Category: Analytics

Imagine you’ve just landed your dream job. You’ve always liked tackling the hardest problems and you’ve got one now: You’ll work for a chain of coffee shops that’s struggling against fierce competition, tight budgets, and low morale. But there’s a new management team in place. As head of business intelligence (BI), you think you can […]

Allan MacInnis is a Kinesis Solution Architect for Amazon Web Services Starting today, you can easily scale your Amazon Kinesis streams to respond in real time to changes in your streaming data needs. Customers use Amazon Kinesis to capture, store, and analyze terabytes of data per hour from clickstreams, financial transactions, social media feeds, and […]

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.

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.

This post was last updated July 7th, 2021 (original version by Tom Zeng). 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 […]

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.

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.

Amazon Web Services is excited to announce that the Amazon EMR-DynamoDB Connector is now open-source. The code you see in the GitHub repository is exactly what is available on your EMR cluster, making it easier to build applications with this component.