Category: *Post Types

April 2024: This post was reviewed for accuracy. This is a joint blog post co-authored with Anu Jain, Graham Person, and Paul Conroy from JP Morgan Chase.  Most modern organizations recognize that their data benefits their entire enterprise. Data has value to the individual business process that produces it, but data’s additional potential can be […]

At AWS re:Invent 2020, we announced the preview of native support for JSON and semi-structured data in Amazon Redshift. This includes a new data type, SUPER, which allows you to store JSON and other semi-structured data in Amazon Redshift tables, and support for the PartiQL query language, which allows you to seamlessly query and process […]

This is a guest blog post by Kazuhide Fujita, Product Manager at Yahoo! JAPAN. Yahoo! JAPAN is a large internet search and media company, with Yahoo! JAPAN’s web portal being the one of the most commonly used websites in Japan. Our smart devices team is responsible for building and improving Yahoo! JAPAN apps for voice […]

August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. This is a customer post written by the engineers from German startup Baqend and the AWS EMEA Prototyping Labs team. Baqend is one of the fastest-growing software […]

This post discusses how Vortexa harnesses the power of Apache Kafka to improve real-time data accuracy and accelerate time-to-market by using a combination of Lenses.io for greater observability and Amazon Managed Streaming for Apache Kafka (Amazon MSK) to create clusters on demand.

August 2024: This post was reviewed and updated for accuracy. Amazon OpenSearch Service is a fully managed service that makes it easy to deploy, secure, scale, and monitor your OpenSearch cluster in the AWS Cloud. Elasticsearch and OpenSearch are a distributed database solution, which can be difficult to plan for and execute. This post discusses […]

The first post of this series discusses two key AWS Glue capabilities to manage the scaling of data processing jobs. The first allows you to horizontally scale out Apache Spark applications for large splittable datasets. The second allows you to vertically scale up memory-intensive Apache Spark applications with the help of new AWS Glue worker types. The post also shows how to use AWS Glue to scale Apache Spark applications with a large number of small files commonly ingested from streaming applications using Amazon Kinesis Data Firehose. Finally, the post shows how AWS Glue jobs can use the partitioning structure for large datasets in Amazon S3 to provide faster execution times for Apache Spark applications.

In this blog post, we introduce Deequ, an open source tool developed and used at Amazon. Deequ allows you to calculate data quality metrics on your dataset, define and verify data quality constraints, and be informed about changes in the data distribution. Instead of implementing checks and verification algorithms on your own, you can focus on describing how your data should look.

In this post, we show you how to export data from a DynamoDB table, convert it into a more efficient format with AWS Glue, and query the data with Athena. This approach gives you a way to pull insights from your data stored in DynamoDB.

The best practices described in this post are based on our experience in running and operating large-scale Kafka clusters on AWS for more than two years. Our intent for this post is to help AWS customers who are currently running Kafka on AWS, and also customers who are considering migrating on-premises Kafka deployments to AWS.