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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.”

 

 

Facebook and Cambridge Analytica

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/03/facebook_and_ca.html

In the wake of the Cambridge Analytica scandal, news articles and commentators have focused on what Facebook knows about us. A lot, it turns out. It collects data from our posts, our likes, our photos, things we type and delete without posting, and things we do while not on Facebook and even when we’re offline. It buys data about us from others. And it can infer even more: our sexual orientation, political beliefs, relationship status, drug use, and other personality traits — even if we didn’t take the personality test that Cambridge Analytica developed.

But for every article about Facebook’s creepy stalker behavior, thousands of other companies are breathing a collective sigh of relief that it’s Facebook and not them in the spotlight. Because while Facebook is one of the biggest players in this space, there are thousands of other companies that spy on and manipulate us for profit.

Harvard Business School professor Shoshana Zuboff calls it “surveillance capitalism.” And as creepy as Facebook is turning out to be, the entire industry is far creepier. It has existed in secret far too long, and it’s up to lawmakers to force these companies into the public spotlight, where we can all decide if this is how we want society to operate and — if not — what to do about it.

There are 2,500 to 4,000 data brokers in the United States whose business is buying and selling our personal data. Last year, Equifax was in the news when hackers stole personal information on 150 million people, including Social Security numbers, birth dates, addresses, and driver’s license numbers.

You certainly didn’t give it permission to collect any of that information. Equifax is one of those thousands of data brokers, most of them you’ve never heard of, selling your personal information without your knowledge or consent to pretty much anyone who will pay for it.

Surveillance capitalism takes this one step further. Companies like Facebook and Google offer you free services in exchange for your data. Google’s surveillance isn’t in the news, but it’s startlingly intimate. We never lie to our search engines. Our interests and curiosities, hopes and fears, desires and sexual proclivities, are all collected and saved. Add to that the websites we visit that Google tracks through its advertising network, our Gmail accounts, our movements via Google Maps, and what it can collect from our smartphones.

That phone is probably the most intimate surveillance device ever invented. It tracks our location continuously, so it knows where we live, where we work, and where we spend our time. It’s the first and last thing we check in a day, so it knows when we wake up and when we go to sleep. We all have one, so it knows who we sleep with. Uber used just some of that information to detect one-night stands; your smartphone provider and any app you allow to collect location data knows a lot more.

Surveillance capitalism drives much of the internet. It’s behind most of the “free” services, and many of the paid ones as well. Its goal is psychological manipulation, in the form of personalized advertising to persuade you to buy something or do something, like vote for a candidate. And while the individualized profile-driven manipulation exposed by Cambridge Analytica feels abhorrent, it’s really no different from what every company wants in the end. This is why all your personal information is collected, and this is why it is so valuable. Companies that can understand it can use it against you.

None of this is new. The media has been reporting on surveillance capitalism for years. In 2015, I wrote a book about it. Back in 2010, the Wall Street Journal published an award-winning two-year series about how people are tracked both online and offline, titled “What They Know.”

Surveillance capitalism is deeply embedded in our increasingly computerized society, and if the extent of it came to light there would be broad demands for limits and regulation. But because this industry can largely operate in secret, only occasionally exposed after a data breach or investigative report, we remain mostly ignorant of its reach.

This might change soon. In 2016, the European Union passed the comprehensive General Data Protection Regulation, or GDPR. The details of the law are far too complex to explain here, but some of the things it mandates are that personal data of EU citizens can only be collected and saved for “specific, explicit, and legitimate purposes,” and only with explicit consent of the user. Consent can’t be buried in the terms and conditions, nor can it be assumed unless the user opts in. This law will take effect in May, and companies worldwide are bracing for its enforcement.

Because pretty much all surveillance capitalism companies collect data on Europeans, this will expose the industry like nothing else. Here’s just one example. In preparation for this law, PayPal quietly published a list of over 600 companies it might share your personal data with. What will it be like when every company has to publish this sort of information, and explicitly explain how it’s using your personal data? We’re about to find out.

In the wake of this scandal, even Mark Zuckerberg said that his industry probably should be regulated, although he’s certainly not wishing for the sorts of comprehensive regulation the GDPR is bringing to Europe.

He’s right. Surveillance capitalism has operated without constraints for far too long. And advances in both big data analysis and artificial intelligence will make tomorrow’s applications far creepier than today’s. Regulation is the only answer.

The first step to any regulation is transparency. Who has our data? Is it accurate? What are they doing with it? Who are they selling it to? How are they securing it? Can we delete it? I don’t see any hope of Congress passing a GDPR-like data protection law anytime soon, but it’s not too far-fetched to demand laws requiring these companies to be more transparent in what they’re doing.

One of the responses to the Cambridge Analytica scandal is that people are deleting their Facebook accounts. It’s hard to do right, and doesn’t do anything about the data that Facebook collects about people who don’t use Facebook. But it’s a start. The market can put pressure on these companies to reduce their spying on us, but it can only do that if we force the industry out of its secret shadows.

This essay previously appeared on CNN.com.

EDITED TO ADD (4/2): Slashdot thread.

March Machine Learning Madness!

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/march-machine-learning-madness/

Mid-march in the USA means millions of people watching, and betting on, college basketball (I live here but I didn’t make the rules). As the NCAA college championship continues I wanted to briefly highlight the work of Wesley Pasfield one of our Professional Services Machine Learning Specialists. Wesley was able to take data from kenpom.com and College Basketball Reference to build a model predicting the outcome of March Madness using the Amazon SageMaker built-in XGBoost algorithm.

Wesley walks us through grabbing the data, performing an exploratory data analysis (EDA in the data science lingo), reshaping the data for the xgboost algorithm, using the SageMaker SDK to create a training job for two different models, and finally creating a SageMaker inference endpoint for serving predictions at https://cbbpredictions.com/. Check out part one of the post and part two.

Pretty cool right? Why not open the notebook and give the xgboost algorithm a try? Just know that there are a few caveats to the predictions so don’t go making your champion prediction just yet!

Randall

Artificial Intelligence and the Attack/Defense Balance

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/03/artificial_inte.html

Artificial intelligence technologies have the potential to upend the longstanding advantage that attack has over defense on the Internet. This has to do with the relative strengths and weaknesses of people and computers, how those all interplay in Internet security, and where AI technologies might change things.

You can divide Internet security tasks into two sets: what humans do well and what computers do well. Traditionally, computers excel at speed, scale, and scope. They can launch attacks in milliseconds and infect millions of computers. They can scan computer code to look for particular kinds of vulnerabilities, and data packets to identify particular kinds of attacks.

Humans, conversely, excel at thinking and reasoning. They can look at the data and distinguish a real attack from a false alarm, understand the attack as it’s happening, and respond to it. They can find new sorts of vulnerabilities in systems. Humans are creative and adaptive, and can understand context.

Computers — so far, at least — are bad at what humans do well. They’re not creative or adaptive. They don’t understand context. They can behave irrationally because of those things.

Humans are slow, and get bored at repetitive tasks. They’re terrible at big data analysis. They use cognitive shortcuts, and can only keep a few data points in their head at a time. They can also behave irrationally because of those things.

AI will allow computers to take over Internet security tasks from humans, and then do them faster and at scale. Here are possible AI capabilities:

  • Discovering new vulnerabilities­ — and, more importantly, new types of vulnerabilities­ in systems, both by the offense to exploit and by the defense to patch, and then automatically exploiting or patching them.
  • Reacting and adapting to an adversary’s actions, again both on the offense and defense sides. This includes reasoning about those actions and what they mean in the context of the attack and the environment.
  • Abstracting lessons from individual incidents, generalizing them across systems and networks, and applying those lessons to increase attack and defense effectiveness elsewhere.
  • Identifying strategic and tactical trends from large datasets and using those trends to adapt attack and defense tactics.

That’s an incomplete list. I don’t think anyone can predict what AI technologies will be capable of. But it’s not unreasonable to look at what humans do today and imagine a future where AIs are doing the same things, only at computer speeds, scale, and scope.

Both attack and defense will benefit from AI technologies, but I believe that AI has the capability to tip the scales more toward defense. There will be better offensive and defensive AI techniques. But here’s the thing: defense is currently in a worse position than offense precisely because of the human components. Present-day attacks pit the relative advantages of computers and humans against the relative weaknesses of computers and humans. Computers moving into what are traditionally human areas will rebalance that equation.

Roy Amara famously said that we overestimate the short-term effects of new technologies, but underestimate their long-term effects. AI is notoriously hard to predict, so many of the details I speculate about are likely to be wrong­ — and AI is likely to introduce new asymmetries that we can’t foresee. But AI is the most promising technology I’ve seen for bringing defense up to par with offense. For Internet security, that will change everything.

This essay previously appeared in the March/April 2018 issue of IEEE Security & Privacy.

2017 Weather Station round-up

Post Syndicated from Richard Hayler original https://www.raspberrypi.org/blog/2017-weather-station/

As we head into 2018 and start looking forward to longer days in the Northern hemisphere, I thought I’d take a look back at last year’s weather using data from Raspberry Pi Oracle Weather Stations. One of the great things about the kit is that as well as uploading all its readings to the shared online Oracle database, it stores them locally on the Pi in a MySQL or MariaDB database. This means you can use the power of SQL queries coupled with Python code to do automatic data analysis.

Soggy Surrey

My Weather Station has only been installed since May, so I didn’t have a full 52 weeks of my own data to investigate. Still, my station recorded more than 70000 measurements. Living in England, the first thing I wanted to know was: which was the wettest month? Unsurprisingly, both in terms of average daily rainfall and total rainfall, the start of the summer period — exactly when I went on a staycation — was the soggiest:

What about the global Weather Station community?

Even soggier Bavaria

Here things get slightly trickier. Although we have a shiny Oracle database full of all participating schools’ sensor readings, some of the data needs careful interpretation. Many kits are used as part of the school curriculum and do not always record genuine outdoor conditions. Nevertheless, it appears that Adalbert Stifter Gymnasium in Bavaria, Germany, had an even wetter 2017 than my home did:


View larger map

Where the wind blows

The records Robert-Dannemann Schule in Westerstede, Germany, is a good example of data which was most likely collected while testing and investigating the weather station sensors, rather than in genuine external conditions. Unless this school’s Weather Station was transported to a planet which suffers from extreme hurricanes, it wasn’t actually subjected to wind speeds above 1000km/h in November. Dismissing these and all similarly suspect records, I decided to award the ‘Windiest location of the year’ prize to CEIP Noalla-Telleiro, Spain.


View larger map

This school is right on the coast, and is subject to some strong and squally weather systems.

Weather Station at CEIP Noalla - Telleiro

Weather Station at CEIP Noalla-Telleiro

They’ve mounted their wind vane and anemometer nice and high, so I can see how they were able to record such high wind velocities.

A couple of Weather Stations have recently been commissioned in equally exposed places — it will be interesting to see whether they will record even higher speeds during 2018.

Highs and lows

After careful analysis and a few disqualifications (a couple of Weather Stations in contention for this category were housed indoors), the ‘Hottest location’ award went to High School of Chalastra in Thessaloniki, Greece. There were a couple of Weather Stations (the one at The Marwadi Education Foundation in India, for example) that reported higher average temperatures than Chalastra’s 24.54 ºC. However, they had uploaded far fewer readings and their data coverage of 2017 was only partial.


View larger map

At the other end of the thermometer, the location with the coldest average temperature is École de la Rose Sauvage in Calgary, Canada, with a very chilly 9.9 ºC.

Ecole de la Rose sauvage Weather Station

Weather Station at École de la Rose Sauvage

I suspect this school has a good chance of retaining the title: their lowest 2017 temperature of -24 ºC is likely to be beaten in 2018 due to extreme weather currently bringing a freezing start to the year in that part of the world.


View larger map

Analyse your own Weather Station data

If you have an Oracle Raspberry Pi Weather Station and would like to perform an annual review of your local data, you can use this Python script as a starting point. It will display a monthly summary of the temperature and rainfall for 2017, and you should be able to customise the code to focus on other sensor data or on a particular time of year. We’d love to see your results, so please share your findings with [email protected], and we’ll send you some limited-edition Weather Station stickers.

The post 2017 Weather Station round-up appeared first on Raspberry Pi.

Presenting AWS IoT Analytics: Delivering IoT Analytics at Scale and Faster than Ever Before

Post Syndicated from Tara Walker original https://aws.amazon.com/blogs/aws/launch-presenting-aws-iot-analytics/

One of the technology areas I thoroughly enjoy is the Internet of Things (IoT). Even as a child I used to infuriate my parents by taking apart the toys they would purchase for me to see how they worked and if I could somehow put them back together. It seems somehow I was destined to end up the tough and ever-changing world of technology. Therefore, it’s no wonder that I am really enjoying learning and tinkering with IoT devices and technologies. It combines my love of development and software engineering with my curiosity around circuits, controllers, and other facets of the electrical engineering discipline; even though an electrical engineer I can not claim to be.

Despite all of the information that is collected by the deployment of IoT devices and solutions, I honestly never really thought about the need to analyze, search, and process this data until I came up against a scenario where it became of the utmost importance to be able to search and query through loads of sensory data for an anomaly occurrence. Of course, I understood the importance of analytics for businesses to make accurate decisions and predictions to drive the organization’s direction. But it didn’t occur to me initially, how important it was to make analytics an integral part of my IoT solutions. Well, I learned my lesson just in time because this re:Invent a service is launching to make it easier for anyone to process and analyze IoT messages and device data.

 

Hello, AWS IoT Analytics!  AWS IoT Analytics is a fully managed service of AWS IoT that provides advanced data analysis of data collected from your IoT devices.  With the AWS IoT Analytics service, you can process messages, gather and store large amounts of device data, as well as, query your data. Also, the new AWS IoT Analytics service feature integrates with Amazon Quicksight for visualization of your data and brings the power of machine learning through integration with Jupyter Notebooks.

Benefits of AWS IoT Analytics

  • Helps with predictive analysis of data by providing access to pre-built analytical functions
  • Provides ability to visualize analytical output from service
  • Provides tools to clean up data
  • Can help identify patterns in the gathered data

Be In the Know: IoT Analytics Concepts

  • Channel: archives the raw, unprocessed messages and collects data from MQTT topics.
  • Pipeline: consumes messages from channels and allows message processing.
    • Activities: perform transformations on your messages including filtering attributes and invoking lambda functions advanced processing.
  • Data Store: Used as a queryable repository for processed messages. Provide ability to have multiple datastores for messages coming from different devices or locations or filtered by message attributes.
  • Data Set: Data retrieval view from a data store, can be generated by a recurring schedule. 

Getting Started with AWS IoT Analytics

First, I’ll create a channel to receive incoming messages.  This channel can be used to ingest data sent to the channel via MQTT or messages directed from the Rules Engine. To create a channel, I’ll select the Channels menu option and then click the Create a channel button.

I’ll name my channel, TaraIoTAnalyticsID and give the Channel a MQTT topic filter of Temperature. To complete the creation of my channel, I will click the Create Channel button.

Now that I have my Channel created, I need to create a Data Store to receive and store the messages received on the Channel from my IoT device. Remember you can set up multiple Data Stores for more complex solution needs, but I’ll just create one Data Store for my example. I’ll select Data Stores from menu panel and click Create a data store.

 

I’ll name my Data Store, TaraDataStoreID, and once I click the Create the data store button and I would have successfully set up a Data Store to house messages coming from my Channel.

Now that I have my Channel and my Data Store, I will need to connect the two using a Pipeline. I’ll create a simple pipeline that just connects my Channel and Data Store, but you can create a more robust pipeline to process and filter messages by adding Pipeline activities like a Lambda activity.

To create a pipeline, I’ll select the Pipelines menu option and then click the Create a pipeline button.

I will not add an Attribute for this pipeline. So I will click Next button.

As we discussed there are additional pipeline activities that I can add to my pipeline for the processing and transformation of messages but I will keep my first pipeline simple and hit the Next button.

The final step in creating my pipeline is for me to select my previously created Data Store and click Create Pipeline.

All that is left for me to take advantage of the AWS IoT Analytics service is to create an IoT rule that sends data to an AWS IoT Analytics channel.  Wow, that was a super easy process to set up analytics for IoT devices.

If I wanted to create a Data Set as a result of queries run against my data for visualization with Amazon Quicksight or integrate with Jupyter Notebooks to perform more advanced analytical functions, I can choose the Analyze menu option to bring up the screens to create data sets and access the Juypter Notebook instances.

Summary

As you can see, it was a very simple process to set up the advanced data analysis for AWS IoT. With AWS IoT Analytics, you have the ability to collect, visualize, process, query and store large amounts of data generated from your AWS IoT connected device. Additionally, you can access the AWS IoT Analytics service in a myriad of different ways; the AWS Command Line Interface (AWS CLI), the AWS IoT API, language-specific AWS SDKs, and AWS IoT Device SDKs.

AWS IoT Analytics is available today for you to dig into the analysis of your IoT data. To learn more about AWS IoT and AWS IoT Analytics go to the AWS IoT Analytics product page and/or the AWS IoT documentation.

Tara

New AWS Training: Building a Serverless Data Lake

Post Syndicated from Sara Snedeker original https://aws.amazon.com/blogs/big-data/new-aws-training-building-a-serverless-data-lake/

AWS Training allows you to learn from the experts so that you can advance your knowledge with practical skills and get more out of the AWS Cloud. We are adding one of our most popular event boot camps, Building a Serverless Data Lake, to our permanent instructor-led training portfolio.

This one-day course is designed to teach you how to design, build, and operate a serverless data lake solution with AWS services. We cover topics such as ingesting data from any data source at large scale, storing the data securely and durably, enabling the capability to use the right tool to process large volumes of data, and understanding the options available for analyzing the data in near-real time.

This course is intended for solution architects, big data developers, data architects and analysts, and other hands-on data analysis practitioners.

You can explore our complete course catalog, or search for a public class near you. You can also request a private onsite training for your team by contacting AWS Training.

 

CyberChef – Cyber Swiss Army Knife

Post Syndicated from Darknet original http://feedproxy.google.com/~r/darknethackers/~3/SOhld_nebGs/

CyberChef is a simple, intuitive web app for carrying out all manner of “cyber” operations within a web browser. These operations include simple encoding like XOR or Base64, more complex encryption like AES, DES and Blowfish, creating binary and hexdumps, compression and decompression of data, calculating hashes and checksums, IPv6 and X.509…

Read the full post at darknet.org.uk

Book Review: Twitter and Tear Gas, by Zeynep Tufekci

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/07/book_review_twi.html

There are two opposing models of how the Internet has changed protest movements. The first is that the Internet has made protesters mightier than ever. This comes from the successful revolutions in Tunisia (2010-11), Egypt (2011), and Ukraine (2013). The second is that it has made them more ineffectual. Derided as “slacktivism” or “clicktivism,” the ease of action without commitment can result in movements like Occupy petering out in the US without any obvious effects. Of course, the reality is more nuanced, and Zeynep Tufekci teases that out in her new book Twitter and Tear Gas.

Tufekci is a rare interdisciplinary figure. As a sociologist, programmer, and ethnographer, she studies how technology shapes society and drives social change. She has a dual appointment in both the School of Information Science and the Department of Sociology at University of North Carolina at Chapel Hill, and is a Faculty Associate at the Berkman Klein Center for Internet and Society at Harvard University. Her regular New York Times column on the social impacts of technology is a must-read.

Modern Internet-fueled protest movements are the subjects of Twitter and Tear Gas. As an observer, writer, and participant, Tufekci examines how modern protest movements have been changed by the Internet­ — and what that means for protests going forward. Her book combines her own ethnographic research and her usual deft analysis, with the research of others and some big data analysis from social media outlets. The result is a book that is both insightful and entertaining, and whose lessons are much broader than the book’s central topic.

“The Power and Fragility of Networked Protest” is the book’s subtitle. The power of the Internet as a tool for protest is obvious: it gives people newfound abilities to quickly organize and scale. But, according to Tufekci, it’s a mistake to judge modern protests using the same criteria we used to judge pre-Internet protests. The 1963 March on Washington might have culminated in hundreds of thousands of people listening to Martin Luther King Jr. deliver his “I Have a Dream” speech, but it was the culmination of a multi-year protest effort and the result of six months of careful planning made possible by that sustained effort. The 2011 protests in Cairo came together in mere days because they could be loosely coordinated on Facebook and Twitter.

That’s the power. Tufekci describes the fragility by analogy. Nepalese Sherpas assist Mt. Everest climbers by carrying supplies, laying out ropes and ladders, and so on. This means that people with limited training and experience can make the ascent, which is no less dangerous — to sometimes disastrous results. Says Tufekci: “The Internet similarly allows networked movements to grow dramatically and rapidly, but without prior building of formal or informal organizational and other collective capacities that could prepare them for the inevitable challenges they will face and give them the ability to respond to what comes next.” That makes them less able to respond to government counters, change their tactics­ — a phenomenon Tufekci calls “tactical freeze” — make movement-wide decisions, and survive over the long haul.

Tufekci isn’t arguing that modern protests are necessarily less effective, but that they’re different. Effective movements need to understand these differences, and leverage these new advantages while minimizing the disadvantages.

To that end, she develops a taxonomy for talking about social movements. Protests are an example of a “signal” that corresponds to one of several underlying “capacities.” There’s narrative capacity: the ability to change the conversation, as Black Lives Matter did with police violence and Occupy did with wealth inequality. There’s disruptive capacity: the ability to stop business as usual. An early Internet example is the 1999 WTO protests in Seattle. And finally, there’s electoral or institutional capacity: the ability to vote, lobby, fund raise, and so on. Because of various “affordances” of modern Internet technologies, particularly social media, the same signal — a protest of a given size — reflects different underlying capacities.

This taxonomy also informs government reactions to protest movements. Smart responses target attention as a resource. The Chinese government responded to 2015 protesters in Hong Kong by not engaging with them at all, denying them camera-phone videos that would go viral and attract the world’s attention. Instead, they pulled their police back and waited for the movement to die from lack of attention.

If this all sounds dry and academic, it’s not. Twitter and Tear Gasis infused with a richness of detail stemming from her personal participation in the 2013 Gezi Park protests in Turkey, as well as personal on-the-ground interviews with protesters throughout the Middle East — particularly Egypt and her native Turkey — Zapatistas in Mexico, WTO protesters in Seattle, Occupy participants worldwide, and others. Tufekci writes with a warmth and respect for the humans that are part of these powerful social movements, gently intertwining her own story with the stories of others, big data, and theory. She is adept at writing for a general audience, and­despite being published by the intimidating Yale University Press — her book is more mass-market than academic. What rigor is there is presented in a way that carries readers along rather than distracting.

The synthesist in me wishes Tufekci would take some additional steps, taking the trends she describes outside of the narrow world of political protest and applying them more broadly to social change. Her taxonomy is an important contribution to the more-general discussion of how the Internet affects society. Furthermore, her insights on the networked public sphere has applications for understanding technology-driven social change in general. These are hard conversations for society to have. We largely prefer to allow technology to blindly steer society or — in some ways worse — leave it to unfettered for-profit corporations. When you’re reading Twitter and Tear Gas, keep current and near-term future technological issues such as ubiquitous surveillance, algorithmic discrimination, and automation and employment in mind. You’ll come away with new insights.

Tufekci twice quotes historian Melvin Kranzberg from 1985: “Technology is neither good nor bad; nor is it neutral.” This foreshadows her central message. For better or worse, the technologies that power the networked public sphere have changed the nature of political protest as well as government reactions to and suppressions of such protest.

I have long characterized our technological future as a battle between the quick and the strong. The quick — dissidents, hackers, criminals, marginalized groups — are the first to make use of a new technology to magnify their power. The strong are slower, but have more raw power to magnify. So while protesters are the first to use Facebook to organize, the governments eventually figure out how to use Facebook to track protesters. It’s still an open question who will gain the upper hand in the long term, but Tufekci’s book helps us understand the dynamics at work.

This essay originally appeared on Vice Motherboard.

The book on Amazon.com.

Analyze OpenFDA Data in R with Amazon S3 and Amazon Athena

Post Syndicated from Ryan Hood original https://aws.amazon.com/blogs/big-data/analyze-openfda-data-in-r-with-amazon-s3-and-amazon-athena/

One of the great benefits of Amazon S3 is the ability to host, share, or consume public data sets. This provides transparency into data to which an external data scientist or developer might not normally have access. By exposing the data to the public, you can glean many insights that would have been difficult with a data silo.

The openFDA project creates easy access to the high value, high priority, and public access data of the Food and Drug Administration (FDA). The data has been formatted and documented in consumer-friendly standards. Critical data related to drugs, devices, and food has been harmonized and can easily be called by application developers and researchers via API calls. OpenFDA has published two whitepapers that drill into the technical underpinnings of the API infrastructure as well as how to properly analyze the data in R. In addition, FDA makes openFDA data available on S3 in raw format.

In this post, I show how to use S3, Amazon EMR, and Amazon Athena to analyze the drug adverse events dataset. A drug adverse event is an undesirable experience associated with the use of a drug, including serious drug side effects, product use errors, product quality programs, and therapeutic failures.

Data considerations

Keep in mind that this data does have limitations. In addition, in the United States, these adverse events are submitted to the FDA voluntarily from consumers so there may not be reports for all events that occurred. There is no certainty that the reported event was actually due to the product. The FDA does not require that a causal relationship between a product and event be proven, and reports do not always contain the detail necessary to evaluate an event. Because of this, there is no way to identify the true number of events. The important takeaway to all this is that the information contained in this data has not been verified to produce cause and effect relationships. Despite this disclaimer, many interesting insights and value can be derived from the data to accelerate drug safety research.

Data analysis using SQL

For application developers who want to perform targeted searching and lookups, the API endpoints provided by the openFDA project are “ready to go” for software integration using a standard API powered by Elasticsearch, NodeJS, and Docker. However, for data analysis purposes, it is often easier to work with the data using SQL and statistical packages that expect a SQL table structure. For large-scale analysis, APIs often have query limits, such as 5000 records per query. This can cause extra work for data scientists who want to analyze the full dataset instead of small subsets of data.

To address the concern of requiring all the data in a single dataset, the openFDA project released the full 100 GB of harmonized data files that back the openFDA project onto S3. Athena is an interactive query service that makes it easy to analyze data in S3 using standard SQL. It’s a quick and easy way to answer your questions about adverse events and aspirin that does not require you to spin up databases or servers.

While you could point tools directly at the openFDA S3 files, you can find greatly improved performance and use of the data by following some of the preparation steps later in this post.

Architecture

This post explains how to use the following architecture to take the raw data provided by openFDA, leverage several AWS services, and derive meaning from the underlying data.

Steps:

  1. Load the openFDA /drug/event dataset into Spark and convert it to gzip to allow for streaming.
  2. Transform the data in Spark and save the results as a Parquet file in S3.
  3. Query the S3 Parquet file with Athena.
  4. Perform visualization and analysis of the data in R and Python on Amazon EC2.

Optimizing public data sets: A primer on data preparation

Those who want to jump right into preparing the files for Athena may want to skip ahead to the next section.

Transforming, or pre-processing, files is a common task for using many public data sets. Before you jump into the specific steps for transforming the openFDA data files into a format optimized for Athena, I thought it would be worthwhile to provide a quick exploration on the problem.

Making a dataset in S3 efficiently accessible with minimal transformation for the end user has two key elements:

  1. Partitioning the data into objects that contain a complete part of the data (such as data created within a specific month).
  2. Using file formats that make it easy for applications to locate subsets of data (for example, gzip, Parquet, ORC, etc.).

With these two key elements in mind, you can now apply transformations to the openFDA adverse event data to prepare it for Athena. You might find the data techniques employed in this post to be applicable to many of the questions you might want to ask of the public data sets stored in Amazon S3.

Before you get started, I encourage those who are interested in doing deeper healthcare analysis on AWS to make sure that you first read the AWS HIPAA Compliance whitepaper. This covers the information necessary for processing and storing patient health information (PHI).

Also, the adverse event analysis shown for aspirin is strictly for demonstration purposes and should not be used for any real decision or taken as anything other than a demonstration of AWS capabilities. However, there have been robust case studies published that have explored a causal relationship between aspirin and adverse reactions using OpenFDA data. If you are seeking research on aspirin or its risks, visit organizations such as the Centers for Disease Control and Prevention (CDC) or the Institute of Medicine (IOM).

Preparing data for Athena

For this walkthrough, you will start with the FDA adverse events dataset, which is stored as JSON files within zip archives on S3. You then convert it to Parquet for analysis. Why do you need to convert it? The original data download is stored in objects that are partitioned by quarter.

Here is a small sample of what you find in the adverse events (/drugs/event) section of the openFDA website.

If you were looking for events that happened in a specific quarter, this is not a bad solution. For most other scenarios, such as looking across the full history of aspirin events, it requires you to access a lot of data that you won’t need. The zip file format is not ideal for using data in place because zip readers must have random access to the file, which means the data can’t be streamed. Additionally, the zip files contain large JSON objects.

To read the data in these JSON files, a streaming JSON decoder must be used or a computer with a significant amount of RAM must decode the JSON. Opening up these files for public consumption is a great start. However, you still prepare the data with a few lines of Spark code so that the JSON can be streamed.

Step 1:  Convert the file types

Using Apache Spark on EMR, you can extract all of the zip files and pull out the events from the JSON files. To do this, use the Scala code below to deflate the zip file and create a text file. In addition, compress the JSON files with gzip to improve Spark’s performance and reduce your overall storage footprint. The Scala code can be run in either the Spark Shell or in an Apache Zeppelin notebook on your EMR cluster.

If you are unfamiliar with either Apache Zeppelin or the Spark Shell, the following posts serve as great references:

 

import scala.io.Source
import java.util.zip.ZipInputStream
import org.apache.spark.input.PortableDataStream
import org.apache.hadoop.io.compress.GzipCodec

// Input Directory
val inputFile = "s3://download.open.fda.gov/drug/event/2015q4/*.json.zip";

// Output Directory
val outputDir = "s3://{YOUR OUTPUT BUCKET HERE}/output/2015q4/";

// Extract zip files from 
val zipFiles = sc.binaryFiles(inputFile);

// Process zip file to extract the json as text file and save it
// in the output directory 
val rdd = zipFiles.flatMap((file: (String, PortableDataStream)) => {
    val zipStream = new ZipInputStream(file.2.open)
    val entry = zipStream.getNextEntry
    val iter = Source.fromInputStream(zipStream).getLines
    iter
}).map(.replaceAll("\s+","")).saveAsTextFile(outputDir, classOf[GzipCodec])

Step 2:  Transform JSON into Parquet

With just a few more lines of Scala code, you can use Spark’s abstractions to convert the JSON into a Spark DataFrame and then export the data back to S3 in Parquet format.

Spark requires the JSON to be in JSON Lines format to be parsed correctly into a DataFrame.

// Output Parquet directory
val outputDir = "s3://{YOUR OUTPUT BUCKET NAME}/output/drugevents"
// Input json file
val inputJson = "s3://{YOUR OUTPUT BUCKET NAME}/output/2015q4/*”
// Load dataframe from json file multiline 
val df = spark.read.json(sc.wholeTextFiles(inputJson).values)
// Extract results from dataframe
val results = df.select("results")
// Save it to Parquet
results.write.parquet(outputDir)

Step 3:  Create an Athena table

With the data cleanly prepared and stored in S3 using the Parquet format, you can now place an Athena table on top of it to get a better understanding of the underlying data.

Because the openFDA data structure incorporates several layers of nesting, it can be a complex process to try to manually derive the underlying schema in a Hive-compatible format. To shorten this process, you can load the top row of the DataFrame from the previous step into a Hive table within Zeppelin and then extract the “create  table” statement from SparkSQL.

results.createOrReplaceTempView("data")

val top1 = spark.sql("select * from data tablesample(1 rows)")

top1.write.format("parquet").mode("overwrite").saveAsTable("drugevents")

val show_cmd = spark.sql("show create table drugevents”).show(1, false)

This returns a “create table” statement that you can almost paste directly into the Athena console. Make some small modifications (adding the word “external” and replacing “using with “stored as”), and then execute the code in the Athena query editor. The table is created.

For the openFDA data, the DDL returns all string fields, as the date format used in your dataset does not conform to the yyy-mm-dd hh:mm:ss[.f…] format required by Hive. For your analysis, the string format works appropriately but it would be possible to extend this code to use a Presto function to convert the strings into time stamps.

CREATE EXTERNAL TABLE  drugevents (
   companynumb  string, 
   safetyreportid  string, 
   safetyreportversion  string, 
   receiptdate  string, 
   patientagegroup  string, 
   patientdeathdate  string, 
   patientsex  string, 
   patientweight  string, 
   serious  string, 
   seriousnesscongenitalanomali  string, 
   seriousnessdeath  string, 
   seriousnessdisabling  string, 
   seriousnesshospitalization  string, 
   seriousnesslifethreatening  string, 
   seriousnessother  string, 
   actiondrug  string, 
   activesubstancename  string, 
   drugadditional  string, 
   drugadministrationroute  string, 
   drugcharacterization  string, 
   drugindication  string, 
   drugauthorizationnumb  string, 
   medicinalproduct  string, 
   drugdosageform  string, 
   drugdosagetext  string, 
   reactionoutcome  string, 
   reactionmeddrapt  string, 
   reactionmeddraversionpt  string)
STORED AS parquet
LOCATION
  's3://{YOUR TARGET BUCKET}/output/drugevents'

With the Athena table in place, you can start to explore the data by running ad hoc queries within Athena or doing more advanced statistical analysis in R.

Using SQL and R to analyze adverse events

Using the openFDA data with Athena makes it very easy to translate your questions into SQL code and perform quick analysis on the data. After you have prepared the data for Athena, you can begin to explore the relationship between aspirin and adverse drug events, as an example. One of the most common metrics to measure adverse drug events is the Proportional Reporting Ratio (PRR). It is defined as:

PRR = (m/n)/( (M-m)/(N-n) )
Where
m = #reports with drug and event
n = #reports with drug
M = #reports with event in database
N = #reports in database

Gastrointestinal haemorrhage has the highest PRR of any reaction to aspirin when viewed in aggregate. One question you may want to ask is how the PRR has trended on a yearly basis for gastrointestinal haemorrhage since 2005.

Using the following query in Athena, you can see the PRR trend of “GASTROINTESTINAL HAEMORRHAGE” reactions with “ASPIRIN” since 2005:

with drug_and_event as 
(select rpad(receiptdate, 4, 'NA') as receipt_year
    , reactionmeddrapt
    , count(distinct (concat(safetyreportid,receiptdate,reactionmeddrapt))) as reports_with_drug_and_event 
from fda.drugevents
where rpad(receiptdate,4,'NA') 
     between '2005' and '2015' 
     and medicinalproduct = 'ASPIRIN'
     and reactionmeddrapt= 'GASTROINTESTINAL HAEMORRHAGE'
group by reactionmeddrapt, rpad(receiptdate, 4, 'NA') 
), reports_with_drug as 
(
select rpad(receiptdate, 4, 'NA') as receipt_year
    , count(distinct (concat(safetyreportid,receiptdate,reactionmeddrapt))) as reports_with_drug 
 from fda.drugevents 
 where rpad(receiptdate,4,'NA') 
     between '2005' and '2015' 
     and medicinalproduct = 'ASPIRIN'
group by rpad(receiptdate, 4, 'NA') 
), reports_with_event as 
(
   select rpad(receiptdate, 4, 'NA') as receipt_year
    , count(distinct (concat(safetyreportid,receiptdate,reactionmeddrapt))) as reports_with_event 
   from fda.drugevents
   where rpad(receiptdate,4,'NA') 
     between '2005' and '2015' 
     and reactionmeddrapt= 'GASTROINTESTINAL HAEMORRHAGE'
   group by rpad(receiptdate, 4, 'NA')
), total_reports as 
(
   select rpad(receiptdate, 4, 'NA') as receipt_year
    , count(distinct (concat(safetyreportid,receiptdate,reactionmeddrapt))) as total_reports 
   from fda.drugevents
   where rpad(receiptdate,4,'NA') 
     between '2005' and '2015' 
   group by rpad(receiptdate, 4, 'NA')
)
select  drug_and_event.receipt_year, 
(1.0 * drug_and_event.reports_with_drug_and_event/reports_with_drug.reports_with_drug)/ (1.0 * (reports_with_event.reports_with_event- drug_and_event.reports_with_drug_and_event)/(total_reports.total_reports-reports_with_drug.reports_with_drug)) as prr
, drug_and_event.reports_with_drug_and_event
, reports_with_drug.reports_with_drug
, reports_with_event.reports_with_event
, total_reports.total_reports
from drug_and_event
    inner join reports_with_drug on  drug_and_event.receipt_year = reports_with_drug.receipt_year   
    inner join reports_with_event on  drug_and_event.receipt_year = reports_with_event.receipt_year
    inner join total_reports on  drug_and_event.receipt_year = total_reports.receipt_year
order by  drug_and_event.receipt_year


One nice feature of Athena is that you can quickly connect to it via R or any other tool that can use a JDBC driver to visualize the data and understand it more clearly.

With this quick R script that can be run in R Studio either locally or on an EC2 instance, you can create a visualization of the PRR and Reporting Odds Ratio (RoR) for “GASTROINTESTINAL HAEMORRHAGE” reactions from “ASPIRIN” since 2005 to better understand these trends.

# connect to ATHENA
conn <- dbConnect(drv, '<Your JDBC URL>',s3_staging_dir="<Your S3 Location>",user=Sys.getenv(c("USER_NAME"),password=Sys.getenv(c("USER_PASSWORD"))

# Declare Adverse Event
adverseEvent <- "'GASTROINTESTINAL HAEMORRHAGE'"

# Build SQL Blocks
sqlFirst <- "SELECT rpad(receiptdate, 4, 'NA') as receipt_year, count(DISTINCT safetyreportid) as event_count FROM fda.drugsflat WHERE rpad(receiptdate,4,'NA') between '2005' and '2015'"
sqlEnd <- "GROUP BY rpad(receiptdate, 4, 'NA') ORDER BY receipt_year"

# Extract Aspirin with adverse event counts
sql <- paste(sqlFirst,"AND medicinalproduct ='ASPIRIN' AND reactionmeddrapt=",adverseEvent, sqlEnd,sep=" ")
aspirinAdverseCount = dbGetQuery(conn,sql)

# Extract Aspirin counts
sql <- paste(sqlFirst,"AND medicinalproduct ='ASPIRIN'", sqlEnd,sep=" ")
aspirinCount = dbGetQuery(conn,sql)

# Extract adverse event counts
sql <- paste(sqlFirst,"AND reactionmeddrapt=",adverseEvent, sqlEnd,sep=" ")
adverseCount = dbGetQuery(conn,sql)

# All Drug Adverse event Counts
sql <- paste(sqlFirst, sqlEnd,sep=" ")
allDrugCount = dbGetQuery(conn,sql)

# Select correct rows
selAll =  allDrugCount$receipt_year == aspirinAdverseCount$receipt_year
selAspirin = aspirinCount$receipt_year == aspirinAdverseCount$receipt_year
selAdverse = adverseCount$receipt_year == aspirinAdverseCount$receipt_year

# Calculate Numbers
m <- c(aspirinAdverseCount$event_count)
n <- c(aspirinCount[selAspirin,2])
M <- c(adverseCount[selAdverse,2])
N <- c(allDrugCount[selAll,2])

# Calculate proptional reporting ratio
PRR = (m/n)/((M-m)/(N-n))

# Calculate reporting Odds Ratio
d = n-m
D = N-M
ROR = (m/d)/(M/D)

# Plot the PRR and ROR
g_range <- range(0, PRR,ROR)
g_range[2] <- g_range[2] + 3
yearLen = length(aspirinAdverseCount$receipt_year)
axis(1,1:yearLen,lab=ax)
plot(PRR, type="o", col="blue", ylim=g_range,axes=FALSE, ann=FALSE)
axis(1,1:yearLen,lab=ax)
axis(2, las=1, at=1*0:g_range[2])
box()
lines(ROR, type="o", pch=22, lty=2, col="red")

As you can see, the PRR and RoR have both remained fairly steady over this time range. With the R Script above, all you need to do is change the adverseEvent variable from GASTROINTESTINAL HAEMORRHAGE to another type of reaction to analyze and compare those trends.

Summary

In this walkthrough:

  • You used a Scala script on EMR to convert the openFDA zip files to gzip.
  • You then transformed the JSON blobs into flattened Parquet files using Spark on EMR.
  • You created an Athena DDL so that you could query these Parquet files residing in S3.
  • Finally, you pointed the R package at the Athena table to analyze the data without pulling it into a database or creating your own servers.

If you have questions or suggestions, please comment below.


Next Steps

Take your skills to the next level. Learn how to optimize Amazon S3 for an architecture commonly used to enable genomic data analysis. Also, be sure to read more about running R on Amazon Athena.

 

 

 

 

 


About the Authors

Ryan Hood is a Data Engineer for AWS. He works on big data projects leveraging the newest AWS offerings. In his spare time, he enjoys watching the Cubs win the World Series and attempting to Sous-vide anything he can find in his refrigerator.

 

 

Vikram Anand is a Data Engineer for AWS. He works on big data projects leveraging the newest AWS offerings. In his spare time, he enjoys playing soccer and watching the NFL & European Soccer leagues.

 

 

Dave Rocamora is a Solutions Architect at Amazon Web Services on the Open Data team. Dave is based in Seattle and when he is not opening data, he enjoys biking and drinking coffee outside.

 

 

 

 

Perform Near Real-time Analytics on Streaming Data with Amazon Kinesis and Amazon Elasticsearch Service

Post Syndicated from Tristan Li original https://aws.amazon.com/blogs/big-data/perform-near-real-time-analytics-on-streaming-data-with-amazon-kinesis-and-amazon-elasticsearch-service/

Nowadays, streaming data is seen and used everywhere—from social networks, to mobile and web applications, IoT devices, instrumentation in data centers, and many other sources. As the speed and volume of this type of data increases, the need to perform data analysis in real time with machine learning algorithms and extract a deeper understanding from the data becomes ever more important. For example, you might want a continuous monitoring system to detect sentiment changes in a social media feed so that you can react to the sentiment in near real time.

In this post, we use Amazon Kinesis Streams to collect and store streaming data. We then use Amazon Kinesis Analytics to process and analyze the streaming data continuously. Specifically, we use the Kinesis Analytics built-in RANDOM_CUT_FOREST function, a machine learning algorithm, to detect anomalies in the streaming data. Finally, we use Amazon Kinesis Firehose to export the anomalies data to Amazon Elasticsearch Service (Amazon ES). We then build a simple dashboard in the open source tool Kibana to visualize the result.

Solution overview

The following diagram depicts a high-level overview of this solution.

Amazon Kinesis Streams

You can use Amazon Kinesis Streams to build your own streaming application. This application can process and analyze streaming data by continuously capturing and storing terabytes of data per hour from hundreds of thousands of sources.

Amazon Kinesis Analytics

Kinesis Analytics provides an easy and familiar standard SQL language to analyze streaming data in real time. One of its most powerful features is that there are no new languages, processing frameworks, or complex machine learning algorithms that you need to learn.

Amazon Kinesis Firehose

Kinesis Firehose is the easiest way to load streaming data into AWS. It can capture, transform, and load streaming data into Amazon S3, Amazon Redshift, and Amazon Elasticsearch Service.

Amazon Elasticsearch Service

Amazon ES is a fully managed service that makes it easy to deploy, operate, and scale Elasticsearch for log analytics, full text search, application monitoring, and more.

Solution summary

The following is a quick walkthrough of the solution that’s presented in the diagram:

  1. IoT sensors send streaming data into Kinesis Streams. In this post, you use a Python script to simulate an IoT temperature sensor device that sends the streaming data.
  2. By using the built-in RANDOM_CUT_FOREST function in Kinesis Analytics, you can detect anomalies in real time with the sensor data that is stored in Kinesis Streams. RANDOM_CUT_FOREST is also an appropriate algorithm for many other kinds of anomaly-detection use cases—for example, the media sentiment example mentioned earlier in this post.
  3. The processed anomaly data is then loaded into the Kinesis Firehose delivery stream.
  4. By using the built-in integration that Kinesis Firehose has with Amazon ES, you can easily export the processed anomaly data into the service and visualize it with Kibana.

Implementation steps

The following sections walk through the implementation steps in detail.

Creating the delivery stream

  1. Open the Amazon Kinesis Streams console.
  2. Create a new Kinesis stream. Give it a name that indicates it’s for raw incoming stream data—for example, RawStreamData. For Number of shards, type 1.
  3. The Python code provided below simulates a streaming application, such as an IoT device, and generates random data and anomalies into a Kinesis stream. The code generates two temperature ranges, where the first range is the hypothetical sensor’s normal operating temperature range (10–20), and the second is the anomaly temperature range (100–120).Make sure to change the stream name on line 16 and 20 and the Region on line 6 to match your configuration. Alternatively, you can download the Amazon Kinesis Data Generator from this repository and use it to generate the data.
    import json
    import datetime
    import random
    import testdata
    from boto import kinesis
    
    kinesis = kinesis.connect_to_region("us-east-1")
    
    def getData(iotName, lowVal, highVal):
       data = {}
       data["iotName"] = iotName
       data["iotValue"] = random.randint(lowVal, highVal) 
       return data
    
    while 1:
       rnd = random.random()
       if (rnd < 0.01):
          data = json.dumps(getData("DemoSensor", 100, 120))  
          kinesis.put_record("RawStreamData", data, "DemoSensor")
          print '***************************** anomaly ************************* ' + data
       else:
          data = json.dumps(getData("DemoSensor", 10, 20))  
          kinesis.put_record("RawStreamData", data, "DemoSensor")
          print data

  4. Open the Amazon Elasticsearch Service console and create a new domain.
    1. Give the domain a unique name. In the Configure cluster screen, use the default settings.
    2. In the Set up access policy screen, in the Set the domain access policy list, choose Allow access to the domain from specific IP(s).
    3. Enter the public IP address of your computer.
      Note: If you’re working behind a proxy or firewall, see the “Use a proxy to simplify request signing” section in this AWS Database blog post to learn how to work with a proxy. For additional information about securing access to your Amazon ES domain, see How to Control Access to Your Amazon Elasticsearch Domain in the AWS Security Blog.
  5. After the Amazon ES domain is up and running, you can set up and configure Kinesis Firehose to export results to Amazon ES:
    1. Open the Amazon Kinesis Firehose console and choose Create Delivery Stream.
    2. In the Destination dropdown list, choose Amazon Elasticsearch Service.
    3. Type a stream name, and choose the Amazon ES domain that you created in Step 4.
    4. Provide an index name and ES type. In the S3 bucket dropdown list, choose Create New S3 bucket. Choose Next.
    5. In the configuration, change the Elasticsearch Buffer size to 1 MB and the Buffer interval to 60s. Use the default settings for all other fields. This shortens the time for the data to reach the ES cluster.
    6. Under IAM Role, choose Create/Update existing IAM role.
      The best practice is to create a new role every time. Otherwise, the console keeps adding policy documents to the same role. Eventually the size of the attached policies causes IAM to reject the role, but it does it in a non-obvious way, where the console basically quits functioning.
    7. Choose Next to move to the Review page.
  6. Review the configuration, and then choose Create Delivery Stream.
  7. Run the Python file for 1–2 minutes, and then press Ctrl+C to stop the execution. This loads some data into the stream for you to visualize in the next step.

Analyzing the data

Now it’s time to analyze the IoT streaming data using Amazon Kinesis Analytics.

  1. Open the Amazon Kinesis Analytics console and create a new application. Give the application a name, and then choose Create Application.
  2. On the next screen, choose Connect to a source. Choose the raw incoming data stream that you created earlier. (Note the stream name Source_SQL_STREAM_001 because you will need it later.)
  3. Use the default settings for everything else. When the schema discovery process is complete, it displays a success message with the formatted stream sample in a table as shown in the following screenshot. Review the data, and then choose Save and continue.
  4. Next, choose Go to SQL editor. When prompted, choose Yes, start application.
  5. Copy the following SQL code and paste it into the SQL editor window.
    CREATE OR REPLACE STREAM "TEMP_STREAM" (
       "iotName"        varchar (40),
       "iotValue"   integer,
       "ANOMALY_SCORE"  DOUBLE);
    -- Creates an output stream and defines a schema
    CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (
       "iotName"       varchar(40),
       "iotValue"       integer,
       "ANOMALY_SCORE"  DOUBLE,
       "created" TimeStamp);
     
    -- Compute an anomaly score for each record in the source stream
    -- using Random Cut Forest
    CREATE OR REPLACE PUMP "STREAM_PUMP_1" AS INSERT INTO "TEMP_STREAM"
    SELECT STREAM "iotName", "iotValue", ANOMALY_SCORE FROM
      TABLE(RANDOM_CUT_FOREST(
        CURSOR(SELECT STREAM * FROM "SOURCE_SQL_STREAM_001")
      )
    );
    
    -- Sort records by descending anomaly score, insert into output stream
    CREATE OR REPLACE PUMP "OUTPUT_PUMP" AS INSERT INTO "DESTINATION_SQL_STREAM"
    SELECT STREAM "iotName", "iotValue", ANOMALY_SCORE, ROWTIME FROM "TEMP_STREAM"
    ORDER BY FLOOR("TEMP_STREAM".ROWTIME TO SECOND), ANOMALY_SCORE DESC;

 

  1. Choose Save and run SQL.
    As the application is running, it displays the results as stream data arrives. If you don’t see any data coming in, run the Python script again to generate some fresh data. When there is data, it appears in a grid as shown in the following screenshot.Note that you are selecting data from the source stream name Source_SQL_STREAM_001 that you created previously. Also note the ANOMALY_SCORE column. This is the value that the Random_Cut_Forest function calculates based on the temperature ranges provided by the Python script. Higher (anomaly) temperature ranges have a higher score.Looking at the SQL code, note that the first two blocks of code create two new streams to store temporary data and the final result. The third block of code analyzes the raw source data (Stream_Pump_1) using the Random_Cut_Forest function. It calculates an anomaly score (ANOMALY_SCORE) and inserts it into the TEMP_STREAM stream. The final code block loads the result stored in the TEMP_STREAM into DESTINATION_SQL_STREAM.
  2. Choose Exit (done editing) next to the Save and run SQL button to return to the application configuration page.

Load processed data into the Kinesis Firehose delivery stream

Now, you can export the result from DESTINATION_SQL_STREAM into the Amazon Kinesis Firehose stream that you created previously.

  1. On the application configuration page, choose Connect to a destination.
  2. Choose the stream name that you created earlier, and use the default settings for everything else. Then choose Save and Continue.
  3. On the application configuration page, choose Exit to Kinesis Analytics applications to return to the Amazon Kinesis Analytics console.
  4. Run the Python script again for 4–5 minutes to generate enough data to flow through Amazon Kinesis Streams, Kinesis Analytics, Kinesis Firehose, and finally into the Amazon ES domain.
  5. Open the Kinesis Firehose console, choose the stream, and then choose the Monitoring
  6. As the processed data flows into Kinesis Firehose and Amazon ES, the metrics appear on the Delivery Stream metrics page. Keep in mind that the metrics page takes a few minutes to refresh with the latest data.
  7. Open the Amazon Elasticsearch Service dashboard in the AWS Management Console. The count in the Searchable documents column increases as shown in the following screenshot. In addition, the domain shows a cluster health of Yellow. This is because, by default, it needs two instances to deploy redundant copies of the index. To fix this, you can deploy two instances instead of one.

Visualize the data using Kibana

Now it’s time to launch Kibana and visualize the data.

  1. Use the ES domain link to go to the cluster detail page, and then choose the Kibana link as shown in the following screenshot.

    If you’re working behind a proxy or firewall, see the “Use a proxy to simplify request signing” section in this blog post to learn how to work with a proxy.
  2. In the Kibana dashboard, choose the Discover tab to perform a query.
  3. You can also visualize the data using the different types of charts offered by Kibana. For example, by going to the Visualize tab, you can quickly create a split bar chart that aggregates by ANOMALY_SCORE per minute.


Conclusion

In this post, you learned how to use Amazon Kinesis to collect, process, and analyze real-time streaming data, and then export the results to Amazon ES for analysis and visualization with Kibana. If you have comments about this post, add them to the “Comments” section below. If you have questions or issues with implementing this solution, please open a new thread on the Amazon Kinesis or Amazon ES discussion forums.


Next Steps

Take your skills to the next level. Learn real-time clickstream anomaly detection with Amazon Kinesis Analytics.

 


About the Author

Tristan Li is a Solutions Architect with Amazon Web Services. He works with enterprise customers in the US, helping them adopt cloud technology to build scalable and secure solutions on AWS.

 

 

 

 

New Power Bundle for Amazon WorkSpaces – More vCPUs, Memory, and Storage

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-power-bundle-for-amazon-workspaces-more-vcpus-memory-and-storage/

Are you tired of hearing me talk about Amazon WorkSpaces yet? I hope not, because we have a lot of customer-driven additions on the roadmap! Our customers in the developer and analyst community have been asking for a workstation-class machine that will allow them to take advantage of the low cost and flexibility of WorkSpaces. Developers want to run Visual Studio, IntelliJ, Eclipse, and other IDEs. Analysts want to run complex simulations and statistical analysis using MatLab, GNU Octave, R, and Stata.

New Power Bundle
Today we are extending the current set of WorkSpaces bundles with a new Power bundle. With four vCPUs, 16 GiB of memory, and 275 GB of storage (175 GB on the system volume and another 100 GB on the user volume), this bundle is designed to make developers, analysts, (and me) smile. You can launch them in all of the usual ways: Console, CLI (create-workspaces), or API (CreateWorkSpaces):

One really interesting benefit to using a cloud-based virtual desktop for simulations and statistical analysis is the ease of access to data that’s already stored in the cloud. Analysts can mine and analyze petabytes of data stored in S3 that is effectively local (with respect to access time) to the WorkSpace. This low-latency access will boost productivity and also simplifies the use of other AWS data analysis tools such as Amazon Redshift, Amazon Redshift Spectrum, Amazon QuickSight, and Amazon Athena.

Like the existing bundles, the new Power bundle can be used in either billing configuration, AlwaysOn or AutoStop (read Amazon WorkSpaces Update – Hourly Usage and Expanded Root Volume to learn more). The bundle is available in all AWS Regions where WorkSpaces is available and you can launch one today! Visit the WorkSpaces Pricing page for pricing in your region.

Jeff;

New AWS Certification Specialty Exam for Big Data

Post Syndicated from Sara Snedeker original https://aws.amazon.com/blogs/big-data/new-aws-certification-specialty-exam-for-big-data/

AWS Certifications validate technical knowledge with an industry-recognized credential. Today, the AWS Certification team released the AWS Certified Big Data – Specialty exam. This new exam validates technical skills and experience in designing and implementing AWS services to derive value from data. The exam requires a current Associate AWS Certification and is intended for individuals who perform complex big data analyses.

Individuals who are interested in sitting for this exam should know how to do the following:

  • Implement core AWS big data services according to basic architectural best practices
  • Design and maintain big data
  • Leverage tools to automate data analysis

To prepare for the exam, we recommend the Big Data on AWS course, plus AWS whitepapers and documentation that are focused on big data.

This credential can help you stand out from the crowd, get recognized, and provide more evidence of your unique technical skills.

The AWS Certification team also released an AWS Certified Advanced Networking – Specialty exam and new AWS Certification Benefits. You can read more about these new releases on the AWS Blog.

Have more questions about AWS Certification? See our AWS Certification FAQ.