AWS Big Data Blog

Today we are making it even easier to run Flink on AWS as it is now natively supported in Amazon EMR 5.1.0. EMR supports running Flink-on-YARN so you can create either a long-running cluster that accepts multiple jobs or a short-running Flink session in a transient cluster that helps reduce your costs by only charging you for the time that you use.

Another month of big data solutions on the Big Data Blog. Take a look at our summaries below and learn, comment, and share. Thanks for reading! Building Event-Driven Batch Analytics on AWS Modern businesses typically collect data from internal and external sources at various frequencies throughout the day. In this post, you learn an elastic […]

In this blog post, we’ll use a real customer scenario to show you how to create a caching layer in front of Amazon Redshift using pgpool and Amazon ElastiCache.

Publishing misleading performance benchmarks is a classic old guard marketing tactic. It’s not surprising to see old guard companies (like Oracle) doing this, but we were kind of surprised to see Google take this approach, too. So, when Google presented their BigQuery vs. Amazon Redshift benchmark results at a private event in San Francisco on September 29, 2016, it piqued our interest and we decided to dig deeper.

Peter Schmiedeskamp also contributed to this post. The Sparklyr package by RStudio has made processing big data in R a lot easier. Sparklyr is an R interface to Spark, it allows using Spark as the backend for dplyr – one of the most popular data manipulation packages. Sparklyr also allows user to query data in […]

In today’s blog post, I will discuss how to optimize Amazon S3 for an architecture commonly used to enable genomic data analyses. This optimization is important to my work in genomics because, as genome sequencing continues to drop in price, the rate at which data becomes available is accelerating.

In this post, I discuss some of the practical challenges faced during the implementation of the data lake for Eliza and the corresponding details of the ways we solved these issues with AWS. The challenges we faced involved the variety of data and a need for a common view of the data.

In this post, I walk you through an architectural approach as well as a sample implementation on how to collect, process, and analyze data for event-driven applications in AWS.

Another month of big data solutions on the Big Data Blog. Take a look at our summaries below and learn, comment, and share. Thanks for reading! Processing VPC Flow Logs with Amazon EMR In this post, learn how to gain valuable insight into your network by using Amazon EMR and Amazon VPC Flow Logs. The […]

This post demonstrates how to set up Apache Kafka on EC2, use Spark Streaming on EMR to process data coming in to Apache Kafka topics, and query streaming data using Spark SQL on EMR.