Category: Database

In this post, I show you how to use AWS Glue’s DynamoDB integration and AWS Step Functions to create a workflow to export your DynamoDB tables to S3 in Parquet. I also show how to create an Athena view for each table’s latest snapshot, giving you a consistent view of your DynamoDB table exports.

Data warehousing is a critical component for analyzing and extracting actionable insights from your data. Amazon Redshift is a fast, scalable data warehouse that makes it cost-effective to analyze all of your data across your data warehouse and data lake. The Amazon Redshift console recently launched the Query Editor. The Query Editor is an in-browser […]

This post shows a step-by-step walkthrough of how to set up a cross-account Amazon Redshift COPY and Spectrum query using a sample dataset in Amazon S3. The sample dataset is encrypted at rest using AWS KMS-managed keys (SSE-KMS). About AWS Key Management Service (AWS KMS) With AWS Key Management Service (AWS KMS), you can have […]

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. In this post, we talk about designing a cloud-native data warehouse as a replacement for our legacy data warehouse built on a relational database. At the beginning of the design process, the […]

The Amazon Payments Data Engineering team is responsible for data ingestion, transformation, and storage of a growing dataset of more than 750 TB. The team makes these services available to more than 300 business customers around the globe. These customers include managers from the product, marketing, and programs domains; as well as data scientists, business analysts, […]

Amazon Redshift is the cloud data warehouse of choice for organizations of all sizes—from fast-growing technology companies such as Turo and Yelp to Fortune 500 companies such as 21st Century Fox and Johnson & Johnson. With quickly expanding use cases, data sizes, and analyst populations, these customers have a critical need for scalable data warehouses. […]

In this blog post, we describe how to configure the networking routes and interfaces to give AWS Glue access to a data store in an AWS Region different from the one with your AWS Glue resources. In our example, we connect AWS Glue, located in Region A, to an Amazon Redshift data warehouse located in Region B.

In this blog post, we’ll go through the steps needed to build an ETL pipeline that consumes from one source in one VPC and outputs it to another source in a different VPC. We’ll set up in multiple VPCs to reproduce a situation where your database instances are in multiple VPCs for isolation related to security, audit, or other purposes.

As organizations expand analytics practices and hire data scientists and other specialized roles, big data pipelines are growing increasingly complex. Sophisticated models are being built using the troves of data being collected every second. The bottleneck today is often not the know-how of analytical techniques. Rather, it’s the difficulty of building and maintaining ETL (extract, transform, and load) jobs using tools that might be unsuitable for the cloud. In this post, I demonstrate a solution to this challenge.

Since we launched Amazon Redshift, thousands of customers have trusted us to get uncompromising speed for their most complex analytical workloads. Over the course of 2017, our customers benefited from a 3x to 5x performance gain, resulting from short query acceleration, result caching, late materialization, and many other under-the-hood improvements. In this post, we highlight […]