AWS Big Data Blog

Dipankar Kushari

Author: Dipankar Kushari

Automate notifications on Slack for Amazon Redshift query monitoring rule violations

In this post, we walk you through how to set up automatic notifications of query monitoring rule (QMR) violations in Amazon Redshift to a Slack channel, so that Amazon Redshift users can take timely action. Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. With Amazon Redshift, you can analyze your […]

Export JSON data to Amazon S3 using Amazon Redshift UNLOAD

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL. Amazon Redshift offers up to three times better price performance than any other cloud data warehouse. Tens of thousands of customers use Amazon Redshift to process exabytes of […]

Introducing new features for Amazon Redshift COPY: Part 1

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL. Amazon Redshift offers up to three times better price performance than any other cloud data warehouse. Tens of thousands of customers use Amazon Redshift to process exabytes of […]

Run and debug Apache Spark applications on AWS with Amazon EMR on Amazon EKS

Customers today want to focus more on their core business model and less on the underlying infrastructure and operational burden. As customers migrate to the AWS Cloud, they’re realizing the benefits of being able to innovate faster on their own applications by relying on AWS to handle big data platforms, operations, and automation. Many of […]

Process data with varying data ingestion frequencies using AWS Glue job bookmarks

We often have data processing requirements in which we need to merge multiple datasets with varying data ingestion frequencies. Some of these datasets are ingested one time in full, received infrequently, and always used in their entirety, whereas other datasets are incremental, received at certain intervals, and joined with the full datasets to generate output. To address this requirement, this post demonstrates how to build an extract, transform, and load (ETL) pipeline using AWS Glue.