Category: Amazon Redshift

In this post, we discuss how AWS can help you successfully address the challenges of extracting insights from unstructured data. We discuss various design patterns and architectures for extracting and cataloging valuable insights from unstructured data using AWS. Additionally, we show how to use AWS AI/ML services for analyzing unstructured data.

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud, providing up to five times better price-performance than any other cloud data warehouse, with performance innovation out of the box at no additional cost to you. Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to […]

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. You can start with just a few hundred gigabytes of data and scale to a petabyte or more. Today, tens of thousands of AWS customers—from Fortune 500 companies, startups, and everything in between—use Amazon Redshift to run mission-critical business intelligence (BI) dashboards, […]

In this post, we show how to migrate a data warehouse from Microsoft Azure Synapse to Redshift Serverless using AWS Schema Conversion Tool (AWS SCT) and AWS SCT data extraction agents. AWS SCT makes heterogeneous database migrations predictable by automatically converting the source database code and storage objects to a format compatible with the target database.

In this post, we describe at a high-level how CDC tasks work in AWS SCT. Then we deep dive into an example of how to configure, start, and manage a CDC migration task. We look briefly at performance and how you can tune a CDC migration, and then conclude with some information about how you can get started on your own migration.

Organizations are grappling with the ever-expanding spectrum of data formats in today’s data-driven landscape. From Avro’s binary serialization to the efficient and compact structure of Protobuf, the landscape of data formats has expanded far beyond the traditional realms of CSV and JSON. As organizations strive to derive insights from these diverse data streams, the challenge […]

Amazon Redshift has added many features to enhance analytical processing like ROLLUP, CUBE and GROUPING SETS, which were demonstrated in the post Simplify Online Analytical Processing (OLAP) queries in Amazon Redshift using new SQL constructs such as ROLLUP, CUBE, and GROUPING SETS. Amazon Redshift has recently added many SQL commands and expressions. In this post, we talk about two new SQL features, the MERGE command and QUALIFY clause, which simplify data ingestion and data filtering.

This post is co-written by Lisa Levy, Content Specialist at Satori. Data democratization enables users to discover and gain access to data faster, improving informed data-driven decisions and using data to generate business impact. It also increases collaboration across teams and organizations, breaking down data silos and enabling cross-functional teams to work together more effectively. […]

In this post, we discuss the enhancements to Amazon Redshift stored procedures for non-atomic transaction mode. This mode provides enhanced transaction controls that enable you to automatically commit the statements inside the stored procedure.

Amazon Redshift supports querying a wide variety of data formats, such as CSV, JSON, Parquet, and ORC, and table formats like Apache Hudi and Delta. Amazon Redshift also supports querying nested data with complex data types such as struct, array, and map. With this capability, Amazon Redshift extends your petabyte-scale data warehouse to an exabyte-scale data lake on Amazon S3 in a cost-effective manner. Apache Iceberg is the latest table format that is supported by Amazon Redshift. In this post, we show you how to query Iceberg tables using Amazon Redshift, and explore Iceberg support and options.