Tag Archives: Apache

Amazon SageMaker Updates – Tokyo Region, CloudFormation, Chainer, and GreenGrass ML

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/sagemaker-tokyo-summit-2018/

Today, at the AWS Summit in Tokyo we announced a number of updates and new features for Amazon SageMaker. Starting today, SageMaker is available in Asia Pacific (Tokyo)! SageMaker also now supports CloudFormation. A new machine learning framework, Chainer, is now available in the SageMaker Python SDK, in addition to MXNet and Tensorflow. Finally, support for running Chainer models on several devices was added to AWS Greengrass Machine Learning.

Amazon SageMaker Chainer Estimator


Chainer is a popular, flexible, and intuitive deep learning framework. Chainer networks work on a “Define-by-Run” scheme, where the network topology is defined dynamically via forward computation. This is in contrast to many other frameworks which work on a “Define-and-Run” scheme where the topology of the network is defined separately from the data. A lot of developers enjoy the Chainer scheme since it allows them to write their networks with native python constructs and tools.

Luckily, using Chainer with SageMaker is just as easy as using a TensorFlow or MXNet estimator. In fact, it might even be a bit easier since it’s likely you can take your existing scripts and use them to train on SageMaker with very few modifications. With TensorFlow or MXNet users have to implement a train function with a particular signature. With Chainer your scripts can be a little bit more portable as you can simply read from a few environment variables like SM_MODEL_DIR, SM_NUM_GPUS, and others. We can wrap our existing script in a if __name__ == '__main__': guard and invoke it locally or on sagemaker.


import argparse
import os

if __name__ =='__main__':

    parser = argparse.ArgumentParser()

    # hyperparameters sent by the client are passed as command-line arguments to the script.
    parser.add_argument('--epochs', type=int, default=10)
    parser.add_argument('--batch-size', type=int, default=64)
    parser.add_argument('--learning-rate', type=float, default=0.05)

    # Data, model, and output directories
    parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
    parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])
    parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])
    parser.add_argument('--test', type=str, default=os.environ['SM_CHANNEL_TEST'])

    args, _ = parser.parse_known_args()

    # ... load from args.train and args.test, train a model, write model to args.model_dir.

Then, we can run that script locally or use the SageMaker Python SDK to launch it on some GPU instances in SageMaker. The hyperparameters will get passed in to the script as CLI commands and the environment variables above will be autopopulated. When we call fit the input channels we pass will be populated in the SM_CHANNEL_* environment variables.


from sagemaker.chainer.estimator import Chainer
# Create my estimator
chainer_estimator = Chainer(
    entry_point='example.py',
    train_instance_count=1,
    train_instance_type='ml.p3.2xlarge',
    hyperparameters={'epochs': 10, 'batch-size': 64}
)
# Train my estimator
chainer_estimator.fit({'train': train_input, 'test': test_input})

# Deploy my estimator to a SageMaker Endpoint and get a Predictor
predictor = chainer_estimator.deploy(
    instance_type="ml.m4.xlarge",
    initial_instance_count=1
)

Now, instead of bringing your own docker container for training and hosting with Chainer, you can just maintain your script. You can see the full sagemaker-chainer-containers on github. One of my favorite features of the new container is built-in chainermn for easy multi-node distribution of your chainer training jobs.

There’s a lot more documentation and information available in both the README and the example notebooks.

AWS GreenGrass ML with Chainer

AWS GreenGrass ML now includes a pre-built Chainer package for all devices powered by Intel Atom, NVIDIA Jetson, TX2, and Raspberry Pi. So, now GreenGrass ML provides pre-built packages for TensorFlow, Apache MXNet, and Chainer! You can train your models on SageMaker then easily deploy it to any GreenGrass-enabled device using GreenGrass ML.

JAWS UG

I want to give a quick shout out to all of our wonderful and inspirational friends in the JAWS UG who attended the AWS Summit in Tokyo today. I’ve very much enjoyed seeing your pictures of the summit. Thanks for making Japan an amazing place for AWS developers! I can’t wait to visit again and meet with all of you.

Randall

Amazon Neptune Generally Available

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/amazon-neptune-generally-available/

Amazon Neptune is now Generally Available in US East (N. Virginia), US East (Ohio), US West (Oregon), and EU (Ireland). Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. At the core of Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with millisecond latencies. Neptune supports two popular graph models, Property Graph and RDF, through Apache TinkerPop Gremlin and SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets. Neptune can be used to power everything from recommendation engines and knowledge graphs to drug discovery and network security. Neptune is fully-managed with automatic minor version upgrades, backups, encryption, and fail-over. I wrote about Neptune in detail for AWS re:Invent last year and customers have been using the preview and providing great feedback that the team has used to prepare the service for GA.

Now that Amazon Neptune is generally available there are a few changes from the preview:

Launching an Amazon Neptune Cluster

Launching a Neptune cluster is as easy as navigating to the AWS Management Console and clicking create cluster. Of course you can also launch with CloudFormation, the CLI, or the SDKs.

You can monitor your cluster health and the health of individual instances through Amazon CloudWatch and the console.

Additional Resources

We’ve created two repos with some additional tools and examples here. You can expect continuous development on these repos as we add additional tools and examples.

  • Amazon Neptune Tools Repo
    This repo has a useful tool for converting GraphML files into Neptune compatible CSVs for bulk loading from S3.
  • Amazon Neptune Samples Repo
    This repo has a really cool example of building a collaborative filtering recommendation engine for video game preferences.

Purpose Built Databases

There’s an industry trend where we’re moving more and more onto purpose-built databases. Developers and businesses want to access their data in the format that makes the most sense for their applications. As cloud resources make transforming large datasets easier with tools like AWS Glue, we have a lot more options than we used to for accessing our data. With tools like Amazon Redshift, Amazon Athena, Amazon Aurora, Amazon DynamoDB, and more we get to choose the best database for the job or even enable entirely new use-cases. Amazon Neptune is perfect for workloads where the data is highly connected across data rich edges.

I’m really excited about graph databases and I see a huge number of applications. Looking for ideas of cool things to build? I’d love to build a web crawler in AWS Lambda that uses Neptune as the backing store. You could further enrich it by running Amazon Comprehend or Amazon Rekognition on the text and images found and creating a search engine on top of Neptune.

As always, feel free to reach out in the comments or on twitter to provide any feedback!

Randall

Security updates for Wednesday

Post Syndicated from ris original https://lwn.net/Articles/756020/rss

Security updates have been issued by Arch Linux (strongswan, wireshark-cli, wireshark-common, wireshark-gtk, and wireshark-qt), CentOS (libvirt, procps-ng, and thunderbird), Debian (apache2, git, and qemu), Gentoo (beep, git, and procps), Mageia (mariadb, microcode, python, virtualbox, and webkit2), openSUSE (ceph, pdns, and perl-DBD-mysql), Red Hat (kernel), SUSE (HA kernel modules, libmikmod, ntp, and tiff), and Ubuntu (nvidia-graphics-drivers-384).

From Framework to Function: Deploying AWS Lambda Functions for Java 8 using Apache Maven Archetype

Post Syndicated from Ryosuke Iwanaga original https://aws.amazon.com/blogs/compute/from-framework-to-function-deploying-aws-lambda-functions-for-java-8-using-apache-maven-archetype/

As a serverless computing platform that supports Java 8 runtime, AWS Lambda makes it easy to run any type of Java function simply by uploading a JAR file. To help define not only a Lambda serverless application but also Amazon API Gateway, Amazon DynamoDB, and other related services, the AWS Serverless Application Model (SAM) allows developers to use a simple AWS CloudFormation template.

AWS provides the AWS Toolkit for Eclipse that supports both Lambda and SAM. AWS also gives customers an easy way to create Lambda functions and SAM applications in Java using the AWS Command Line Interface (AWS CLI). After you build a JAR file, all you have to do is type the following commands:

aws cloudformation package 
aws cloudformation deploy

To consolidate these steps, customers can use Archetype by Apache Maven. Archetype uses a predefined package template that makes getting started to develop a function exceptionally simple.

In this post, I introduce a Maven archetype that allows you to create a skeleton of AWS SAM for a Java function. Using this archetype, you can generate a sample Java code example and an accompanying SAM template to deploy it on AWS Lambda by a single Maven action.

Prerequisites

Make sure that the following software is installed on your workstation:

  • Java
  • Maven
  • AWS CLI
  • (Optional) AWS SAM CLI

Install Archetype

After you’ve set up those packages, install Archetype with the following commands:

git clone https://github.com/awslabs/aws-serverless-java-archetype
cd aws-serverless-java-archetype
mvn install

These are one-time operations, so you don’t run them for every new package. If you’d like, you can add Archetype to your company’s Maven repository so that other developers can use it later.

With those packages installed, you’re ready to develop your new Lambda Function.

Start a project

Now that you have the archetype, customize it and run the code:

cd /path/to/project_home
mvn archetype:generate \
  -DarchetypeGroupId=com.amazonaws.serverless.archetypes \
  -DarchetypeArtifactId=aws-serverless-java-archetype \
  -DarchetypeVersion=1.0.0 \
  -DarchetypeRepository=local \ # Forcing to use local maven repository
  -DinteractiveMode=false \ # For batch mode
  # You can also specify properties below interactively if you omit the line for batch mode
  -DgroupId=YOUR_GROUP_ID \
  -DartifactId=YOUR_ARTIFACT_ID \
  -Dversion=YOUR_VERSION \
  -DclassName=YOUR_CLASSNAME

You should have a directory called YOUR_ARTIFACT_ID that contains the files and folders shown below:

├── event.json
├── pom.xml
├── src
│   └── main
│       ├── java
│       │   └── Package
│       │       └── Example.java
│       └── resources
│           └── log4j2.xml
└── template.yaml

The sample code is a working example. If you install SAM CLI, you can invoke it just by the command below:

cd YOUR_ARTIFACT_ID
mvn -P invoke verify
[INFO] Scanning for projects...
[INFO]
[INFO] ---------------------------< com.riywo:foo >----------------------------
[INFO] Building foo 1.0
[INFO] --------------------------------[ jar ]---------------------------------
...
[INFO] --- maven-jar-plugin:3.0.2:jar (default-jar) @ foo ---
[INFO] Building jar: /private/tmp/foo/target/foo-1.0.jar
[INFO]
[INFO] --- maven-shade-plugin:3.1.0:shade (shade) @ foo ---
[INFO] Including com.amazonaws:aws-lambda-java-core:jar:1.2.0 in the shaded jar.
[INFO] Replacing /private/tmp/foo/target/lambda.jar with /private/tmp/foo/target/foo-1.0-shaded.jar
[INFO]
[INFO] --- exec-maven-plugin:1.6.0:exec (sam-local-invoke) @ foo ---
2018/04/06 16:34:35 Successfully parsed template.yaml
2018/04/06 16:34:35 Connected to Docker 1.37
2018/04/06 16:34:35 Fetching lambci/lambda:java8 image for java8 runtime...
java8: Pulling from lambci/lambda
Digest: sha256:14df0a5914d000e15753d739612a506ddb8fa89eaa28dcceff5497d9df2cf7aa
Status: Image is up to date for lambci/lambda:java8
2018/04/06 16:34:37 Invoking Package.Example::handleRequest (java8)
2018/04/06 16:34:37 Decompressing /tmp/foo/target/lambda.jar
2018/04/06 16:34:37 Mounting /private/var/folders/x5/ldp7c38545v9x5dg_zmkr5kxmpdprx/T/aws-sam-local-1523000077594231063 as /var/task:ro inside runtime container
START RequestId: a6ae19fe-b1b0-41e2-80bc-68a40d094d74 Version: $LATEST
Log output: Greeting is 'Hello Tim Wagner.'
END RequestId: a6ae19fe-b1b0-41e2-80bc-68a40d094d74
REPORT RequestId: a6ae19fe-b1b0-41e2-80bc-68a40d094d74	Duration: 96.60 ms	Billed Duration: 100 ms	Memory Size: 128 MB	Max Memory Used: 7 MB

{"greetings":"Hello Tim Wagner."}


[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 10.452 s
[INFO] Finished at: 2018-04-06T16:34:40+09:00
[INFO] ------------------------------------------------------------------------

This maven goal invokes sam local invoke -e event.json, so you can see the sample output to greet Tim Wagner.

To deploy this application to AWS, you need an Amazon S3 bucket to upload your package. You can use the following command to create a bucket if you want:

aws s3 mb s3://YOUR_BUCKET --region YOUR_REGION

Now, you can deploy your application by just one command!

mvn deploy \
    -DawsRegion=YOUR_REGION \
    -Ds3Bucket=YOUR_BUCKET \
    -DstackName=YOUR_STACK
[INFO] Scanning for projects...
[INFO]
[INFO] ---------------------------< com.riywo:foo >----------------------------
[INFO] Building foo 1.0
[INFO] --------------------------------[ jar ]---------------------------------
...
[INFO] --- exec-maven-plugin:1.6.0:exec (sam-package) @ foo ---
Uploading to aws-serverless-java/com.riywo:foo:1.0/924732f1f8e4705c87e26ef77b080b47  11657 / 11657.0  (100.00%)
Successfully packaged artifacts and wrote output template to file target/sam.yaml.
Execute the following command to deploy the packaged template
aws cloudformation deploy --template-file /private/tmp/foo/target/sam.yaml --stack-name <YOUR STACK NAME>
[INFO]
[INFO] --- maven-deploy-plugin:2.8.2:deploy (default-deploy) @ foo ---
[INFO] Skipping artifact deployment
[INFO]
[INFO] --- exec-maven-plugin:1.6.0:exec (sam-deploy) @ foo ---

Waiting for changeset to be created..
Waiting for stack create/update to complete
Successfully created/updated stack - archetype
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 37.176 s
[INFO] Finished at: 2018-04-06T16:41:02+09:00
[INFO] ------------------------------------------------------------------------

Maven automatically creates a shaded JAR file, uploads it to your S3 bucket, replaces template.yaml, and creates and updates the CloudFormation stack.

To customize the process, modify the pom.xml file. For example, to avoid typing values for awsRegion, s3Bucket or stackName, write them inside pom.xml and check in your VCS. Afterward, you and the rest of your team can deploy the function by typing just the following command:

mvn deploy

Options

Lambda Java 8 runtime has some types of handlers: POJO, Simple type and Stream. The default option of this archetype is POJO style, which requires to create request and response classes, but they are baked by the archetype by default. If you want to use other type of handlers, you can use handlerType property like below:

## POJO type (default)
mvn archetype:generate \
 ...
 -DhandlerType=pojo

## Simple type - String
mvn archetype:generate \
 ...
 -DhandlerType=simple

### Stream type
mvn archetype:generate \
 ...
 -DhandlerType=stream

See documentation for more details about handlers.

Also, Lambda Java 8 runtime supports two types of Logging class: Log4j 2 and LambdaLogger. This archetype creates LambdaLogger implementation by default, but you can use Log4j 2 if you want:

## LambdaLogger (default)
mvn archetype:generate \
 ...
 -Dlogger=lambda

## Log4j 2
mvn archetype:generate \
 ...
 -Dlogger=log4j2

If you use LambdaLogger, you can delete ./src/main/resources/log4j2.xml. See documentation for more details.

Conclusion

So, what’s next? Develop your Lambda function locally and type the following command: mvn deploy !

With this Archetype code example, available on GitHub repo, you should be able to deploy Lambda functions for Java 8 in a snap. If you have any questions or comments, please submit them below or leave them on GitHub.

Analyze Apache Parquet optimized data using Amazon Kinesis Data Firehose, Amazon Athena, and Amazon Redshift

Post Syndicated from Roy Hasson original https://aws.amazon.com/blogs/big-data/analyzing-apache-parquet-optimized-data-using-amazon-kinesis-data-firehose-amazon-athena-and-amazon-redshift/

Amazon Kinesis Data Firehose is the easiest way to capture and stream data into a data lake built on Amazon S3. This data can be anything—from AWS service logs like AWS CloudTrail log files, Amazon VPC Flow Logs, Application Load Balancer logs, and others. It can also be IoT events, game events, and much more. To efficiently query this data, a time-consuming ETL (extract, transform, and load) process is required to massage and convert the data to an optimal file format, which increases the time to insight. This situation is less than ideal, especially for real-time data that loses its value over time.

To solve this common challenge, Kinesis Data Firehose can now save data to Amazon S3 in Apache Parquet or Apache ORC format. These are optimized columnar formats that are highly recommended for best performance and cost-savings when querying data in S3. This feature directly benefits you if you use Amazon Athena, Amazon Redshift, AWS Glue, Amazon EMR, or any other big data tools that are available from the AWS Partner Network and through the open-source community.

Amazon Connect is a simple-to-use, cloud-based contact center service that makes it easy for any business to provide a great customer experience at a lower cost than common alternatives. Its open platform design enables easy integration with other systems. One of those systems is Amazon Kinesis—in particular, Kinesis Data Streams and Kinesis Data Firehose.

What’s really exciting is that you can now save events from Amazon Connect to S3 in Apache Parquet format. You can then perform analytics using Amazon Athena and Amazon Redshift Spectrum in real time, taking advantage of this key performance and cost optimization. Of course, Amazon Connect is only one example. This new capability opens the door for a great deal of opportunity, especially as organizations continue to build their data lakes.

Amazon Connect includes an array of analytics views in the Administrator dashboard. But you might want to run other types of analysis. In this post, I describe how to set up a data stream from Amazon Connect through Kinesis Data Streams and Kinesis Data Firehose and out to S3, and then perform analytics using Athena and Amazon Redshift Spectrum. I focus primarily on the Kinesis Data Firehose support for Parquet and its integration with the AWS Glue Data Catalog, Amazon Athena, and Amazon Redshift.

Solution overview

Here is how the solution is laid out:

 

 

The following sections walk you through each of these steps to set up the pipeline.

1. Define the schema

When Kinesis Data Firehose processes incoming events and converts the data to Parquet, it needs to know which schema to apply. The reason is that many times, incoming events contain all or some of the expected fields based on which values the producers are advertising. A typical process is to normalize the schema during a batch ETL job so that you end up with a consistent schema that can easily be understood and queried. Doing this introduces latency due to the nature of the batch process. To overcome this issue, Kinesis Data Firehose requires the schema to be defined in advance.

To see the available columns and structures, see Amazon Connect Agent Event Streams. For the purpose of simplicity, I opted to make all the columns of type String rather than create the nested structures. But you can definitely do that if you want.

The simplest way to define the schema is to create a table in the Amazon Athena console. Open the Athena console, and paste the following create table statement, substituting your own S3 bucket and prefix for where your event data will be stored. A Data Catalog database is a logical container that holds the different tables that you can create. The default database name shown here should already exist. If it doesn’t, you can create it or use another database that you’ve already created.

CREATE EXTERNAL TABLE default.kfhconnectblog (
  awsaccountid string,
  agentarn string,
  currentagentsnapshot string,
  eventid string,
  eventtimestamp string,
  eventtype string,
  instancearn string,
  previousagentsnapshot string,
  version string
)
STORED AS parquet
LOCATION 's3://your_bucket/kfhconnectblog/'
TBLPROPERTIES ("parquet.compression"="SNAPPY")

That’s all you have to do to prepare the schema for Kinesis Data Firehose.

2. Define the data streams

Next, you need to define the Kinesis data streams that will be used to stream the Amazon Connect events.  Open the Kinesis Data Streams console and create two streams.  You can configure them with only one shard each because you don’t have a lot of data right now.

3. Define the Kinesis Data Firehose delivery stream for Parquet

Let’s configure the Data Firehose delivery stream using the data stream as the source and Amazon S3 as the output. Start by opening the Kinesis Data Firehose console and creating a new data delivery stream. Give it a name, and associate it with the Kinesis data stream that you created in Step 2.

As shown in the following screenshot, enable Record format conversion (1) and choose Apache Parquet (2). As you can see, Apache ORC is also supported. Scroll down and provide the AWS Glue Data Catalog database name (3) and table names (4) that you created in Step 1. Choose Next.

To make things easier, the output S3 bucket and prefix fields are automatically populated using the values that you defined in the LOCATION parameter of the create table statement from Step 1. Pretty cool. Additionally, you have the option to save the raw events into another location as defined in the Source record S3 backup section. Don’t forget to add a trailing forward slash “ / “ so that Data Firehose creates the date partitions inside that prefix.

On the next page, in the S3 buffer conditions section, there is a note about configuring a large buffer size. The Parquet file format is highly efficient in how it stores and compresses data. Increasing the buffer size allows you to pack more rows into each output file, which is preferred and gives you the most benefit from Parquet.

Compression using Snappy is automatically enabled for both Parquet and ORC. You can modify the compression algorithm by using the Kinesis Data Firehose API and update the OutputFormatConfiguration.

Be sure to also enable Amazon CloudWatch Logs so that you can debug any issues that you might run into.

Lastly, finalize the creation of the Firehose delivery stream, and continue on to the next section.

4. Set up the Amazon Connect contact center

After setting up the Kinesis pipeline, you now need to set up a simple contact center in Amazon Connect. The Getting Started page provides clear instructions on how to set up your environment, acquire a phone number, and create an agent to accept calls.

After setting up the contact center, in the Amazon Connect console, choose your Instance Alias, and then choose Data Streaming. Under Agent Event, choose the Kinesis data stream that you created in Step 2, and then choose Save.

At this point, your pipeline is complete.  Agent events from Amazon Connect are generated as agents go about their day. Events are sent via Kinesis Data Streams to Kinesis Data Firehose, which converts the event data from JSON to Parquet and stores it in S3. Athena and Amazon Redshift Spectrum can simply query the data without any additional work.

So let’s generate some data. Go back into the Administrator console for your Amazon Connect contact center, and create an agent to handle incoming calls. In this example, I creatively named mine Agent One. After it is created, Agent One can get to work and log into their console and set their availability to Available so that they are ready to receive calls.

To make the data a bit more interesting, I also created a second agent, Agent Two. I then made some incoming and outgoing calls and caused some failures to occur, so I now have enough data available to analyze.

5. Analyze the data with Athena

Let’s open the Athena console and run some queries. One thing you’ll notice is that when we created the schema for the dataset, we defined some of the fields as Strings even though in the documentation they were complex structures.  The reason for doing that was simply to show some of the flexibility of Athena to be able to parse JSON data. However, you can define nested structures in your table schema so that Kinesis Data Firehose applies the appropriate schema to the Parquet file.

Let’s run the first query to see which agents have logged into the system.

The query might look complex, but it’s fairly straightforward:

WITH dataset AS (
  SELECT 
    from_iso8601_timestamp(eventtimestamp) AS event_ts,
    eventtype,
    -- CURRENT STATE
    json_extract_scalar(
      currentagentsnapshot,
      '$.agentstatus.name') AS current_status,
    from_iso8601_timestamp(
      json_extract_scalar(
        currentagentsnapshot,
        '$.agentstatus.starttimestamp')) AS current_starttimestamp,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.firstname') AS current_firstname,
    json_extract_scalar(
      currentagentsnapshot,
      '$.configuration.lastname') AS current_lastname,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.username') AS current_username,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.routingprofile.defaultoutboundqueue.name') AS               current_outboundqueue,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.routingprofile.inboundqueues[0].name') as current_inboundqueue,
    -- PREVIOUS STATE
    json_extract_scalar(
      previousagentsnapshot, 
      '$.agentstatus.name') as prev_status,
    from_iso8601_timestamp(
      json_extract_scalar(
        previousagentsnapshot, 
       '$.agentstatus.starttimestamp')) as prev_starttimestamp,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.firstname') as prev_firstname,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.lastname') as prev_lastname,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.username') as prev_username,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.routingprofile.defaultoutboundqueue.name') as current_outboundqueue,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.routingprofile.inboundqueues[0].name') as prev_inboundqueue
  from kfhconnectblog
  where eventtype <> 'HEART_BEAT'
)
SELECT
  current_status as status,
  current_username as username,
  event_ts
FROM dataset
WHERE eventtype = 'LOGIN' AND current_username <> ''
ORDER BY event_ts DESC

The query output looks something like this:

Here is another query that shows the sessions each of the agents engaged with. It tells us where they were incoming or outgoing, if they were completed, and where there were missed or failed calls.

WITH src AS (
  SELECT
     eventid,
     json_extract_scalar(currentagentsnapshot, '$.configuration.username') as username,
     cast(json_extract(currentagentsnapshot, '$.contacts') AS ARRAY(JSON)) as c,
     cast(json_extract(previousagentsnapshot, '$.contacts') AS ARRAY(JSON)) as p
  from kfhconnectblog
),
src2 AS (
  SELECT *
  FROM src CROSS JOIN UNNEST (c, p) AS contacts(c_item, p_item)
),
dataset AS (
SELECT 
  eventid,
  username,
  json_extract_scalar(c_item, '$.contactid') as c_contactid,
  json_extract_scalar(c_item, '$.channel') as c_channel,
  json_extract_scalar(c_item, '$.initiationmethod') as c_direction,
  json_extract_scalar(c_item, '$.queue.name') as c_queue,
  json_extract_scalar(c_item, '$.state') as c_state,
  from_iso8601_timestamp(json_extract_scalar(c_item, '$.statestarttimestamp')) as c_ts,
  
  json_extract_scalar(p_item, '$.contactid') as p_contactid,
  json_extract_scalar(p_item, '$.channel') as p_channel,
  json_extract_scalar(p_item, '$.initiationmethod') as p_direction,
  json_extract_scalar(p_item, '$.queue.name') as p_queue,
  json_extract_scalar(p_item, '$.state') as p_state,
  from_iso8601_timestamp(json_extract_scalar(p_item, '$.statestarttimestamp')) as p_ts
FROM src2
)
SELECT 
  username,
  c_channel as channel,
  c_direction as direction,
  p_state as prev_state,
  c_state as current_state,
  c_ts as current_ts,
  c_contactid as id
FROM dataset
WHERE c_contactid = p_contactid
ORDER BY id DESC, current_ts ASC

The query output looks similar to the following:

6. Analyze the data with Amazon Redshift Spectrum

With Amazon Redshift Spectrum, you can query data directly in S3 using your existing Amazon Redshift data warehouse cluster. Because the data is already in Parquet format, Redshift Spectrum gets the same great benefits that Athena does.

Here is a simple query to show querying the same data from Amazon Redshift. Note that to do this, you need to first create an external schema in Amazon Redshift that points to the AWS Glue Data Catalog.

SELECT 
  eventtype,
  json_extract_path_text(currentagentsnapshot,'agentstatus','name') AS current_status,
  json_extract_path_text(currentagentsnapshot, 'configuration','firstname') AS current_firstname,
  json_extract_path_text(currentagentsnapshot, 'configuration','lastname') AS current_lastname,
  json_extract_path_text(
    currentagentsnapshot,
    'configuration','routingprofile','defaultoutboundqueue','name') AS current_outboundqueue,
FROM default_schema.kfhconnectblog

The following shows the query output:

Summary

In this post, I showed you how to use Kinesis Data Firehose to ingest and convert data to columnar file format, enabling real-time analysis using Athena and Amazon Redshift. This great feature enables a level of optimization in both cost and performance that you need when storing and analyzing large amounts of data. This feature is equally important if you are investing in building data lakes on AWS.

 


Additional Reading

If you found this post useful, be sure to check out Analyzing VPC Flow Logs with Amazon Kinesis Firehose, Amazon Athena, and Amazon QuickSight and Work with partitioned data in AWS Glue.


About the Author

Roy Hasson is a Global Business Development Manager for AWS Analytics. He works with customers around the globe to design solutions to meet their data processing, analytics and business intelligence needs. Roy is big Manchester United fan cheering his team on and hanging out with his family.

 

 

 

Security updates for Thursday

Post Syndicated from ris original https://lwn.net/Articles/754145/rss

Security updates have been issued by Arch Linux (freetype2, libraw, and powerdns), CentOS (389-ds-base and kernel), Debian (php5, prosody, and wavpack), Fedora (ckeditor, fftw, flac, knot-resolver, patch, perl, and perl-Dancer2), Mageia (cups, flac, graphicsmagick, libcdio, libid3tag, and nextcloud), openSUSE (apache2), Oracle (389-ds-base and kernel), Red Hat (389-ds-base and flash-plugin), Scientific Linux (389-ds-base), Slackware (firefox and wget), SUSE (xen), and Ubuntu (wget).

AWS Online Tech Talks – May and Early June 2018

Post Syndicated from Devin Watson original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-may-and-early-june-2018/

AWS Online Tech Talks – May and Early June 2018  

Join us this month to learn about some of the exciting new services and solution best practices at AWS. We also have our first re:Invent 2018 webinar series, “How to re:Invent”. Sign up now to learn more, we look forward to seeing you.

Note – All sessions are free and in Pacific Time.

Tech talks featured this month:

Analytics & Big Data

May 21, 2018 | 11:00 AM – 11:45 AM PT Integrating Amazon Elasticsearch with your DevOps Tooling – Learn how you can easily integrate Amazon Elasticsearch Service into your DevOps tooling and gain valuable insight from your log data.

May 23, 2018 | 11:00 AM – 11:45 AM PTData Warehousing and Data Lake Analytics, Together – Learn how to query data across your data warehouse and data lake without moving data.

May 24, 2018 | 11:00 AM – 11:45 AM PTData Transformation Patterns in AWS – Discover how to perform common data transformations on the AWS Data Lake.

Compute

May 29, 2018 | 01:00 PM – 01:45 PM PT – Creating and Managing a WordPress Website with Amazon Lightsail – Learn about Amazon Lightsail and how you can create, run and manage your WordPress websites with Amazon’s simple compute platform.

May 30, 2018 | 01:00 PM – 01:45 PM PTAccelerating Life Sciences with HPC on AWS – Learn how you can accelerate your Life Sciences research workloads by harnessing the power of high performance computing on AWS.

Containers

May 24, 2018 | 01:00 PM – 01:45 PM PT – Building Microservices with the 12 Factor App Pattern on AWS – Learn best practices for building containerized microservices on AWS, and how traditional software design patterns evolve in the context of containers.

Databases

May 21, 2018 | 01:00 PM – 01:45 PM PTHow to Migrate from Cassandra to Amazon DynamoDB – Get the benefits, best practices and guides on how to migrate your Cassandra databases to Amazon DynamoDB.

May 23, 2018 | 01:00 PM – 01:45 PM PT5 Hacks for Optimizing MySQL in the Cloud – Learn how to optimize your MySQL databases for high availability, performance, and disaster resilience using RDS.

DevOps

May 23, 2018 | 09:00 AM – 09:45 AM PT.NET Serverless Development on AWS – Learn how to build a modern serverless application in .NET Core 2.0.

Enterprise & Hybrid

May 22, 2018 | 11:00 AM – 11:45 AM PTHybrid Cloud Customer Use Cases on AWS – Learn how customers are leveraging AWS hybrid cloud capabilities to easily extend their datacenter capacity, deliver new services and applications, and ensure business continuity and disaster recovery.

IoT

May 31, 2018 | 11:00 AM – 11:45 AM PTUsing AWS IoT for Industrial Applications – Discover how you can quickly onboard your fleet of connected devices, keep them secure, and build predictive analytics with AWS IoT.

Machine Learning

May 22, 2018 | 09:00 AM – 09:45 AM PTUsing Apache Spark with Amazon SageMaker – Discover how to use Apache Spark with Amazon SageMaker for training jobs and application integration.

May 24, 2018 | 09:00 AM – 09:45 AM PTIntroducing AWS DeepLens – Learn how AWS DeepLens provides a new way for developers to learn machine learning by pairing the physical device with a broad set of tutorials, examples, source code, and integration with familiar AWS services.

Management Tools

May 21, 2018 | 09:00 AM – 09:45 AM PTGaining Better Observability of Your VMs with Amazon CloudWatch – Learn how CloudWatch Agent makes it easy for customers like Rackspace to monitor their VMs.

Mobile

May 29, 2018 | 11:00 AM – 11:45 AM PT – Deep Dive on Amazon Pinpoint Segmentation and Endpoint Management – See how segmentation and endpoint management with Amazon Pinpoint can help you target the right audience.

Networking

May 31, 2018 | 09:00 AM – 09:45 AM PTMaking Private Connectivity the New Norm via AWS PrivateLink – See how PrivateLink enables service owners to offer private endpoints to customers outside their company.

Security, Identity, & Compliance

May 30, 2018 | 09:00 AM – 09:45 AM PT – Introducing AWS Certificate Manager Private Certificate Authority (CA) – Learn how AWS Certificate Manager (ACM) Private Certificate Authority (CA), a managed private CA service, helps you easily and securely manage the lifecycle of your private certificates.

June 1, 2018 | 09:00 AM – 09:45 AM PTIntroducing AWS Firewall Manager – Centrally configure and manage AWS WAF rules across your accounts and applications.

Serverless

May 22, 2018 | 01:00 PM – 01:45 PM PTBuilding API-Driven Microservices with Amazon API Gateway – Learn how to build a secure, scalable API for your application in our tech talk about API-driven microservices.

Storage

May 30, 2018 | 11:00 AM – 11:45 AM PTAccelerate Productivity by Computing at the Edge – Learn how AWS Snowball Edge support for compute instances helps accelerate data transfers, execute custom applications, and reduce overall storage costs.

June 1, 2018 | 11:00 AM – 11:45 AM PTLearn to Build a Cloud-Scale Website Powered by Amazon EFS – Technical deep dive where you’ll learn tips and tricks for integrating WordPress, Drupal and Magento with Amazon EFS.

 

 

 

 

Analyze data in Amazon DynamoDB using Amazon SageMaker for real-time prediction

Post Syndicated from YongSeong Lee original https://aws.amazon.com/blogs/big-data/analyze-data-in-amazon-dynamodb-using-amazon-sagemaker-for-real-time-prediction/

Many companies across the globe use Amazon DynamoDB to store and query historical user-interaction data. DynamoDB is a fast NoSQL database used by applications that need consistent, single-digit millisecond latency.

Often, customers want to turn their valuable data in DynamoDB into insights by analyzing a copy of their table stored in Amazon S3. Doing this separates their analytical queries from their low-latency critical paths. This data can be the primary source for understanding customers’ past behavior, predicting future behavior, and generating downstream business value. Customers often turn to DynamoDB because of its great scalability and high availability. After a successful launch, many customers want to use the data in DynamoDB to predict future behaviors or provide personalized recommendations.

DynamoDB is a good fit for low-latency reads and writes, but it’s not practical to scan all data in a DynamoDB database to train a model. In this post, I demonstrate how you can use DynamoDB table data copied to Amazon S3 by AWS Data Pipeline to predict customer behavior. I also demonstrate how you can use this data to provide personalized recommendations for customers using Amazon SageMaker. You can also run ad hoc queries using Amazon Athena against the data. DynamoDB recently released on-demand backups to create full table backups with no performance impact. However, it’s not suitable for our purposes in this post, so I chose AWS Data Pipeline instead to create managed backups are accessible from other services.

To do this, I describe how to read the DynamoDB backup file format in Data Pipeline. I also describe how to convert the objects in S3 to a CSV format that Amazon SageMaker can read. In addition, I show how to schedule regular exports and transformations using Data Pipeline. The sample data used in this post is from Bank Marketing Data Set of UCI.

The solution that I describe provides the following benefits:

  • Separates analytical queries from production traffic on your DynamoDB table, preserving your DynamoDB read capacity units (RCUs) for important production requests
  • Automatically updates your model to get real-time predictions
  • Optimizes for performance (so it doesn’t compete with DynamoDB RCUs after the export) and for cost (using data you already have)
  • Makes it easier for developers of all skill levels to use Amazon SageMaker

All code and data set in this post are available in this .zip file.

Solution architecture

The following diagram shows the overall architecture of the solution.

The steps that data follows through the architecture are as follows:

  1. Data Pipeline regularly copies the full contents of a DynamoDB table as JSON into an S3
  2. Exported JSON files are converted to comma-separated value (CSV) format to use as a data source for Amazon SageMaker.
  3. Amazon SageMaker renews the model artifact and update the endpoint.
  4. The converted CSV is available for ad hoc queries with Amazon Athena.
  5. Data Pipeline controls this flow and repeats the cycle based on the schedule defined by customer requirements.

Building the auto-updating model

This section discusses details about how to read the DynamoDB exported data in Data Pipeline and build automated workflows for real-time prediction with a regularly updated model.

Download sample scripts and data

Before you begin, take the following steps:

  1. Download sample scripts in this .zip file.
  2. Unzip the src.zip file.
  3. Find the automation_script.sh file and edit it for your environment. For example, you need to replace 's3://<your bucket>/<datasource path>/' with your own S3 path to the data source for Amazon ML. In the script, the text enclosed by angle brackets—< and >—should be replaced with your own path.
  4. Upload the json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar file to your S3 path so that the ADD jar command in Apache Hive can refer to it.

For this solution, the banking.csv  should be imported into a DynamoDB table.

Export a DynamoDB table

To export the DynamoDB table to S3, open the Data Pipeline console and choose the Export DynamoDB table to S3 template. In this template, Data Pipeline creates an Amazon EMR cluster and performs an export in the EMRActivity activity. Set proper intervals for backups according to your business requirements.

One core node(m3.xlarge) provides the default capacity for the EMR cluster and should be suitable for the solution in this post. Leave the option to resize the cluster before running enabled in the TableBackupActivity activity to let Data Pipeline scale the cluster to match the table size. The process of converting to CSV format and renewing models happens in this EMR cluster.

For a more in-depth look at how to export data from DynamoDB, see Export Data from DynamoDB in the Data Pipeline documentation.

Add the script to an existing pipeline

After you export your DynamoDB table, you add an additional EMR step to EMRActivity by following these steps:

  1. Open the Data Pipeline console and choose the ID for the pipeline that you want to add the script to.
  2. For Actions, choose Edit.
  3. In the editing console, choose the Activities category and add an EMR step using the custom script downloaded in the previous section, as shown below.

Paste the following command into the new step after the data ­­upload step:

s3://#{myDDBRegion}.elasticmapreduce/libs/script-runner/script-runner.jar,s3://<your bucket name>/automation_script.sh,#{output.directoryPath},#{myDDBRegion}

The element #{output.directoryPath} references the S3 path where the data pipeline exports DynamoDB data as JSON. The path should be passed to the script as an argument.

The bash script has two goals, converting data formats and renewing the Amazon SageMaker model. Subsequent sections discuss the contents of the automation script.

Automation script: Convert JSON data to CSV with Hive

We use Apache Hive to transform the data into a new format. The Hive QL script to create an external table and transform the data is included in the custom script that you added to the Data Pipeline definition.

When you run the Hive scripts, do so with the -e option. Also, define the Hive table with the 'org.openx.data.jsonserde.JsonSerDe' row format to parse and read JSON format. The SQL creates a Hive EXTERNAL table, and it reads the DynamoDB backup data on the S3 path passed to it by Data Pipeline.

Note: You should create the table with the “EXTERNAL” keyword to avoid the backup data being accidentally deleted from S3 if you drop the table.

The full automation script for converting follows. Add your own bucket name and data source path in the highlighted areas.

#!/bin/bash
hive -e "
ADD jar s3://<your bucket name>/json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar ; 
DROP TABLE IF EXISTS blog_backup_data ;
CREATE EXTERNAL TABLE blog_backup_data (
 customer_id map<string,string>,
 age map<string,string>, job map<string,string>, 
 marital map<string,string>,education map<string,string>, 
 default map<string,string>, housing map<string,string>,
 loan map<string,string>, contact map<string,string>, 
 month map<string,string>, day_of_week map<string,string>, 
 duration map<string,string>, campaign map<string,string>,
 pdays map<string,string>, previous map<string,string>, 
 poutcome map<string,string>, emp_var_rate map<string,string>, 
 cons_price_idx map<string,string>, cons_conf_idx map<string,string>,
 euribor3m map<string,string>, nr_employed map<string,string>, 
 y map<string,string> ) 
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe' 
LOCATION '$1/';

INSERT OVERWRITE DIRECTORY 's3://<your bucket name>/<datasource path>/' 
SELECT concat( customer_id['s'],',', 
 age['n'],',', job['s'],',', 
 marital['s'],',', education['s'],',', default['s'],',', 
 housing['s'],',', loan['s'],',', contact['s'],',', 
 month['s'],',', day_of_week['s'],',', duration['n'],',', 
 campaign['n'],',',pdays['n'],',',previous['n'],',', 
 poutcome['s'],',', emp_var_rate['n'],',', cons_price_idx['n'],',',
 cons_conf_idx['n'],',', euribor3m['n'],',', nr_employed['n'],',', y['n'] ) 
FROM blog_backup_data
WHERE customer_id['s'] > 0 ; 

After creating an external table, you need to read data. You then use the INSERT OVERWRITE DIRECTORY ~ SELECT command to write CSV data to the S3 path that you designated as the data source for Amazon SageMaker.

Depending on your requirements, you can eliminate or process the columns in the SELECT clause in this step to optimize data analysis. For example, you might remove some columns that have unpredictable correlations with the target value because keeping the wrong columns might expose your model to “overfitting” during the training. In this post, customer_id  columns is removed. Overfitting can make your prediction weak. More information about overfitting can be found in the topic Model Fit: Underfitting vs. Overfitting in the Amazon ML documentation.

Automation script: Renew the Amazon SageMaker model

After the CSV data is replaced and ready to use, create a new model artifact for Amazon SageMaker with the updated dataset on S3.  For renewing model artifact, you must create a new training job.  Training jobs can be run using the AWS SDK ( for example, Amazon SageMaker boto3 ) or the Amazon SageMaker Python SDK that can be installed with “pip install sagemaker” command as well as the AWS CLI for Amazon SageMaker described in this post.

In addition, consider how to smoothly renew your existing model without service impact, because your model is called by applications in real time. To do this, you need to create a new endpoint configuration first and update a current endpoint with the endpoint configuration that is just created.

#!/bin/bash
## Define variable 
REGION=$2
DTTIME=`date +%Y-%m-%d-%H-%M-%S`
ROLE="<your AmazonSageMaker-ExecutionRole>" 


# Select containers image based on region.  
case "$REGION" in
"us-west-2" )
    IMAGE="174872318107.dkr.ecr.us-west-2.amazonaws.com/linear-learner:latest"
    ;;
"us-east-1" )
    IMAGE="382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:latest" 
    ;;
"us-east-2" )
    IMAGE="404615174143.dkr.ecr.us-east-2.amazonaws.com/linear-learner:latest" 
    ;;
"eu-west-1" )
    IMAGE="438346466558.dkr.ecr.eu-west-1.amazonaws.com/linear-learner:latest" 
    ;;
 *)
    echo "Invalid Region Name"
    exit 1 ;  
esac

# Start training job and creating model artifact 
TRAINING_JOB_NAME=TRAIN-${DTTIME} 
S3OUTPUT="s3://<your bucket name>/model/" 
INSTANCETYPE="ml.m4.xlarge"
INSTANCECOUNT=1
VOLUMESIZE=5 
aws sagemaker create-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}  --algorithm-specification TrainingImage=${IMAGE},TrainingInputMode=File --role-arn ${ROLE}  --input-data-config '[{ "ChannelName": "train", "DataSource": { "S3DataSource": { "S3DataType": "S3Prefix", "S3Uri": "s3://<your bucket name>/<datasource path>/", "S3DataDistributionType": "FullyReplicated" } }, "ContentType": "text/csv", "CompressionType": "None" , "RecordWrapperType": "None"  }]'  --output-data-config S3OutputPath=${S3OUTPUT} --resource-config  InstanceType=${INSTANCETYPE},InstanceCount=${INSTANCECOUNT},VolumeSizeInGB=${VOLUMESIZE} --stopping-condition MaxRuntimeInSeconds=120 --hyper-parameters feature_dim=20,predictor_type=binary_classifier  

# Wait until job completed 
aws sagemaker wait training-job-completed-or-stopped --training-job-name ${TRAINING_JOB_NAME}  --region ${REGION}

# Get newly created model artifact and create model
MODELARTIFACT=`aws sagemaker describe-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}  --query 'ModelArtifacts.S3ModelArtifacts' --output text `
MODELNAME=MODEL-${DTTIME}
aws sagemaker create-model --region ${REGION} --model-name ${MODELNAME}  --primary-container Image=${IMAGE},ModelDataUrl=${MODELARTIFACT}  --execution-role-arn ${ROLE}

# create a new endpoint configuration 
CONFIGNAME=CONFIG-${DTTIME}
aws sagemaker  create-endpoint-config --region ${REGION} --endpoint-config-name ${CONFIGNAME}  --production-variants  VariantName=Users,ModelName=${MODELNAME},InitialInstanceCount=1,InstanceType=ml.m4.xlarge

# create or update the endpoint
STATUS=`aws sagemaker describe-endpoint --endpoint-name  ServiceEndpoint --query 'EndpointStatus' --output text --region ${REGION} `
if [[ $STATUS -ne "InService" ]] ;
then
    aws sagemaker  create-endpoint --endpoint-name  ServiceEndpoint  --endpoint-config-name ${CONFIGNAME} --region ${REGION}    
else
    aws sagemaker  update-endpoint --endpoint-name  ServiceEndpoint  --endpoint-config-name ${CONFIGNAME} --region ${REGION}
fi

Grant permission

Before you execute the script, you must grant proper permission to Data Pipeline. Data Pipeline uses the DataPipelineDefaultResourceRole role by default. I added the following policy to DataPipelineDefaultResourceRole to allow Data Pipeline to create, delete, and update the Amazon SageMaker model and data source in the script.

{
 "Version": "2012-10-17",
 "Statement": [
 {
 "Effect": "Allow",
 "Action": [
 "sagemaker:CreateTrainingJob",
 "sagemaker:DescribeTrainingJob",
 "sagemaker:CreateModel",
 "sagemaker:CreateEndpointConfig",
 "sagemaker:DescribeEndpoint",
 "sagemaker:CreateEndpoint",
 "sagemaker:UpdateEndpoint",
 "iam:PassRole"
 ],
 "Resource": "*"
 }
 ]
}

Use real-time prediction

After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint. This approach is useful for interactive web, mobile, or desktop applications.

Following, I provide a simple Python code example that queries against Amazon SageMaker endpoint URL with its name (“ServiceEndpoint”) and then uses them for real-time prediction.

=== Python sample for real-time prediction ===

#!/usr/bin/env python
import boto3
import json 

client = boto3.client('sagemaker-runtime', region_name ='<your region>' )
new_customer_info = '34,10,2,4,1,2,1,1,6,3,190,1,3,4,3,-1.7,94.055,-39.8,0.715,4991.6'
response = client.invoke_endpoint(
    EndpointName='ServiceEndpoint',
    Body=new_customer_info, 
    ContentType='text/csv'
)
result = json.loads(response['Body'].read().decode())
print(result)
--- output(response) ---
{u'predictions': [{u'score': 0.7528127431869507, u'predicted_label': 1.0}]}

Solution summary

The solution takes the following steps:

  1. Data Pipeline exports DynamoDB table data into S3. The original JSON data should be kept to recover the table in the rare event that this is needed. Data Pipeline then converts JSON to CSV so that Amazon SageMaker can read the data.Note: You should select only meaningful attributes when you convert CSV. For example, if you judge that the “campaign” attribute is not correlated, you can eliminate this attribute from the CSV.
  2. Train the Amazon SageMaker model with the new data source.
  3. When a new customer comes to your site, you can judge how likely it is for this customer to subscribe to your new product based on “predictedScores” provided by Amazon SageMaker.
  4. If the new user subscribes your new product, your application must update the attribute “y” to the value 1 (for yes). This updated data is provided for the next model renewal as a new data source. It serves to improve the accuracy of your prediction. With each new entry, your application can become smarter and deliver better predictions.

Running ad hoc queries using Amazon Athena

Amazon Athena is a serverless query service that makes it easy to analyze large amounts of data stored in Amazon S3 using standard SQL. Athena is useful for examining data and collecting statistics or informative summaries about data. You can also use the powerful analytic functions of Presto, as described in the topic Aggregate Functions of Presto in the Presto documentation.

With the Data Pipeline scheduled activity, recent CSV data is always located in S3 so that you can run ad hoc queries against the data using Amazon Athena. I show this with example SQL statements following. For an in-depth description of this process, see the post Interactive SQL Queries for Data in Amazon S3 on the AWS News Blog. 

Creating an Amazon Athena table and running it

Simply, you can create an EXTERNAL table for the CSV data on S3 in Amazon Athena Management Console.

=== Table Creation ===
CREATE EXTERNAL TABLE datasource (
 age int, 
 job string, 
 marital string , 
 education string, 
 default string, 
 housing string, 
 loan string, 
 contact string, 
 month string, 
 day_of_week string, 
 duration int, 
 campaign int, 
 pdays int , 
 previous int , 
 poutcome string, 
 emp_var_rate double, 
 cons_price_idx double,
 cons_conf_idx double, 
 euribor3m double, 
 nr_employed double, 
 y int 
)
ROW FORMAT DELIMITED 
FIELDS TERMINATED BY ',' ESCAPED BY '\\' LINES TERMINATED BY '\n' 
LOCATION 's3://<your bucket name>/<datasource path>/';

The following query calculates the correlation coefficient between the target attribute and other attributes using Amazon Athena.

=== Sample Query ===

SELECT corr(age,y) AS correlation_age_and_target, 
 corr(duration,y) AS correlation_duration_and_target, 
 corr(campaign,y) AS correlation_campaign_and_target,
 corr(contact,y) AS correlation_contact_and_target
FROM ( SELECT age , duration , campaign , y , 
 CASE WHEN contact = 'telephone' THEN 1 ELSE 0 END AS contact 
 FROM datasource 
 ) datasource ;

Conclusion

In this post, I introduce an example of how to analyze data in DynamoDB by using table data in Amazon S3 to optimize DynamoDB table read capacity. You can then use the analyzed data as a new data source to train an Amazon SageMaker model for accurate real-time prediction. In addition, you can run ad hoc queries against the data on S3 using Amazon Athena. I also present how to automate these procedures by using Data Pipeline.

You can adapt this example to your specific use case at hand, and hopefully this post helps you accelerate your development. You can find more examples and use cases for Amazon SageMaker in the video AWS 2017: Introducing Amazon SageMaker on the AWS website.

 


Additional Reading

If you found this post useful, be sure to check out Serving Real-Time Machine Learning Predictions on Amazon EMR and Analyzing Data in S3 using Amazon Athena.

 


About the Author

Yong Seong Lee is a Cloud Support Engineer for AWS Big Data Services. He is interested in every technology related to data/databases and helping customers who have difficulties in using AWS services. His motto is “Enjoy life, be curious and have maximum experience.”

 

 

Security updates for Tuesday

Post Syndicated from ris original https://lwn.net/Articles/753257/rss

Security updates have been issued by Fedora (cups-filters, ghostscript, glusterfs, PackageKit, qpdf, and xen), Mageia (anki, libofx, ming, sox, webkit2, and xdg-user-dirs), Oracle (corosync, java-1.7.0-openjdk, and pcs), Red Hat (java-1.7.0-openjdk), Scientific Linux (corosync, firefox, gcc, glibc, golang, java-1.7.0-openjdk, java-1.8.0-openjdk, kernel, krb5, librelp, libvncserver, libvorbis, ntp, openssh, openssl, PackageKit, patch, pcs, policycoreutils, qemu-kvm, and xdg-user-dirs), Slackware (libwmf and mozilla), and Ubuntu (apache2, ghostscript, mysql-5.7, wavpack, and webkit2gtk).

Secure Build with AWS CodeBuild and LayeredInsight

Post Syndicated from Asif Khan original https://aws.amazon.com/blogs/devops/secure-build-with-aws-codebuild-and-layeredinsight/

This post is written by Asif Awan, Chief Technology Officer of Layered InsightSubin Mathew – Software Development Manager for AWS CodeBuild, and Asif Khan – Solutions Architect

Enterprises adopt containers because they recognize the benefits: speed, agility, portability, and high compute density. They understand how accelerating application delivery and deployment pipelines makes it possible to rapidly slipstream new features to customers. Although the benefits are indisputable, this acceleration raises concerns about security and corporate compliance with software governance. In this blog post, I provide a solution that shows how Layered Insight, the pioneer and global leader in container-native application protection, can be used with seamless application build and delivery pipelines like those available in AWS CodeBuild to address these concerns.

Layered Insight solutions

Layered Insight enables organizations to unify DevOps and SecOps by providing complete visibility and control of containerized applications. Using the industry’s first embedded security approach, Layered Insight solves the challenges of container performance and protection by providing accurate insight into container images, adaptive analysis of running containers, and automated enforcement of container behavior.

 

AWS CodeBuild

AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages that are ready to deploy. With CodeBuild, you don’t need to provision, manage, and scale your own build servers. CodeBuild scales continuously and processes multiple builds concurrently, so your builds are not left waiting in a queue. You can get started quickly by using prepackaged build environments, or you can create custom build environments that use your own build tools.

 

Problem Definition

Security and compliance concerns span the lifecycle of application containers. Common concerns include:

Visibility into the container images. You need to verify the software composition information of the container image to determine whether known vulnerabilities associated with any of the software packages and libraries are included in the container image.

Governance of container images is critical because only certain open source packages/libraries, of specific versions, should be included in the container images. You need support for mechanisms for blacklisting all container images that include a certain version of a software package/library, or only allowing open source software that come with a specific type of license (such as Apache, MIT, GPL, and so on). You need to be able to address challenges such as:

·       Defining the process for image compliance policies at the enterprise, department, and group levels.

·       Preventing the images that fail the compliance checks from being deployed in critical environments, such as staging, pre-prod, and production.

Visibility into running container instances is critical, including:

·       CPU and memory utilization.

·       Security of the build environment.

·       All activities (system, network, storage, and application layer) of the application code running in each container instance.

Protection of running container instances that is:

·       Zero-touch to the developers (not an SDK-based approach).

·       Zero touch to the DevOps team and doesn’t limit the portability of the containerized application.

·       This protection must retain the option to switch to a different container stack or orchestration layer, or even to a different Container as a Service (CaaS ).

·       And it must be a fully automated solution to SecOps, so that the SecOps team doesn’t have to manually analyze and define detailed blacklist and whitelist policies.

 

Solution Details

In AWS CodeCommit, we have three projects:
●     “Democode” is a simple Java application, with one buildspec to build the app into a Docker container (run by build-demo-image CodeBuild project), and another to instrument said container (instrument-image CodeBuild project). The resulting container is stored in ECR repo javatestasjavatest:20180415-layered. This instrumented container is running in AWS Fargate cluster demo-java-appand can be seen in the Layered Insight runtime console as the javatestapplication in us-east-1.
●     aws-codebuild-docker-imagesis a clone of the official aws-codebuild-docker-images repo on GitHub . This CodeCommit project is used by the build-python-builder CodeBuild project to build the python 3.3.6 codebuild image and is stored at the codebuild-python ECR repo. We then manually instructed the Layered Insight console to instrument the image.
●     scan-java-imagecontains just a buildspec.yml file. This file is used by the scan-java-image CodeBuild project to instruct Layered Assessment to perform a vulnerability scan of the javatest container image built previously, and then run the scan results through a compliance policy that states there should be no medium vulnerabilities. This build fails — but in this case that is a success: the scan completes successfully, but compliance fails as there are medium-level issues found in the scan.

This build is performed using the instrumented version of the Python 3.3.6 CodeBuild image, so the activity of the processes running within the build are recorded each time within the LI console.

Build container image

Create or use a CodeCommit project with your application. To build this image and store it in Amazon Elastic Container Registry (Amazon ECR), add a buildspec file to the project and build a container image and create a CodeBuild project.

Scan container image

Once the image is built, create a new buildspec in the same project or a new one that looks similar to below (update ECR URL as necessary):

version: 0.2
phases:
  pre_build:
    commands:
      - echo Pulling down LI Scan API client scripts
      - git clone https://github.com/LayeredInsight/scan-api-example-python.git
      - echo Setting up LI Scan API client
      - cd scan-api-example-python
      - pip install layint_scan_api
      - pip install -r requirements.txt
  build:
    commands:
      - echo Scanning container started on `date`
      - IMAGEID=$(./li_add_image --name <aws-region>.amazonaws.com/javatest:20180415)
      - ./li_wait_for_scan -v --imageid $IMAGEID
      - ./li_run_image_compliance -v --imageid $IMAGEID --policyid PB15260f1acb6b2aa5b597e9d22feffb538256a01fbb4e5a95

Add the buildspec file to the git repo, push it, and then build a CodeBuild project using with the instrumented Python 3.3.6 CodeBuild image at <aws-region>.amazonaws.com/codebuild-python:3.3.6-layered. Set the following environment variables in the CodeBuild project:
●     LI_APPLICATIONNAME – name of the build to display
●     LI_LOCATION – location of the build project to display
●     LI_API_KEY – ApiKey:<key-name>:<api-key>
●     LI_API_HOST – location of the Layered Insight API service

Instrument container image

Next, to instrument the new container image:

  1. In the Layered Insight runtime console, ensure that the ECR registry and credentials are defined (click the Setup icon and the ‘+’ sign on the top right of the screen to add a new container registry). Note the name given to the registry in the console, as this needs to be referenced in the li_add_imagecommand in the script, below.
  2. Next, add a new buildspec (with a new name) to the CodeCommit project, such as the one shown below. This code will download the Layered Insight runtime client, and use it to instruct the Layered Insight service to instrument the image that was just built:
    version: 0.2
    phases:
    pre_build:
    commands:
    echo Pulling down LI API Runtime client scripts
    git clone https://github.com/LayeredInsight/runtime-api-example-python
    echo Setting up LI API client
    cd runtime-api-example-python
    pip install layint-runtime-api
    pip install -r requirements.txt
    build:
    commands:
    echo Instrumentation started on `date`
    ./li_add_image --registry "Javatest ECR" --name IMAGE_NAME:TAG --description "IMAGE DESCRIPTION" --policy "Default Policy" --instrument --wait --verbose
  3. Commit and push the new buildspec file.
  4. Going back to CodeBuild, create a new project, with the same CodeCommit repo, but this time select the new buildspec file. Use a Python 3.3.6 builder – either the AWS or LI Instrumented version.
  5. Click Continue
  6. Click Save
  7. Run the build, again on the master branch.
  8. If everything runs successfully, a new image should appear in the ECR registry with a -layered suffix. This is the instrumented image.

Run instrumented container image

When the instrumented container is now run — in ECS, Fargate, or elsewhere — it will log data back to the Layered Insight runtime console. It’s appearance in the console can be modified by setting the LI_APPLICATIONNAME and LI_LOCATION environment variables when running the container.

Conclusion

In the above blog we have provided you steps needed to embed governance and runtime security in your build pipelines running on AWS CodeBuild using Layered Insight.

 

 

 

Implement continuous integration and delivery of serverless AWS Glue ETL applications using AWS Developer Tools

Post Syndicated from Prasad Alle original https://aws.amazon.com/blogs/big-data/implement-continuous-integration-and-delivery-of-serverless-aws-glue-etl-applications-using-aws-developer-tools/

AWS Glue is an increasingly popular way to develop serverless ETL (extract, transform, and load) applications for big data and data lake workloads. Organizations that transform their ETL applications to cloud-based, serverless ETL architectures need a seamless, end-to-end continuous integration and continuous delivery (CI/CD) pipeline: from source code, to build, to deployment, to product delivery. Having a good CI/CD pipeline can help your organization discover bugs before they reach production and deliver updates more frequently. It can also help developers write quality code and automate the ETL job release management process, mitigate risk, and more.

AWS Glue is a fully managed data catalog and ETL service. It simplifies and automates the difficult and time-consuming tasks of data discovery, conversion, and job scheduling. AWS Glue crawls your data sources and constructs a data catalog using pre-built classifiers for popular data formats and data types, including CSV, Apache Parquet, JSON, and more.

When you are developing ETL applications using AWS Glue, you might come across some of the following CI/CD challenges:

  • Iterative development with unit tests
  • Continuous integration and build
  • Pushing the ETL pipeline to a test environment
  • Pushing the ETL pipeline to a production environment
  • Testing ETL applications using real data (live test)
  • Exploring and validating data

In this post, I walk you through a solution that implements a CI/CD pipeline for serverless AWS Glue ETL applications supported by AWS Developer Tools (including AWS CodePipeline, AWS CodeCommit, and AWS CodeBuild) and AWS CloudFormation.

Solution overview

The following diagram shows the pipeline workflow:

This solution uses AWS CodePipeline, which lets you orchestrate and automate the test and deploy stages for ETL application source code. The solution consists of a pipeline that contains the following stages:

1.) Source Control: In this stage, the AWS Glue ETL job source code and the AWS CloudFormation template file for deploying the ETL jobs are both committed to version control. I chose to use AWS CodeCommit for version control.

To get the ETL job source code and AWS CloudFormation template, download the gluedemoetl.zip file. This solution is developed based on a previous post, Build a Data Lake Foundation with AWS Glue and Amazon S3.

2.) LiveTest: In this stage, all resources—including AWS Glue crawlers, jobs, S3 buckets, roles, and other resources that are required for the solution—are provisioned, deployed, live tested, and cleaned up.

The LiveTest stage includes the following actions:

  • Deploy: In this action, all the resources that are required for this solution (crawlers, jobs, buckets, roles, and so on) are provisioned and deployed using an AWS CloudFormation template.
  • AutomatedLiveTest: In this action, all the AWS Glue crawlers and jobs are executed and data exploration and validation tests are performed. These validation tests include, but are not limited to, record counts in both raw tables and transformed tables in the data lake and any other business validations. I used AWS CodeBuild for this action.
  • LiveTestApproval: This action is included for the cases in which a pipeline administrator approval is required to deploy/promote the ETL applications to the next stage. The pipeline pauses in this action until an administrator manually approves the release.
  • LiveTestCleanup: In this action, all the LiveTest stage resources, including test crawlers, jobs, roles, and so on, are deleted using the AWS CloudFormation template. This action helps minimize cost by ensuring that the test resources exist only for the duration of the AutomatedLiveTest and LiveTestApproval

3.) DeployToProduction: In this stage, all the resources are deployed using the AWS CloudFormation template to the production environment.

Try it out

This code pipeline takes approximately 20 minutes to complete the LiveTest test stage (up to the LiveTest approval stage, in which manual approval is required).

To get started with this solution, choose Launch Stack:

This creates the CI/CD pipeline with all of its stages, as described earlier. It performs an initial commit of the sample AWS Glue ETL job source code to trigger the first release change.

In the AWS CloudFormation console, choose Create. After the template finishes creating resources, you see the pipeline name on the stack Outputs tab.

After that, open the CodePipeline console and select the newly created pipeline. Initially, your pipeline’s CodeCommit stage shows that the source action failed.

Allow a few minutes for your new pipeline to detect the initial commit applied by the CloudFormation stack creation. As soon as the commit is detected, your pipeline starts. You will see the successful stage completion status as soon as the CodeCommit source stage runs.

In the CodeCommit console, choose Code in the navigation pane to view the solution files.

Next, you can watch how the pipeline goes through the LiveTest stage of the deploy and AutomatedLiveTest actions, until it finally reaches the LiveTestApproval action.

At this point, if you check the AWS CloudFormation console, you can see that a new template has been deployed as part of the LiveTest deploy action.

At this point, make sure that the AWS Glue crawlers and the AWS Glue job ran successfully. Also check whether the corresponding databases and external tables have been created in the AWS Glue Data Catalog. Then verify that the data is validated using Amazon Athena, as shown following.

Open the AWS Glue console, and choose Databases in the navigation pane. You will see the following databases in the Data Catalog:

Open the Amazon Athena console, and run the following queries. Verify that the record counts are matching.

SELECT count(*) FROM "nycitytaxi_gluedemocicdtest"."data";
SELECT count(*) FROM "nytaxiparquet_gluedemocicdtest"."datalake";

The following shows the raw data:

The following shows the transformed data:

The pipeline pauses the action until the release is approved. After validating the data, manually approve the revision on the LiveTestApproval action on the CodePipeline console.

Add comments as needed, and choose Approve.

The LiveTestApproval stage now appears as Approved on the console.

After the revision is approved, the pipeline proceeds to use the AWS CloudFormation template to destroy the resources that were deployed in the LiveTest deploy action. This helps reduce cost and ensures a clean test environment on every deployment.

Production deployment is the final stage. In this stage, all the resources—AWS Glue crawlers, AWS Glue jobs, Amazon S3 buckets, roles, and so on—are provisioned and deployed to the production environment using the AWS CloudFormation template.

After successfully running the whole pipeline, feel free to experiment with it by changing the source code stored on AWS CodeCommit. For example, if you modify the AWS Glue ETL job to generate an error, it should make the AutomatedLiveTest action fail. Or if you change the AWS CloudFormation template to make its creation fail, it should affect the LiveTest deploy action. The objective of the pipeline is to guarantee that all changes that are deployed to production are guaranteed to work as expected.

Conclusion

In this post, you learned how easy it is to implement CI/CD for serverless AWS Glue ETL solutions with AWS developer tools like AWS CodePipeline and AWS CodeBuild at scale. Implementing such solutions can help you accelerate ETL development and testing at your organization.

If you have questions or suggestions, please comment below.

 


Additional Reading

If you found this post useful, be sure to check out Implement Continuous Integration and Delivery of Apache Spark Applications using AWS and Build a Data Lake Foundation with AWS Glue and Amazon S3.

 


About the Authors

Prasad Alle is a Senior Big Data Consultant with AWS Professional Services. He spends his time leading and building scalable, reliable Big data, Machine learning, Artificial Intelligence and IoT solutions for AWS Enterprise and Strategic customers. His interests extend to various technologies such as Advanced Edge Computing, Machine learning at Edge. In his spare time, he enjoys spending time with his family.

 
Luis Caro is a Big Data Consultant for AWS Professional Services. He works with our customers to provide guidance and technical assistance on big data projects, helping them improving the value of their solutions when using AWS.