Category: HAQM Redshift

We’re just two days away from AWS Summit Sydney (April 10–11) and a month away from the AWS Summit season in Southeast Asia, starting with the AWS Summit Singapore (May 7) and the AWS Summit Bangkok (May 30). If you happen to be in Sydney, Singapore, or Bangkok around those dates, please join us. Last […]

“Data is at the center of every application, process, and business decision,” wrote Swami Sivasubramanian, VP of Database, Analytics, and Machine Learning at AWS, and I couldn’t agree more. A common pattern customers use today is to build data pipelines to move data from HAQM Aurora to HAQM Redshift. These solutions help them gain insights […]

It’s probably no surprise that generative artificial intelligence and machine learning were the stars of the show, but there were several other bright lights from the day-long cloud conference.

This edition of Week in Review marks the end of the month of May. In addition, we just finished all of the in-person AWS Summits in Asia-Pacific and Japan starting from AWS Summit Sydney and AWS Summit Tokyo in April to AWS Summit ASEAN, AWS Summit Seoul, and AWS Summit Mumbai in May. Thank you […]

Here are your AWS updates from the previous 7 days. Last week I was in Turin, Italy for CloudConf, a conference I’ve had the pleasure to participate in for the last 10 years. AWS Hero Anahit Pogosova was also there sharing a few serverless tips in front of a full house. Here’s a picture I […]

When we talk with customers, we hear that they want to be able to harness insights from data in order to make timely, impactful, and actionable business decisions. A common pattern with data-driven organizations is that they have many different data sources they need to ingest into their analytics systems. This requires them to build […]

Ten years ago, just a few months after I joined AWS, HAQM Redshift was launched. Over the years, many features have been added to improve performance and make it easier to use. HAQM Redshift now allows you to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes. More recently, HAQM Redshift […]

Apache Spark is an open-source, distributed processing system commonly used for big data workloads. Spark application developers working in HAQM EMR, HAQM SageMaker, and AWS Glue often use third-party Apache Spark connectors that allow them to read and write the data with HAQM Redshift. These third-party connectors are not regularly maintained, supported, or tested with […]