Category: Database

Kinesis Data Firehose can now save data to Amazon S3 in Apache Parquet or Apache ORC format. These are optimized columnar formats that are highly recommended for best performance and cost-savings when querying data in S3. This feature directly benefits you if you use Amazon Athena, Amazon Redshift, AWS Glue, Amazon EMR, or any other big data tools that are available from the AWS Partner Network and through the open-source community.

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.

In this blog post, I present a solution that exports system tables from multiple Amazon Redshift clusters into an Amazon S3 bucket. This solution is serverless, and you can schedule it as frequently as every five minutes. The AWS CloudFormation deployment template that I provide automates the solution setup in your environment. The system tables’ data in the Amazon S3 bucket is partitioned by cluster name and query execution date to enable efficient joins in cross-cluster diagnostic queries.

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.

This blog post demonstrates how to combine some of the OHDSI projects (Atlas, Achilles, WebAPI, and the OMOP Common Data Model) with AWS technologies. By doing so, you can quickly and inexpensively implement a health data science and informatics environment.

In this post, we outline three Cassandra deployment options, as well as provide guidance about determining the best practices for your use case.

We have been busy adding new features and capabilities to Amazon Redshift, and we wanted to give you a glimpse of what we’ve been doing over the past year. In this article, we recap a few of our enhancements and provide a set of resources that you can use to learn more and get the most out of your Amazon Redshift implementation.

Over the years, I have developed and created a number of data warehouses from scratch. Recently, I built a data warehouse for the iGaming industry single-handedly. To do it, I used the power and flexibility of Amazon Redshift and the wider AWS data management ecosystem. In this post, I explain how I was able to build a robust and scalable data warehouse without the large team of experts typically needed.

When migrating from a legacy data warehouse to Amazon Redshift, it is tempting to adopt a lift-and-shift approach, but this can result in performance and scale issues long term. This post guides you through the following best practices for ensuring optimal, consistent runtimes for your ETL processes.

A few months ago, we published a blog post about capturing data changes in an Amazon Aurora database and sending it to Amazon Athena and Amazon QuickSight for fast analysis and visualization. In this post, I want to demonstrate how easy it can be to take the data in Aurora and combine it with data in Amazon Redshift using Amazon Redshift Spectrum.