AWS Big Data Blog

Category: Analytics

Anomaly Detection Using PySpark, Hive, and Hue on Amazon EMR

Veronika Megler, Ph.D., is a Senior Consultant with AWS Professional Services We are surrounded by more and more sensors – some of which we’re not even consciously aware. As sensors become cheaper and easier to connect, they create an increasing flood of data that’s getting cheaper and easier to store and process. However, sensor readings […]

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Import Zeppelin notes from GitHub or JSON in Zeppelin 0.5.6 on Amazon EMR

Jonathan Fritz is a Senior Product Manager for Amazon EMR Many Amazon EMR customers use Zeppelin to create interactive notebooks to run workloads with Spark using Scala, Python, and SQL. These customers have found Amazon EMR to be a great platform for running Zeppelin because of strong integration with other AWS services and the ability […]

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Analyze a Time Series in Real Time with AWS Lambda, Amazon Kinesis and Amazon DynamoDB Streams

This is a guest post by Richard Freeman, Ph.D., a solutions architect and data scientist at JustGiving. JustGiving in their own words: “We are one of the world’s largest social platforms for giving that’s helped 26.1 million registered users in 196 countries raise $3.8 billion for over 27,000 good causes.” Introduction As more devices, sensors […]

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Analyze Your Data on Amazon DynamoDB with Apache Spark

Manjeet Chayel is a Solutions Architect with AWS Every day, tons of customer data is generated, such as website logs, gaming data, advertising data, and streaming videos. Many companies capture this information as it’s generated and process it in real time to understand their customers. Amazon DynamoDB is a fast and flexible NoSQL database service […]

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Optimize Spark-Streaming to Efficiently Process Amazon Kinesis Streams

Rahul Bhartia is a Solutions Architect with AWS Martin Schade, a Solutions Architect with AWS, also contributed to this post. Do you use real-time analytics on AWS to quickly extract value from large volumes of data streams? For example, have you built a recommendation engine on clickstream data to personalize content suggestions in real time […]

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Introducing On-Demand Pipeline Execution in AWS Data Pipeline

Marc Beitchman is a Software Development Engineer in the AWS Database Services team Now it is possible to trigger activation of pipelines in AWS Data Pipeline using the new on-demand schedule type. You can access this functionality through the existing AWS Data Pipeline activation API. On-demand schedules make it easy to integrate pipelines in AWS […]

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Process Amazon Kinesis Aggregated Data with AWS Lambda

Ian Meyers is a Solutions Architecture Senior Manager with AWS Last year, we introduced the Amazon Kinesis Producer Library (KPL) to simplify the development of applications that need to send data to Amazon Kinesis Streams. Many customers use aggregation, which allows you to send multiple records to a single Amazon Kinesis Streams record.  Although the […]

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Submitting User Applications with spark-submit

Francisco Oliveira is a consultant with AWS Professional Services Customers starting their big data journey often ask for guidelines on how to submit user applications to Spark running on Amazon EMR. For example, customers ask for guidelines on how to size memory and compute resources available to their applications and the best resource allocation model […]

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Turning Amazon EMR into a Massive Amazon S3 Processing Engine with Campanile

Michael Wallman is a senior consultant with AWS ProServ Have you ever had to copy a huge Amazon S3 bucket to another account or region? Or create a list based on object name or size? How about mapping a function over millions of objects? Amazon EMR to the rescue! EMR allows you to deploy large […]

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Querying Amazon Kinesis Streams Directly with SQL and Spark Streaming

Amo Abeyaratne is a Big Data consultant with AWS Professional Services Introduction What if you could use your SQL knowledge to discover patterns directly from an incoming stream of data? Streaming analytics is a very popular topic of conversation around big data use cases.  These use cases can vary from just accumulating simple web transaction […]

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