Category: Amazon EMR

In this blog post, we discuss how to build a real-time IoT stream processing, visualization, and alerting pipeline using various AWS services. We took advantage of the Complex Event Processing feature provided by Apache Flink to detect patterns within a network from the incoming events.

This blog post shows how our customers can benefit by using the Apache Sqoop tool. This tool is designed to transfer and import data from a Relational Database Management System (RDBMS) into AWS – EMR Hadoop Distributed File System (HDFS), transform the data in Hadoop, and then export the data into a Data Warehouse (e.g. in Hive or Amazon Redshift).

In this post, we explore orchestrating a Spark data pipeline on Amazon EMR using Apache Livy and Apache Airflow, we create a simple Airflow DAG to demonstrate how to run spark jobs concurrently, and we see how Livy helps to hide the complexity to submit spark jobs via REST by using optimal EMR resources.

In this post, we describe how to set up and run ADAM and Mango on Amazon EMR. We demonstrate how you can use these tools in an interactive notebook environment to explore the 1000 Genomes dataset, which is publicly available in Amazon S3 as a public dataset.

Many enterprises have highly regulated policies around cloud security. Those policies might be even more restrictive for Amazon EMR where sensitive data is processed. EMR provides security configurations that allow you to set up encryption for data at rest stored on Amazon S3 and local Amazon EBS volumes. It also allows the setup of Transport […]

In this post, I detail how EMR clusters resize, and I present some best practices for getting the maximum benefit and resulting cost savings for your own cluster through this feature.

In this post, we’ll show you how to deploy an Ethereum blockchain using the AWS Blockchain Templates, deploy a smart contract, and build a serverless analytics pipeline for that contract based around AWS Lambda, Amazon Kinesis, and Amazon Athena.

In this post, I’ll show you how to use AWS Step Functions to orchestrate your Spark jobs that are running on Amazon EMR.

I’ll describe how to read the DynamoDB backup file format in Data Pipeline, how to convert the objects in S3 to a CSV format that Amazon ML can read, and I’ll show you how to schedule regular exports and transformations using Data Pipeline.

Whether you’ve just signed up for your first AWS account or you’ve been with us for some time, there’s always something new to learn as our services evolve to meet the ever-changing needs of our customers. To help ensure you’re set up for success as you build with AWS, we put together this quick reference guide for Big Data training and resources available here on the AWS site.