base 2 (or binary), each position is either a 0 or a 1. (In a future blog post we might cover quantum computing, which goes beyond the concept of just 1’s and 0’s in computing.)

Decimal—Base 10 Binary—Base 2
0 0000
1 0001
2 0010
3 0011
4 0100
5 0101
6 0110
7 0111
8 1000
9 1001

How Processes Work

The program has been loaded into the computer’s memory in binary form. Now what?

An executing program needs more than just the binary code that tells the computer what to do. The program needs memory and various operating system resources that it needs in order to run. A “process” is what we call a program that has been loaded into memory along with all the resources it needs to operate. The “operating system” is the brains behind allocating all these resources, and comes in different flavors such as macOS, iOS, Microsoft Windows, Linux, and Android. The OS handles the task of managing the resources needed to turn your program into a running process.

Some essential resources every process needs are registers, a program counter, and a stack. The “registers” are data holding places that are part of the computer processor (CPU). A register may hold an instruction, a storage address, or other kind of data needed by the process. The “program counter,” also called the “instruction pointer,” keeps track of where a computer is in its program sequence. The “stack” is a data structure that stores information about the active subroutines of a computer program and is used as scratch space for the process. It is distinguished from dynamically allocated memory for the process that is known as “the heap.”

diagram of how processes work

There can be multiple instances of a single program, and each instance of that running program is a process. Each process has a separate memory address space, which means that a process runs independently and is isolated from other processes. It cannot directly access shared data in other processes. Switching from one process to another requires some time (relatively) for saving and loading registers, memory maps, and other resources.

This independence of processes is valuable because the operating system tries its best to isolate processes so that a problem with one process doesn’t corrupt or cause havoc with another process. You’ve undoubtedly run into the situation in which one application on your computer freezes or has a problem and you’ve been able to quit that program without affecting others.

How Threads Work

So, are you still with us? We finally made it to threads!

A thread is the unit of execution within a process. A process can have anywhere from just one thread to many threads.

Process vs. Thread

diagram of threads in a process over time

When a process starts, it is assigned memory and resources. Each thread in the process shares that memory and resources. In single-threaded processes, the process contains one thread. The process and the thread are one and the same, and there is only one thing happening.

In multithreaded processes, the process contains more than one thread, and the process is accomplishing a number of things at the same time (technically, it’s almost at the same time—read more on that in the “What about Parallelism and Concurrency?” section below).

diagram of single and multi-treaded process

We talked about the two types of memory available to a process or a thread, the stack and the heap. It is important to distinguish between these two types of process memory because each thread will have its own stack, but all the threads in a process will share the heap.

Threads are sometimes called lightweight processes because they have their own stack but can access shared data. Because threads share the same address space as the process and other threads within the process, the operational cost of communication between the threads is low, which is an advantage. The disadvantage is that a problem with one thread in a process will certainly affect other threads and the viability of the process itself.

Threads vs. Processes

So to review:

  1. The program starts out as a text file of programming code,
  2. The program is compiled or interpreted into binary form,
  3. The program is loaded into memory,
  4. The program becomes one or more running processes.
  5. Processes are typically independent of each other,
  6. While threads exist as the subset of a process.
  7. Threads can communicate with each other more easily than processes can,
  8. But threads are more vulnerable to problems caused by other threads in the same process.

Processes vs. Threads — Advantages and Disadvantages

Process Thread
Processes are heavyweight operations Threads are lighter weight operations
Each process has its own memory space Threads use the memory of the process they belong to
Inter-process communication is slow as processes have different memory addresses Inter-thread communication can be faster than inter-process communication because threads of the same process share memory with the process they belong to
Context switching between processes is more expensive Context switching between threads of the same process is less expensive
Processes don’t share memory with other processes Threads share memory with other threads of the same process

What about Concurrency and Parallelism?

A question you might ask is whether processes or threads can run at the same time. The answer is: it depends. On a system with multiple processors or CPU cores (as is common with modern processors), multiple processes or threads can be executed in parallel. On a single processor, though, it is not possible to have processes or threads truly executing at the same time. In this case, the CPU is shared among running processes or threads using a process scheduling algorithm that divides the CPU’s time and yields the illusion of parallel execution. The time given to each task is called a “time slice.” The switching back and forth between tasks happens so fast it is usually not perceptible. The terms parallelism (true operation at the same time) and concurrency (simulated operation at the same time), distinguish between the two type of real or approximate simultaneous operation.

diagram of concurrency and parallelism

Why Choose Process over Thread, or Thread over Process?

So, how would a programmer choose between a process and a thread when creating a program in which she wants to execute multiple tasks at the same time? We’ve covered some of the differences above, but let’s look at a real world example with a program that many of us use, Google Chrome.

When Google was designing the Chrome browser, they needed to decide how to handle the many different tasks that needed computer, communications, and network resources at the same time. Each browser window or tab communicates with multiple servers on the internet to retrieve text, programs, graphics, audio, video, and other resources, and renders that data for display and interaction with the user. In addition, the browser can open many windows, each with many tasks.

Google had to decide how to handle that separation of tasks. They chose to run each browser window in Chrome as a separate process rather than a thread or many threads, as is common with other browsers. Doing that brought Google a number of benefits. Running each window as a process protects the overall application from bugs and glitches in the rendering engine and restricts access from each rendering engine process to others and to the rest of the system. Isolating JavaScript programs in a process prevents them from running away with too much CPU time and memory, and making the entire browser non-responsive.

Google made the calculated trade-off with a multi-processing design as starting a new process for each browser window has a higher fixed cost in memory and resources than using threads. They were betting that their approach would end up with less memory bloat overall.

Using processes instead of threads provides better memory usage when memory gets low. An inactive window is treated as a lower priority by the operating system and becomes eligible to be swapped to disk when memory is needed for other processes, helping to keep the user-visible windows more responsive. If the windows were threaded, it would be more difficult to separate the used and unused memory as cleanly, wasting both memory and performance.

You can read more about Google’s design decisions on Google’s Chromium Blog or on the Chrome Introduction Comic.

The screen capture below shows the Google Chrome processes running on a MacBook Air with many tabs open. Some Chrome processes are using a fair amount of CPU time and resources, and some are using very little. You can see that each process also has many threads running as well.

activity monitor of Google Chrome

The Activity Monitor or Task Manager on your system can be a valuable ally in helping fine-tune your computer or troubleshooting problems. If your computer is running slowly, or a program or browser window isn’t responding for a while, you can check its status using the system monitor. Sometimes you’ll see a process marked as “Not Responding.” Try quitting that process and see if your system runs better. If an application is a memory hog, you might consider choosing a different application that will accomplish the same task.

Windows Task Manager view

Made it This Far?

We hope this Tron-like dive into the fascinating world of computer programs, processes, and threads has helped clear up some questions you might have had.

The next time your computer is running slowly or an application is acting up, you know your assignment. Fire up the system monitor and take a look under the hood to see what’s going on. You’re in charge now.

We love to hear from you

Are you still confused? Have questions? If so, please let us know in the comments. And feel free to suggest topics for future blog posts.

The post What’s the Diff: Programs, Processes, and Threads appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Security updates for Wednesday

Post Syndicated from ris original https://lwn.net/Articles/731167/rss

Security updates have been issued by CentOS (firefox, httpd, and java-1.7.0-openjdk), Fedora (cups-filters, potrace, and qpdf), Mageia (libsoup and mingw32-nsis), openSUSE (kernel), Oracle (httpd, kernel, spice, and subversion), Red Hat (httpd, java-1.7.1-ibm, and subversion), Scientific Linux (httpd), Slackware (xorg), SUSE (java-1_8_0-openjdk), and Ubuntu (firefox, linux, linux-aws, linux-gke, linux-raspi2, linux-snapdragon, linux-lts-xenial, postgresql-9.3, postgresql-9.5, postgresql-9.6, and ubufox).

Community Profile: David Pride

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/community-profile-david-pride/

This column is from The MagPi issue 55. You can download a PDF of the full issue for free, or subscribe to receive the print edition in your mailbox or the digital edition on your tablet. All proceeds from the print and digital editions help the Raspberry Pi Foundation achieve its charitable goals.

David Pride’s experiences in computer education came slightly later in life. He admits to not being a grade-A student: he left school with few qualifications, unable to pursue further education at university. There was, however, a teacher who instilled in him a passion for computers and coding which would stick with him indefinitely.

David Pride The MagPi Raspberry Pi Community Profile

David joined us at the St James’s Palace community celebration, mingling with the likes of the Duke of York, plus organisers of Jams and clubs, such as Grace and Femi

Welcome to the Community

Twenty years later, back in 2012, David heard of the Raspberry Pi – a soon-to-be-released “new little marvel” that he instantly fell for, head first. Despite a lack of knowledge in Linux and Python, he experimented and had fun. He found a Raspberry Jam and, with it, Pi enthusiasts like Mike Horne and Peter Onion. The projects on display at the Jam were enough to push David further into the Raspberry Pi rabbit hole and, after working his way through several Python books, he began to take steps into the world of formal higher education.

David Pride The MagPi Raspberry Pi Community Profile

David’s determination to access and complete further education in computing has earned him a three-year PhD studentship. Not bad for a “lousy student”

Back to School

With a Mooc qualification from Rice University under his belt, he continued to improve upon his self-taught knowledge, and was fortunate enough to be accepted to study for a master’s degree in Computer Science at the University of Hertfordshire. With a distinction for his final dissertation, David completed the course with an overall distinction for his MSc, and was recently awarded a fully funded PhD studentship with The Open University’s Knowledge Media Institute.

David Pride The MagPi Raspberry Pi Community Profile

Self-playing xylophones, Wiimote air drums, Lego sorters, Pi Wars robots, and more. David is continually hacking toys, giving them new Pi-powered life

Maker of things

The portfolio of projects that helped him to achieve his many educational successes has provided regular retweet material for the Raspberry Pi Twitter account, and we’ve highlighted his fun, imaginative work on this blog before. His builds have travelled to a range of Jams and made their way to the Raspberry Pi and Code Club stands at the Bett Show, as well as to our birthday celebrations.

David Pride The MagPi Raspberry Pi Community Profile

“Pi & Chips – with a little extra source”

His website, the pun-tastic Pi and Chips, is home to the majority of his work; David also links to YouTube videos and walk-throughs of his projects, and relates his experiences at various events. If you’ve followed any of the action across the Raspberry Pi social media channels – or indeed read any previous issues of The MagPi magazine – you’ll no doubt have seen a couple of David’s projects.

David Pride The MagPi Raspberry Pi Community Profile 4-Bot

Many readers will have come across the wonderful 4-Bot before, and it has even made an appearance alongside David in a recent Bloomberg interview. Considering the trillions of possible game positions, David made a compromise and, if you’re lucky, you may just be able to beat it

The 4-Bot, a robotic second player for the family game Connect Four, allows people to go head to head with a Pi-powered robotic arm. Using a Python imaging library, the 4-Bot splits the game grid into 42 squares, and recognises them as being red, yellow, or empty by reading the RGB value of the space. Using the minimax algorithm, 4-Bot is able to play each move within 25 seconds. Believe us when we say that it’s not as easy to beat as you’d hope. Then there’s his more recent air drum kit, which uses an old toy found at a car boot sale together with a Wiimote to make a functional air drum that showcases David’s toy-hacking abilities… and his complete lack of rhythm. He does fare much better on his homemade laser harp, though!

The post Community Profile: David Pride appeared first on Raspberry Pi.

Game of Thrones Episode “S07E06” Leaks Online Early

Post Syndicated from Ernesto original https://torrentfreak.com/game-of-thrones-episode-s07e06-leaks-online-early-170816/

Trouble continues for HBO as another episode of the popular Game of Thrones series has just leaked online, days ahead of the official premiere.

Copies of the sixth episode of the current season, titled ‘Death is the Enemy,’ are currently circulating on various streaming portals, direct download, and torrent sites.

The first copy only just appeared on the Pirate Bay, but others were shared elsewhere earlier. One of the leaked videos is 64 minutes long and of high quality, and there are also versions that consist of two separate parts.

Early on, the two parts were circulating on the video streaming site Dailymotion, but these were swiftly removed.

At the moment it’s still unclear how the leak came about but some suggest that it was leaked by HBO itself in Spain. TorrentFreak has not been able to confirm this, but there are no visible watermarks that point elsewhere.

Game of Thrones “S07E06” leak screenshot

This isn’t the first time that a Game of Thrones episode has leaked online early. Two years ago the same happened with the first four episodes of season 5. Nonetheless, that season still broke previous viewership records.

Two weeks ago the fourth episode of the current season was also pirated before its official release. This leak, which carried a prominent “Star India Pvt Ltd” watermark, triggered a lot of interest from impatient Game of Thrones fans as well.

Earlier this week, news broke that four men had been arrested in connection with the breach, which is still being investigated. The arrested men all worked for the local media processing company Prime Focus Technologies, where the leak reportedly originated.

The current leak is not in any way related to the hack on HBO’s system, which occurred earlier and revealed several preliminary Game of Thrones scripts.

This hack has also resulted in leaks of various high profile shows, including the upcoming ninth season of ‘Curb Your Enthusiasm.’ Initially, these were hard to find online, but they are now widely available on the usual pirate sites.

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

AWS Partner Webinar Series – August 2017

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/aws-partner-webinar-series-august-2017/

We love bringing our customers helpful information and we have another cool series we are excited to tell you about. The AWS Partner Webinar Series is a selection of live and recorded presentations covering a broad range of topics at varying technical levels and scale. A little different from our AWS Online TechTalks, each AWS Partner Webinar is hosted by an AWS solutions architect and an AWS Competency Partner who has successfully helped customers evaluate and implement the tools, techniques, and technologies of AWS.

Check out this month’s webinars and let us know which ones you found the most helpful! All schedule times are shown in the Pacific Time (PDT) time zone.

Security Webinars

Sophos
Seeing More Clearly: ATLO Software Secures Online Training Solutions for Correctional Facilities with SophosUTM on AWS Link.
August 17th, 2017 | 10:00 AM PDT

F5
F5 on AWS: How MailControl Improved their Application Visibility and Security
August 23, 2017 | 10:00 AM PDT

Big Data Webinars

Tableau, Matillion, 47Lining, NorthBay
Unlock Insights and Reduce Costs by Modernizing Your Data Warehouse on AWS
August 22, 2017 | 10:00 AM PDT

Storage Webinars

StorReduce
How Globe Telecom does Primary Backups via StorReduce to the AWS Cloud
August 29, 2017 | 8:00 AM PDT

Commvault
Moving Forward Faster: How Monash University Automated Data Movement for 3500 Virtual Machines to AWS with Commvault
August 29, 2017 | 1:00 PM PDT

Dell EMC
Moving Forward Faster: Protect Your Workloads on AWS With Increased Scale and Performance
August 30, 2017 | 11:00 AM PDT

Druva
How Hatco Protects Against Ransomware with Druva on AWS
September 13, 2017 | 10:00 AM PDT

Spinrilla Refuses to Share Its Source Code With the RIAA

Post Syndicated from Ernesto original https://torrentfreak.com/spinrilla-refuses-to-share-its-source-code-with-the-riaa-170815/

Earlier this year, a group of well-known labels targeted Spinrilla, a popular hip-hop mixtape site and accompanying app with millions of users.

The coalition of record labels including Sony Music, Warner Bros. Records, and Universal Music Group, filed a lawsuit accusing the service of alleged copyright infringements.

Both sides have started the discovery process and recently asked the court to rule on several unresolved matters. The parties begin with their statements of facts, clearly from opposite angles.

The RIAA remains confident that the mixtape site is ripping off music creators and wants its operators to be held accountable.

“Since Spinrilla launched, Defendants have facilitated millions of unauthorized downloads and streams of thousands of Plaintiffs’ sound recordings without Plaintiffs’ permission,” RIAA writes, complaining about “rampant” infringement on the site.

However, Spinrilla itself believes that the claims are overblown. The company points out that the RIAA’s complaint only lists a tiny fraction of all the songs uploaded by its users. These somehow slipped through its Audible Magic anti-piracy filter.

Where the RIAA paints a picture of rampant copyright infringement, the mixtape site stresses that the record labels are complaining about less than 0.001% of all the tracks they ever published.

“From 2013 to the present, Spinrilla users have uploaded about 1 million songs to Spinrilla’s servers and Spinrilla published about 850,000 of those. Plaintiffs are complaining that 210 of those songs are owned by them and published on Spinrilla without permission,” Spinrilla’s lawyers write.

“That means that Plaintiffs make no claim to 99.9998% of the songs on Spinrilla. Plaintiffs’ shouting of ‘rampant infringement on Spinrilla’, an accusation that Spinrilla was designed to allow easy and open access to infringing material, and assertion that ‘Defendants have facilitated millions of unauthorized downloads’ of those 210 songs is untrue – it is nothing more than a wish and a dream.”

The company reiterates that it’s a platform for independent musicians and that it doesn’t want to feature the Eminem’s and Bieber’s of this world, especially not without permission.

As for the discovery process, there are still several outstanding issues they need the Court’s advice on. Spinrilla has thus far produced 12,000 pages of documents and answered all RIAA interrogatories, but refuses to hand over certain information, including its source code.

According to Spinrilla, there is no reason for the RIAA to have access to its “crown jewel.”

“The source code is the crown jewel of any software based business, including Spinrilla. Even worse, Plaintiffs want an ‘executable’ version of Spinrilla’s source code, which would literally enable them to replicate Spinrilla’s entire website. Any Plaintiff could, in hours, delete all references to ‘Spinrilla,’ add its own brand and launch Spinrilla’s exact website.

“If we sued YouTube for hosting 210 infringing videos, would I be entitled to the source code for YouTube? There is simply no justification for Spinrilla sharing its source code with Plaintiffs,” Spinrilla adds.

The RIAA, on the other hand, argues that the source code will provide insight into several critical issues, including Spinrilla’s knowledge about infringing activity and its ability to terminate repeat copyright infringers.

In addition to the source code, the RIAA has also requested detailed information about the site’s users, including their download and streaming history. This request is too broad, the mixtape site argues, and has offered to provide information on the uploaders of the 210 infringing tracks instead.

It’s clear that the RIAA and Spinrilla disagree on various fronts and it will be up to the court to decide what information must be handed over. So far, however, the language used clearly shows that both parties are far from reaching some kind of compromise.

—

The first joint discovery statement is available in full here (pdf).

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

Wanted: Front End Developer

Post Syndicated from Yev original https://www.backblaze.com/blog/wanted-front-end-developer/

Want to work at a company that helps customers in over 150 countries around the world protect the memories they hold dear? Do you want to challenge yourself with a business that serves consumers, SMBs, Enterprise, and developers? If all that sounds interesting, you might be interested to know that Backblaze is looking for a Front End Developer​!

Backblaze is a 10 year old company. Providing great customer experiences is the “secret sauce” that enables us to successfully compete against some of technology’s giants. We’ll finish the year at ~$20MM ARR and are a profitable business. This is an opportunity to have your work shine at scale in one of the fastest growing verticals in tech – Cloud Storage.

You will utilize HTML, ReactJS, CSS and jQuery to develop intuitive, elegant user experiences. As a member of our Front End Dev team, you will work closely with our web development, software design, and marketing teams.

On a day to day basis, you must be able to convert image mockups to HTML or ReactJS – There’s some production work that needs to get done. But you will also be responsible for helping build out new features, rethink old processes, and enabling third party systems to empower our marketing/sales/ and support teams.

Our Front End Developer must be proficient in:

  • HTML, ReactJS
  • UTF-8, Java Properties, and Localized HTML (Backblaze runs in 11 languages!)
  • JavaScript, CSS, Ajax
  • jQuery, Bootstrap
  • JSON, XML
  • Understanding of cross-browser compatibility issues and ways to work around them
  • Basic SEO principles and ensuring that applications will adhere to them
  • Learning about third party marketing and sales tools through reading documentation. Our systems include Google Tag Manager, Google Analytics, Salesforce, and Hubspot

Struts, Java, JSP, Servlet and Apache Tomcat are a plus, but not required.

We’re looking for someone that is:

  • Passionate about building friendly, easy to use Interfaces and APIs.
  • Likes to work closely with other engineers, support, and marketing to help customers.
  • Is comfortable working independently on a mutually agreed upon prioritization queue (we don’t micromanage, we do make sure tasks are reasonably defined and scoped).
  • Diligent with quality control. Backblaze prides itself on giving our team autonomy to get work done, do the right thing for our customers, and keep a pace that is sustainable over the long run. As such, we expect everyone that checks in code that is stable. We also have a small QA team that operates as a secondary check when needed.

Backblaze Employees Have:

  • Good attitude and willingness to do whatever it takes to get the job done
  • Strong desire to work for a small fast, paced company
  • Desire to learn and adapt to rapidly changing technologies and work environment
  • Comfort with well behaved pets in the office

This position is located in San Mateo, California. Regular attendance in the office is expected. Backblaze is an Equal Opportunity Employer and we offer competitive salary and benefits, including our no policy vacation policy.

If this sounds like you
Send an email to [email protected] with:

  1. Front End Dev​ in the subject line
  2. Your resume attached
  3. An overview of your relevant experience

The post Wanted: Front End Developer appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Roku Gets Tough on Pirate Channels, Warns Users

Post Syndicated from Ernesto original https://torrentfreak.com/roku-gets-tough-on-pirate-channels-warns-users-170815/

In recent years it has become much easier to stream movies and TV-shows over the Internet.

Legal services such as Netflix and HBO are flourishing, but there’s also a darker side to this streaming epidemic. Millions of people are streaming from unauthorized sources, often paired with perfectly legal streaming platforms and devices.

Hollywood insiders have dubbed this trend “Piracy 3.0” are actively working with stakeholders to address the threat. One of the companies rightsholders are working with is Roku, known for its easy-to-use media players.

Earlier this year Roku was harshly confronted with this new piracy crackdown when a Mexican court ordered local retailers to take its media player off the shelves. While this legal battle isn’t over yet, it was clear to Roku that misuse of its platform wasn’t without consequences.

While Roku never permitted any infringing content, it appears that the company has recently made some adjustments to better deal with the problem, or at least clarify its stance.

Pirate content generally doesn’t show up in the official Roku Channel Store but is directly loaded onto the device through third-party “private” channels. A few weeks ago, Roku renamed these “private” channels to “non-certified” channels, while making it very clear that copyright infringement is not allowed.

A “WARNING!” message that pops up during the installation of these third-party channels stresses that Roku has no control over the content. In addition, the company notes that these channels may be removed if it links to copyright infringing content.

Roku Warning

“By continuing, you acknowledge you are accessing a non-certified channel that may include content that is offensive or inappropriate for some audiences,” Roku’s warning reads.

“Moreover, if Roku determines that this channel violates copyright, contains illegal content, or otherwise violates Roku’s terms and conditions, then ROKU MAY REMOVE THIS CHANNEL WITHOUT PRIOR NOTICE.”

TorrentFreak reached out to Roku to find out how they plan to enforce this policy, but we have yet to hear back. According to Cord Cutters News, several piracy channels have already been removed recently, with other developers opting to leave the platform.

Roku’s General Counsel Steve Kay previously informed us that the company is taking the piracy problem seriously. Together with various stakeholders, they are working hard to address the problem.

“We actively work to prevent third-parties from using our platform to distribute copyright infringing content. Moreover, we have been actively working with other industry stakeholders on a wide range of anti-piracy initiatives,” Kay said.

Roku is not the only platform dealing with the piracy epidemic, the popular media player software Kodi is in the same boat. Kodi has also taken an active anti-piracy stance but they’re not banning any add-ons. They believe it would be pointless due to the open source nature of their software.

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

GNOME turns 20

Post Syndicated from ris original https://lwn.net/Articles/731036/rss

The GNOME project was founded by Miguel de Icaza and Federico Mena Quintero
on August 15, 1997, so today the project celebrates
its 20th birthday. “There have been 33 stable releases since the initial release of GNOME 1.0 in 1999. The latest stable release, GNOME 3.24 “Portland,” was well-received. “Portland” included exciting new features like the GNOME Recipes application and Night Light, which helps users avoid eyestrain. The upcoming version of GNOME 3.26 “Manchester,” is scheduled for release in September of this year. With over 6,000 contributors, and 8 million lines of code, the GNOME Project continues to thrive in its twentieth year.”

OK Google, be aesthetically pleasing

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/aesthetically-pleasing-ok-google/

Maker Andrew Jones took a Raspberry Pi and the Google Assistant SDK and created a gorgeous-looking, and highly functional, alternative to store-bought smart speakers.

Raspberry Pi Google AI Assistant

In this video I get an “Ok Google” voice activated AI assistant running on a raspberry pi. I also hand make a nice wooden box for it to live in.

OK Google, what are you?

Google Assistant is software of the same ilk as Amazon’s Alexa, Apple’s Siri and Microsoft’s Cortana. It’s a virtual assistant that allows you to request information, play audio, and control smart home devices via voice commands.

Infinite Looping Siri, Alexa and Google Home

One can barely see the iPhone’s screen. That’s because I have a privacy protection screen. Sorry, did not check the camera angle. Learn how to create your own loop, why we put Cortana out of the loop, and how to train Siri to an artificial voice: https://www.danrl.com/2016/12/01/looping-ais-siri-alexa-google-home.html

You probably have a digital assistant on your mobile phone, and if you go to the home of someone even mildly tech-savvy, you may see a device awaiting commands via a wake word such the device’s name or, for the Google Assistant, the phrase “OK, Google”.

Homebrew versions

Understanding the maker need to ‘put tech into stuff’ and upgrade everyday objects into everyday objects 2.0, the creators of these virtual assistants have allowed access for developers to run their software on devices such as the Raspberry Pi. This means that your common-or-garden homemade robot can now be controlled via voice, and your shed-built home automation system can have easy-to-use internet connectivity via a reliable, multi-device platform.

Andrew’s Google Assistant build

Andrew gives a peerless explanation of how the Google Assistant works:

There’s Google’s Cloud. You log into Google’s Cloud and you do a bunch of cloud configuration cloud stuff. And then on the Raspberry Pi you install some Python software and you do a bunch of configuration. And then the cloud and the Pi talk the clouds kitten rainbow protocol and then you get a Google AI assistant.

It all makes perfect sense. Though for more extra detail, you could always head directly to Google.

Andrew Jones Raspberry Pi OK Google Assistant

I couldn’t have explained it better myself

Andrew decided to take his Google Assistant-enabled Raspberry Pi and create a new body for it. One that was more aesthetically pleasing than the standard Pi-inna-box. After wiring his build and cannibalising some speakers and a microphone, he created a sleek, wooden body that would sit quite comfortably in any Bang & Olufsen shop window.

Find the entire build tutorial on Instructables.

Make your own

It’s more straightforward than Andrew’s explanation suggests, we promise! And with an array of useful resources online, you should be able to incorporate your choice of virtual assistants into your build.

There’s The Raspberry Pi Guy’s tutorial on setting up Amazon Alexa on the Raspberry Pi. If you’re looking to use Siri on your Pi, YouTube has a plethora of tutorials waiting for you. And lastly, check out Microsoft’s site for using Cortana on the Pi!

If you’re looking for more information on Google Assistant, check out issue 57 of The MagPi Magazine, free to download as a PDF. The print edition of this issue came with a free AIY Projects Voice Kit, and you can sign up for The MagPi newsletter to be the first to know about the kit’s availability for purchase.

The post OK Google, be aesthetically pleasing appeared first on Raspberry Pi.

BREIN is Taking Infamous ‘Piracy’ Hosting Provider Ecatel to Court

Post Syndicated from Andy original https://torrentfreak.com/brein-is-taking-infamous-piracy-hosting-provider-ecatel-to-court-170815/

A regular website can be easily hosted in most countries of the world but when the nature of the project begins to step on toes, opportunities begin to reduce. Openly hosting The Pirate Bay, for example, is something few providers want to get involved with.

There are, however, providers out there who specialize in hosting services that others won’t touch. They develop a reputation of turning a blind eye to their customers’ activities, only reacting when a crisis looms on the horizon. Despite the problems, there are a few that are surprisingly resilient.

One such host is Netherlands-based Ecatel, which has hit the headlines many times over the years for allegedly having customers involved in warez, torrents, and streaming, not to mention spam and malware. For hosting the former group, it’s now in the crosshairs of Dutch anti-piracy group BREIN.

According to an application for a witness hearing filed with The Court of the Hague by BREIN, Ecatel has repeatedly hosted websites dealing in infringing content over recent years. While this is nothing particularly out of the ordinary, BREIN claims that complaints filed against the sites were dealt with slowly by Ecatel or not at all.

Ecatel Ltd is a company incorporated in the UK with servers in the Netherlands but since 2015, another hosting company called Novogara has appeared in tandem. Court documents suggest that Novogara is associated with Ecatel, something that was confirmed early 2016 in an email sent out by Ecatel itself.

“We’d like to inform you that all services of Ecatel Ltd are taken over by a new brand called Novogara Ltd with immediate effect. The take-over includes Ecatel and all her subsidiaries,” the email read.

Muddying the waters a little more, in 2015 Ecatel’s IP addresses were apparently taken over by Quasi Networks Ltd, a Seychelles-based company whose business is described locally as being conducted entirely overseas.

“Stichting BREIN has found several websites in the network of Quasi Networks with obviously infringing content. Quasi Networks, however, does not respond structurally to requests for closing those websites. This involves unlawful acts against the parties associated with the BREIN Foundation,” a ruling from the Court reads.

As a result, BREIN wants a witness hearing with three defendants connected to the Ecatel/Novgara/Quasi group of companies in order to establish the relationship between the businesses, where their servers are, and who is behind Quasi Networks.

“Stichting BREIN is interested in this information in order to be able to judge who it can appeal to and whether it is useful to start a legal procedure,” the Court adds.

Two of the defendants failed to lodge a defense against BREIN’s application but one objected to the request for a hearing. He said that since Quasi Networks, Ecatel and Novogara are all incorporated outside the Netherlands, a trial must also be conducted abroad and therefore a Dutch judge would not have jurisdiction.

He also argued that BREIN would use the witness hearing as a “fishing expedition” in order to gather information it currently does not have, in order to formulate some kind of case against the defendants, in one way or another.

In a decision published this week, The Court of the Hague rejected that argument, noting that the basis for the claim is copyright infringement through Netherlands-hosted websites. Furthermore, the majority of the witnesses are resident in the district of The Hague. It also underlined the importance of a hearing.

“The request for holding a preliminary witness hearing opens an independent petition procedure, which does not address the eligibility of any claim that may be lodged. An investigation must be made by the judge who has to deal with and decide the main case – if it comes.

“The court points out that a preliminary witness hearing is now (partly) necessary to clarify whether and to what extent a claim has any chance of success,” the decision reads.

According to documents published by Companies House in the UK, Ecatel Ltd ceased to exist this morning, having been dissolved at the request of its directors.

The hearing of the witnesses is set to take place on Tuesday, September 26, 2017 at 9.30 in the Palace of Justice at Prince Claus 60 in The Hague.

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

AWS Summit New York – Summary of Announcements

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-summit-new-york-summary-of-announcements/

Whew – what a week! Tara, Randall, Ana, and I have been working around the clock to create blog posts for the announcements that we made at the AWS Summit in New York. Here’s a summary to help you to get started:

Amazon Macie – This new service helps you to discover, classify, and secure content at scale. Powered by machine learning and making use of Natural Language Processing (NLP), Macie looks for patterns and alerts you to suspicious behavior, and can help you with governance, compliance, and auditing. You can read Tara’s post to see how to put Macie to work; you select the buckets of interest, customize the classification settings, and review the results in the Macie Dashboard.

AWS Glue – Randall’s post (with deluxe animated GIFs) introduces you to this new extract, transform, and load (ETL) service. Glue is serverless and fully managed, As you can see from the post, Glue crawls your data, infers schemas, and generates ETL scripts in Python. You define jobs that move data from place to place, with a wide selection of transforms, each expressed as code and stored in human-readable form. Glue uses Development Endpoints and notebooks to provide you with a testing environment for the scripts you build. We also announced that Amazon Athena now integrates with Amazon Glue, as does Apache Spark and Hive on Amazon EMR.

AWS Migration Hub – This new service will help you to migrate your application portfolio to AWS. My post outlines the major steps and shows you how the Migration Hub accelerates, tracks,and simplifies your migration effort. You can begin with a discovery step, or you can jump right in and migrate directly. Migration Hub integrates with tools from our migration partners and builds upon the Server Migration Service and the Database Migration Service.

CloudHSM Update – We made a major upgrade to AWS CloudHSM, making the benefits of hardware-based key management available to a wider audience. The service is offered on a pay-as-you-go basis, and is fully managed. It is open and standards compliant, with support for multiple APIs, programming languages, and cryptography extensions. CloudHSM is an integral part of AWS and can be accessed from the AWS Management Console, AWS Command Line Interface (CLI), and through API calls. Read my post to learn more and to see how to set up a CloudHSM cluster.

Managed Rules to Secure S3 Buckets – We added two new rules to AWS Config that will help you to secure your S3 buckets. The s3-bucket-public-write-prohibited rule identifies buckets that have public write access and the s3-bucket-public-read-prohibited rule identifies buckets that have global read access. As I noted in my post, you can run these rules in response to configuration changes or on a schedule. The rules make use of some leading-edge constraint solving techniques, as part of a larger effort to use automated formal reasoning about AWS.

CloudTrail for All Customers – Tara’s post revealed that AWS CloudTrail is now available and enabled by default for all AWS customers. As a bonus, Tara reviewed the principal benefits of CloudTrail and showed you how to review your event history and to deep-dive on a single event. She also showed you how to create a second trail, for use with CloudWatch CloudWatch Events.

Encryption of Data at Rest for EFS – When you create a new file system, you now have the option to select a key that will be used to encrypt the contents of the files on the file system. The encryption is done using an industry-standard AES-256 algorithm. My post shows you how to select a key and to verify that it is being used.

Watch the Keynote
My colleagues Adrian Cockcroft and Matt Wood talked about these services and others on the stage, and also invited some AWS customers to share their stories. Here’s the video:

— Jeff;

 

Game of Thrones Pirates Arrested For Leaking Episode Early

Post Syndicated from Andy original https://torrentfreak.com/game-of-thrones-pirates-arrested-for-leaking-episode-early-170814/

Over the past several years, Game of Thrones has become synonymous with fantastic drama and story telling on the one hand, and Internet piracy on the other. It’s the most pirated TV show in history, hands down.

With the new season well underway, another GoT drama began to unfold early August when the then-unaired episode “The Spoils of War” began to circulate on various file-sharing and streaming sites. The leak only trumped the official release by a few days, but that didn’t stop people downloading in droves.

As previously reported, the leaked episode stated that it was “For Internal Viewing Only” at the top of the screen and on the bottom right sported a “Star India Pvt Ltd” watermark. The company commented shortly after.

“We take this breach very seriously and have immediately initiated forensic investigations at our and the technology partner’s end to swiftly determine the cause. This is a grave issue and we are taking appropriate legal remedial action,” a spokesperson said.

Now, just ten days later, that investigation has already netted its first victims. Four people have reportedly been arrested in India for leaking the episode before it aired.

“We investigated the case and have arrested four individuals for unauthorized publication of the fourth episode from season seven,” Deputy Commissioner of Police Akbar Pathan told AFP.

The report indicates that a complaint was filed by a Mumbai-based company that was responsible for storing and processing the TV episodes for an app. It has been named locally as Prime Focus Technologies, which markets itself as a Netflix “Preferred Vendor”.

It’s claimed that at least some of the men had access to login credentials for Game of Thrones episodes which were then abused for the purposes of leaking.

Local media identified the men as Bhaskar Joshi, Alok Sharma and Abhishek Ghadiyal, who were employed by Prime Focus, and Mohamad Suhail, a former employee, who was responsible for leaking the episode onto the Internet.

All of the men were based in Bangalore and were interrogated “throughout the night” at their workplace on August 11. Star India welcomed the arrests and thanked the authorities for their swift action.

“We are deeply grateful to the police for their swift and prompt action. We believe that valuable intellectual property is a critical part of the development of the creative industry and strict enforcement of the law is essential to protecting it,” the company said in a statement.

“We at Star India and Novi Digital Entertainment Private Limited stand committed and ready to help the law enforcement agencies with any technical assistance and help they may require in taking the investigation to its logical conclusion.”

The men will be held in custody until August 21 while investigations continue.

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

Upsert into Amazon Redshift using AWS Glue and SneaQL

Post Syndicated from Jeremy Winters original https://aws.amazon.com/blogs/big-data/upsert-into-amazon-redshift-using-aws-glue-and-sneaql/

This is a guest post by Jeremy Winters and Ritu Mishra, Solution Architects at Full 360. In their own words, “Full 360 is a cloud first, cloud native integrator, and true believers in the cloud since inception in 2007, our focus has been on helping customers with their journey into the cloud. Our practice areas – Big Data and Warehousing, Application Modernization, and Cloud Ops/Strategy – represent deep, but focused expertise.”

AWS Glue is a fully managed ETL (extract, transform, and load) service that makes it simple and cost-effective to categorize your data, clean it, enrich it, and move it reliably between various data stores.  As a company who has been building data warehouse solutions in the cloud for 10 years, we at Full 360 were interested to see how we can leverage AWS Glue in customer solutions. This post details our experience and lessons learned from using AWS Glue for an Amazon Redshift data integration use case.

UI-based ETL Tools

We have been anticipating the release of AWS Glue since it was announced at re:Invent 2016. Many of our customers are looking for an easy to use, UI-based tooling to manage their data transformation pipeline. However in our experience, the complexity of any production pipeline tends to be difficult to unwind, regardless of the technology used to create them. At Full 360, we build cloud-native, script-based applications deployed in containers to handle data integration. We think script-based transformation provides a balance of robustness and flexibility necessary to handle any data problem that comes our way.

AWS Glue caters both to developers who prefer writing scripts and those who want UI-based interactions. It is possible to initially create jobs using the UI, by selecting data source and target. Under the hood, AWS Glue auto-generates the Python code for you, which can be edited if needed, though this isn’t necessary for the majority of use cases.

Of course, you don’t have to rely on the UI at all. You can simply write your own Python scripts, store them in Amazon S3 with any dependent libraries, and import them into AWS Glue. AWS Glue also supports IDE integration with tools such as PyCharm, and interestingly enough, Zeppelin notebooks! These integrations are targeted toward developers who prefer writing Python themselves and want a cleaner dev/test cycle.

Developers who are already in the business of scripting ETL will be excited by the ability to easily deploy Python scripts with AWS Glue, using existing workflows for source control and CI/CD, and have them deployed and executed in a fully managed manner by AWS.  The UI experience of AWS Glue works well, but it is good to know that the tool accommodates those who prefer traditional coding. You can also dig into complex edge cases for data handling where the UI doesn’t cut it.

Serverless!

When AWS Lambda was released, we were excited for its potential to host our ETL processes. With Lambda, you are limited to the five-minute maximum for function execution. We resorted to running Docker containers and Amazon ECS to orchestrate many of our customers long running tasks. With this approach, we are still required to manage the underlying infrastructure.

After a closer look at AWS Glue, we realize that it is a full serverless PySpark runtime, accompanied by an Apache Hive metastore compatible catalog-as-a-service. This means that you are not just running a script on a single core, but instead you have the full power of a multi-worker Spark environment available. If you’re not familiar with the Spark framework, it introduces a new paradigm that allows for the processing of distributed, in-memory datasets. Spark has many uses, from data transformation to machine learning.

In AWS Glue, you use PySpark dataframes to transform data before reaching your database. Dataframes manage data in a way similar to relational databases, so the methods are likely to be familiar to most SQL users. Additionally, you can use SQL in your PySpark code to manipulate the data.

AWS Glue also simplifies the management of the runtime environment by providing you with a DPU setting, which allows you to dial up or down the amount of compute resources used to run your job. One DPU is equivalent to 4 vCPU, 16 GB Mem.

Common use case

We can see that most customers would leverage AWS Glue to load one or many files from S3 into Amazon Redshift. To accelerate this process, you can use the crawler, an AWS console-based utility, to discover the schema of your data and store it in the AWS Glue Data Catalog, whether your data sits in a file or a database. We were able to discover the schemas of our source file and target table, then have AWS Glue construct and execute the ETL job. It worked! We successfully loaded the beer drinkers’ dataset, JSON files used in our advanced tuning class, into Amazon Redshift!

Our use case

For our use case, we integrated AWS Glue and Amazon Redshift Spectrum with an open-source project that we initiated called SneaQL. SneaQL is an open source, containerized framework for sneaking interactivity into static SQL. SneaQL provides an extension to ANSI SQL with command tags to provide functionality such as loops, variables, conditional execution, and error handling to your scripts. It allows you to manage your scripts in an AWS CodeCommit Git repo, which then gets deployed and executed in a container.

We use SneaQL for complex data integrations, usually with an ELT model, where the data is loaded into the database, then transformed into fact tables using parameterized SQL. SneaQL enables advanced use cases like partial upsert aggregation of data, where multiple data sources can merge into the same fact table.

We think AWS Glue, Redshift Spectrum, and SneaQL offer a compelling way to build a data lake in S3, with all of your metadata accessible through a variety of tools such as Hive, Presto, Spark, and Redshift Spectrum). Build your aggregation table in Amazon Redshift to drive your dashboards or other high-performance analytics.

In the video below, you see a demonstration of using AWS Glue to convert JSON documents into Parquet for partitioned storage in an S3 data lake. We then access the data from S3 into Amazon Redshift by way of Redshift Spectrum. Nearing the end of the AWS Glue job, we then call AWS boto3 to trigger an Amazon ECS SneaQL task to perform an upsert of the data into our fact table. All the sample artifacts needed for this demonstration are available in the Full360/Sneaql Github repository.

In this scenario, AWS Glue manipulates data outside of a data warehouse and loads it to Amazon Redshift, and SneaQL manipulates data inside Amazon Redshift.

We think that they are a good compliment to each other when doing complex integrations.

Our pipeline works as follows:

  • New files arrive in S3 in JSON format.
  • AWS Glue converts the JSON files in Parquet format, stored in another S3 bucket.
  • At the end of the AWS Glue script, the AWS SDK for Python (Boto) is used to trigger the Amazon ECS task that runs SneaQL.
  • SneaQL container pulls the secrets file from S3 then decrypts it with biscuit or AWS KMS.
  • SneaQL clones the appropriate branch from an AWS CodeCommit Git repo.
  • Data in Amazon Redshift is transformed by SneaQL statements stored in the repo.

The AWS Glue team also supports conditional triggers that would allow us to split the jobs, and make one dependent upon the other.

Final thoughts

Although, as a general rule, we believe in managing schema definitions in source control, we definitely think that the crawler feature has some attractive perks. Besides being handy for ad-hoc jobs, it is also partition-aware. This means that you can perform data movements without worrying about which data has been processed already, which is one less thing for you to manage. We’re interested to see the design patterns that emerge around the use of the crawler.

The freedom of PySpark also allows us to implement advanced use cases. While the boto3 library is available to all AWS Glue jobs, it is also possible to include any libraries and additional JAR files along with your Python script.

We think many customers will find AWS Glue valuable. Those who prefer to code their data processing pipeline will be quick to realize how powerful it is, especially for solving complex data cleansing problems. The learning curve for PySpark is real, so we suggest looking into dataframes as a good entry point. In the long run, the fact that AWS Glue is serverless makes the effort more than worth it.

If you have questions or suggestions, please comment below.

 

Launch – Hello Amazon Macie: Automatically Discover, Classify, and Secure Content at Scale

Post Syndicated from Tara Walker original https://aws.amazon.com/blogs/aws/launch-amazon-macie-securing-your-s3-buckets/

When Jeff and I heard about this service, we both were curious on the meaning of the name Macie. Of course, Jeff being a great researcher looked up the name Macie and found that the name Macie has two meanings. It has both French and English (UK) based origin, it is typically a girl name, has various meanings. The first meaning of Macie that was found, said that that name meant “weapon”.  The second meaning noted the name was representative of a person that is bold, sporty, and sweet. In a way, these definitions are appropriate, as today I am happy to announce that we are launching  Amazon Macie, a new security service that uses machine learning to help identify and protect sensitive data stored in AWS from breaches, data leaks, and unauthorized access with Amazon Simple Storage Service (S3) being the initial data store. Therefore, I can imagine that Amazon Macie could be described as a bold, weapon for AWS customers providing a sweet service with a sporty user interface that helps to protects against malicious access of your data at rest. Whew, that was a mouthful, but I unbelievably got all the Macie descriptions out in a single sentence! Nevertheless, I am a thrilled to share with you the power of the new Amazon Macie service.

Amazon Macie is a service powered by machine learning that can automatically discover and classify your data stored in Amazon S3. But Macie doesn’t stop there, once your data has been classified by Macie, it assigns each data item a business value, and then continuously monitors the data in order to detect any suspicious activity based upon access patterns. Key features of the Macie service include:

  • Data Security Automation: analyzes, classifies, and processes data to understand the historical patterns, user authentications to data, data access locations, and times of access.
  • Data Security & Monitoring: actively monitors usage log data for anomaly detected along with automatic resolution of reported issues through CloudWatch Events and Lambda
  • Data Visibility for Proactive Loss prevention: Provides management visibility into details of storage data while providing immediate protection without the need for manual customer input
  • Data Research and Reporting: allows administrative configuration for reporting and alert management requirements

How does Amazon Macie accomplish this you ask? 

Using machine learning algorithms for natural language processing (NLP), Macie can automate the classification of data in your S3 buckets. In addition, Amazon Macie takes advantage of predictive analytics algorithms enabling data access patterns to be dynamically analyzed. Learnings are then used to inform and to alert you on possible suspicious behavior. Macie also runs an engine specifically to detect common sources of personally identifiable information (PII), or sensitive personal information (SP).  Macie takes advantage of AWS CloudTrail and continuously checks Cloudtrail events for PUT requests in S3 buckets and automatically classify new objects in almost real time.

While Macie is a powerful tool to use for security and data protection in the AWS cloud, it also can aid you with governance, compliance requirements, and/or audit standards.  Many of you may already be aware of the EU’s most stringent privacy regulation to date – The General Protection Data Regulation (GDPR), which becomes enforceable on May 25, 2018. As Amazon Macie recognizes personally identifiable information (PII) and provides customers with dashboards and alerts, it will enable customers to comply with GDPR regulations around encryption and pseudonymization of data. When combined with Lambda queries, Macie becomes a powerful tool to help remediate GDPR concerns.

Tour of the Amazon Macie Service

Let’s look a tour of the service and look at Amazon Macie up close and personal.

First, I will log onto the Macie console and start the process of setting up Macie so that I can start to my data classification and protection by clicking the Get Started button.


As you can see, to enable the Amazon Macie service, I must have the appropriate IAM roles created for the service, and additionally I will need to have AWS CloudTrail enabled in my account.

I will create these roles and turn on the AWS CloudTrail service in my account. To make things easier for you to setup Macie, you can take advantage of sample template for CloudFormation provided in the Macie User Guide that will set up required IAM roles and policies for you, you then would only need to setup a trail as noted in the CloudTrail documentation.

If you have multiple AWS accounts, you should note that the account you use to enable the Macie service will be noted as the master account, you can integrate other accounts with the Macie service but they will have the member account designation. Users from member accounts will need to use an IAM role to federate access to the master account in order access the Macie console.

Now that my IAM roles are created and CloudTrail is enabled, I will click the Enable Macie button to start Macie’s data monitoring and protection.


Once Macie is finished starting the service in your account, you will be brought to the service main screen and any existing alerts in your account will be presented to you. Since I have just started the service, I currently have no existing alerts at this time.


Considering we are doing a tour of the Macie service, I will now integrate some of my S3 buckets with Macie. However, you do not have to specify any S3 buckets for Macie to start monitoring since the service already uses the AWS CloudTrail Management API analyze and process information. With this tour of Macie, I have decided to monitor some object level API events in from certain buckets in CloudTrail.

In order to integrate with S3, I will go to the Integrations tab of the Macie console.  Once on the Integrations tab, I will see two options: Accounts and Services. The Account option is used to integrate member accounts with Macie and to set your data retention policy. Since I want to integrate specific S3 buckets with Macie, I’ll click the Services option go to the Services tab.


When I integrate Macie with the S3 service, a trail and a S3 bucket will be created to store logs about S3 data events. To get started, I will use the Select an account drop down to choose an account.  Once my account is selected, the services available for integration are presented. I’ll select the Amazon S3 service by clicking the Add button.

Now I can select the buckets that I want Macie to analyze, selecting the Review and Save button takes me to a screen which I confirm that I desire object level logging by clicking Save button.

4
Next, on our Macie tour, let’s look at how we can customize data classification with Macie.

As we discussed, Macie will automatically monitor and classify your data. Once Macie identifies your data it will classify your data objects by file and content type. Macie will also use a support vector machine (SVM) classifier to classify the content within S3 objects in addition to the metadata of the file. In deep learning/machine learning fields of study, support vector machines are supervised learning models, which have learning algorithms used for classification and regression analysis of data. Macie trained the SVM classifier by using a data of varying content types optimized to support accurate detection of data content even including the source code you may write.

Macie will assign only one content type per data object or file, however, you have the ability to enable or disable content type and file extensions in order to include or exclude them from the Macie service classifying these objects. Once Macie classifies the data, it will assign risk level of the object between 1 and 10 with 10 being the highest risk and 1 being the lowest data risk level.

To customize the classification of our data with Macie, I’ll go to the Settings Tab. I am now presented with the choices available to enable or disable the Macie classifications settings.


For an example during our tour of Macie, I will choose File extension. When presented with the list of file extensions that Macie tracks and uses for classifications.

As a test, I’ll edit the apk file extension for Android application install file, and disable monitoring of this file by selecting No – disabled from the dropdown and clicking the Save button. Of course, later I will turn this back on since I want to keep my entire collection of data files safe including my Android development binaries.


One last thing I want to note about data classification using Macie is that the service provides visibility in how you data object are being classified and highlights data assets that you have stored regarding how critical or important the information for compliance, for your personal data and for your business.

Now that we have explored the data that Macie classifies and monitors, the last stop on our service tour is the Macie dashboard.

 

The Macie Dashboard provides us with a complete picture of all of the data and activity that has been gathered as Macie monitors and classifies our data. The dashboard displays Metrics and Views grouped by categories to provide different visual perspectives of your data. Within these dashboard screens, you also you can go from a metric perspective directly to the Research tab to build and run queries based on the metric. These queries can be used to set up customized alerts for notification of any possible security issues or problems. We won’t have an opportunity to tour the Research or Alerts tab, but you can find out more information about these features in the Macie user guide.

Turning back to the Dashboard, there are so many great resources in the Macie Dashboard that we will not be able to stop at each view, metric, and feature during our tour, so let me give you an overview of all the features of the dashboard that you can take advantage of using.

Dashboard Metrics – monitored data grouped by the following categories:

  • High-risk S3 objects: data objects with risk levels of 8 through 10.
  • Total event occurrences: – total count of all event occurrences since Macie was enabled
  • Total user sessions – 5-minute snapshot of CloudTrail data

Dashboard Views – views to display various points of the monitored data and activity:

  • S3 objects for a selected time range
  • S3 objects
  • S3 objects by personally identifiable information (PII)
  • S3 objects by ACL
  • CloudTrail events and associated users
  • CloudTrail errors and associated users
  • Activity location
  • AWS CLoudTrail events
  • Activity ISPs
  • AWS CloudTrail user identity types

Summary

Well, that concludes our tour of the new and exciting Amazon Macie service. Amazon Macie is a sensational new service that uses the power of machine learning and deep learning to aid you in securing, identifying, and protecting your data stored in Amazon S3. Using natural language processing (NLP) to automate data classification, Amazon Macie enables you to easily get started with high accuracy classification and immediate protection of your data by simply enabling the service.  The interactive dashboards give visibility to the where, what, who, and when of your information allowing you to proactively analyze massive streams of data, data accesses, and API calls in your environment. Learn more about Amazon Macie by visiting the product page or the documentation in the Amazon Macie user guide.

– Tara

AWS Config Update – New Managed Rules to Secure S3 Buckets

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-config-update-new-managed-rules-to-secure-s3-buckets/

AWS Config captures the state of your AWS resources and the relationships between them. Among other features, it allows you to select a resource and then view a timeline of configuration changes that affect the resource (read Track AWS Resource Relationships With AWS Config to learn more).

AWS Config rules extends Config with a powerful rule system, with support for a “managed” collection of AWS rules as well as custom rules that you write yourself (my blog post, AWS Config Rules – Dynamic Compliance Checking for Cloud Resources, contains more info). The rules (AWS Lambda functions) represent the ideal (properly configured and compliant) state of your AWS resources. The appropriate functions are invoked when a configuration change is detected and check to ensure compliance.

You already have access to about three dozen managed rules. For example, here are some of the rules that check your EC2 instances and related resources:

Two New Rules
Today we are adding two new managed rules that will help you to secure your S3 buckets. You can enable these rules with a single click. The new rules are:

s3-bucket-public-write-prohibited – Automatically identifies buckets that allow global write access. There’s rarely a reason to create this configuration intentionally since it allows
unauthorized users to add malicious content to buckets and to delete (by overwriting) existing content. The rule checks all of the buckets in the account.

s3-bucket-public-read-prohibited – Automatically identifies buckets that allow global read access. This will flag content that is publicly available, including web sites and documentation. This rule also checks all buckets in the account.

Like the existing rules, the new rules can be run on a schedule or in response to changes detected by Config. You can see the compliance status of all of your rules at a glance:

Each evaluation runs in a matter of milliseconds; scanning an account with 100 buckets will take less than a minute. Behind the scenes, the rules are evaluated by a reasoning engine that uses some leading-edge constraint solving techniques that can, in many cases, address NP-complete problems in polynomial time (we did not resolve P versus NP; that would be far bigger news). This work is part of a larger effort within AWS, some of which is described in a AWS re:Invent presentation: Automated Formal Reasoning About AWS Systems:

Now Available
The new rules are available now and you can start using them today. Like the other rules, they are priced at $2 per rule per month.

— Jeff;

AWS Migration Hub – Plan & Track Enterprise Application Migration

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-migration-hub-plan-track-enterprise-application-migration/

About once a week, I speak to current and potential AWS customers in our Seattle Executive Briefing Center. While I generally focus on our innovation process, we sometimes discuss other topics, including application migration. When enterprises decide to migrate their application portfolios they want to do it in a structured, orderly fashion. These portfolios typically consist of hundreds of complex Windows and Linux applications, relational databases, and more. Customers find themselves eager yet uncertain as to how to proceed. After spending time working with these customers, we have learned that their challenges generally fall in to three major categories:

Discovery – They want to make sure that they have a deep and complete understanding of all of the moving parts that power each application.

Server & Database Migration – They need to transfer on-premises workloads and database tables to the cloud.

Tracking / Management – With large application portfolios and multiple migrations happening in parallel, they need to track and manage progress in an application-centric fashion.

Over the last couple of years we have launched a set of tools that address the first two challenges. The AWS Application Discovery Service automates the process of discovering and collecting system information, the AWS Server Migration Service takes care of moving workloads to the cloud, and the AWS Database Migration Service moves relational databases, NoSQL databases, and data warehouses with minimal downtime. Partners like Racemi and CloudEndure also offer migration tools of their own.

New AWS Migration Hub
Today we are bringing this collection of AWS and partner migration tools together in the AWS Migration Hub. The hub provides access to the tools that I mentioned above, guides you through the migration process, and tracks the status of each migration, all in accord with the methodology and tenets described in our Migration Acceleration Program (MAP).

Here’s the main screen. It outlines the migration process (discovery, migration, and tracking):

Clicking on Start discovery reveals the flow of the migration process:

It is also possible to skip the Discovery step and begin the migration immediately:

The Servers list is populated using data from an AWS migration service (Server Migration Service or Database Migration Service), partner tools, or using data collected by the AWS Application Discovery Service:

I can on Group as application to create my first application:

Once I identify some applications to migrate, I can track them in the Migrations section of the Hub:

The migration tools, if authorized, automatically send status updates and results back to Migration Hub, for display on the migration status page for the application. Here you can see that Racemi DynaCenter and CloudEndure Migration have played their parts in the migration:

I can track the status of my migrations by checking the Migration Hub Dashboard:

Migration Hub works with migration tools from AWS and our Migration Partners; see the list of integrated partner tools to learn more:

Available Now
AWS Migration Hub can manage migrations in any AWS Region that has the necessary migration tools available; the hub itself runs in the US West (Oregon) Region. There is no charge for the Hub; you pay only for the AWS services that you consume in the course of the migration.

If you are ready to begin your migration to the cloud and are in need of some assistance, please take advantage of the services offered by our Migration Acceleration Partners. These organizations have earned their migration competency by repeatedly demonstrating their ability to deliver large-scale migration.

— Jeff;