Author: Raks Khare

The Amazon Redshift integration with AWS Lambda provides the capability to create Amazon Redshift Lambda user-defined functions (UDFs). Because Lambda UDFs provide these significant advantages in integration, flexibility, scalability, and security, we will be ending support for Python UDFs in Amazon Redshift. In this post, we walk you through how to migrate your existing Python UDFs to Lambda UDFs, set up monitoring and cost evaluations, and review key considerations for a smooth transition.

This post will explore brief history of zero-ETL, its importance for customers, and introduce an exciting new feature: history mode for Amazon Aurora PostgreSQL-Compatible Edition, Amazon Aurora MySQL-Compatible Edition, Amazon Relational Database Service (Amazon RDS) for MySQL, and Amazon DynamoDB zero-ETL integration with Amazon Redshift.

Now, we are announcing general availability (GA) of Amazon Redshift multi-data warehouse writes through data sharing. This new capability allows you to scale your write workloads and achieve better performance for extract, transform, and load (ETL) workloads by using different warehouses of different types and sizes based on your workload needs.

Amazon Redshift now provides the ability to incrementally refresh your materialized views on data lake tables including open file and table formats such as Apache Iceberg. In this post, we will show you step-by-step what operations are supported on both open file formats and transactional data lake tables to enable incremental refresh of the materialized view.

Amazon Redshift has introduced a new feature called the Query profiler. The Query profiler is a graphical tool that helps users analyze the components and performance of a query. This feature is part of the Amazon Redshift console and provides a visual and graphical representation of the query’s run order, execution plan, and various statistics. The Query profiler makes it easier for users to understand and troubleshoot their queries. In this post, we cover two common use cases for troubleshooting query performance. We show you step-by-step how to analyze and troubleshoot long-running queries using the Query profiler.

Our zero-ETL integration with Amazon Redshift facilitates point-to-point data movement to get it ready for analytics, artificial intelligence (AI) and machine learning (ML) using Amazon Redshift on petabytes of data. In this post, we provide step-by-step guidance on how to get started with near real time operational analytics using the Amazon Aurora PostgreSQL zero-ETL integration with Amazon Redshift.

Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to analyze all your data efficiently and securely. Users such as data analysts, database developers, and data scientists use SQL to analyze their data in Amazon Redshift data warehouses. Amazon Redshift provides a web-based Query Editor V2 in […]

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, […]

Amazon Redshift is a fast, petabyte-scale cloud data warehouse delivering the best price-performance. Tens of thousands of customers use Amazon Redshift to analyze exabytes of data per day and power analytics workloads such as BI, predictive analytics, and real-time streaming analytics without having to manage the data warehouse infrastructure. It natively integrates with other AWS […]