Category: AWS Big Data

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 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.

Get technical details and best practices from AWS experts. Hear directly from customers and learn from the experience of other organizations that are deploying big data solutions on AWS.

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.

ustomers running analytics, stream processing, machine learning, and ETL workloads on personally identifiable information, health information, and financial data have strict requirements for encryption of data at-rest and in-transit. The Apache Spark and Hadoop ecosystems lend themselves to these big data use cases, and customers have asked us to provide a quick and easy way to encrypt data at-rest and data in-transit between nodes in each execution framework.

In this post, I show an analytics pipeline which detects anomalies in real time for a web traffic stream, using the RANDOM_CUT_FOREST function available in Amazon Kinesis Analytics.