AWS Big Data Blog

Amazon Redshift is the most popular cloud data warehouse because it provides fast insights at a low cost. Customers can confidently run mission critical workloads, even in highly regulated industries, because Amazon Redshift comes with out of the box security and compliance. The security features, combined with the ability to easily analyze data in-place and […]

The Amazon Redshift materialized views function helps you achieve significantly faster query performance on repeated or predictable workloads such as dashboard queries from Business Intelligence (BI) tools, such as Amazon QuickSight. It also speeds up and simplifies extract, load, and transform (ELT) data processing. You can use materialized views to store frequently used precomputations and […]

This post showcases TruFactor Intelligence-as-a-Service data on AWS Data Exchange. TruFactor’s anonymization platform and proprietary AI ingests, filters, and transforms more than 85 billion high-quality raw signals daily from wireless carriers, OEMs, and mobile apps into a unified phygital consumer graph across physical and digital dimensions. TruFactor intelligence is application-ready for use within any AWS analytics or ML service to power your models and applications running on AWS, with no additional processing required.

Amazon Redshift Federated Query allows you to combine the data from one or more Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL databases with data already in Amazon Redshift. You can also combine such data with data in an Amazon S3 data lake.

You may have heard the saying that the best ETL is no ETL. Amazon Redshift now makes this possible with Federated Query. In its initial release, this feature lets you query data in Amazon Aurora PostgreSQL or Amazon RDS for PostgreSQL using Amazon Redshift external schemas. Federated Query also exposes the metadata from these source databases through system views and driver APIs, which allows business intelligence tools like Tableau and Amazon Quicksight to connect to Amazon Redshift and query data in PostgreSQL without having to make local copies.

This post demonstrates a serverless, cloud-based approach to building a network performance analytics solution using AWS services that can provide flexibility and performance while keeping costs under control with pay-per-use AWS services. Without good network performance, you may struggle to face the challenges of real-time and low latency services and the increase of the total […]

This post shows how to ingest data from Amazon RDS into a data lake on Amazon S3 using Lake Formation blueprints and how to have column-level access controls for running SQL queries on the extracted data from Amazon Athena.

As the COVID-19 pandemic continues to threaten and take lives around the world, we must work together across organizations and scientific disciplines to fight this disease. Innumerable healthcare workers, medical researchers, scientists, and public health officials are already on the front lines caring for patients, searching for therapies, educating the public, and helping to set […]

Apache Spark is a powerful data processing engine that gives data analyst and engineering teams easy to use APIs and tools to analyze their data, but it can be challenging for teams to manage their Python and R library dependencies. Installing every dependency that a job may need before it runs and dealing with library […]

Amazon Redshift delivers fast performance, at scale, for the most demanding workloads. Getting there was not easy, and it takes consistent investment across a variety of technical focus areas to make this happen. This post breaks down what it takes to build the world’s fastest cloud data warehouse.