Category: Amazon Redshift

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.

Clickstream analysis tools handle their data well, and some even have impressive BI interfaces. However, analyzing clickstream data in isolation comes with many limitations. For example, a customer is interested in a product or service on your website. They go to your physical store to purchase it. The clickstream analyst asks, “What happened after they […]

By establishing a data warehouse strategy using Amazon S3 for storage and Redshift Spectrum for analytics, we increased the size of the datasets we support by over an order of magnitude. In addition, we improved our ability to ingest large volumes of data quickly, and maintained fast performance without increasing our costs. Our analysts and modelers can now perform deeper analytics to improve ad buying strategies and results.

Narrativ, in their own words: Narrativ is building monetization technology for the next generation of digital content producers. Our product portfolio includes a real-time bidding platform and visual storytelling tools that together generate millions of dollars of advertiser value and billions of data points each month. At Narrativ, we have seen massive growth in our […]

In this post, we take a look at query result caching in Amazon Redshift. Result caching does exactly what its name implies—it caches the results of a query.

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.