article thumbnail

Centralize near-real-time governance through alerts on Amazon Redshift data warehouses for sensitive queries

AWS Big Data

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud that delivers powerful and secure insights on all your data with the best price-performance. With Amazon Redshift, you can analyze your data to derive holistic insights about your business and your customers.

article thumbnail

Achieve near real time operational analytics using Amazon Aurora PostgreSQL zero-ETL integration with Amazon Redshift

AWS Big Data

and zero-ETL support) as the source, and a Redshift data warehouse as the target. The integration replicates data from the source database into the target data warehouse. Additionally, you can choose the capacity, to limit the compute resources of the data warehouse. For this post, set this to 8 RPUs.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Getting started guide for near-real time operational analytics using Amazon Aurora zero-ETL integration with Amazon Redshift

AWS Big Data

There are two broad approaches to analyzing operational data for these use cases: Analyze the data in-place in the operational database (e.g. With Aurora zero-ETL integration with Amazon Redshift, the integration replicates data from the source database into the target data warehouse.

article thumbnail

Introducing Amazon Q data integration in AWS Glue

AWS Big Data

It can generate data integration jobs for extracts and loads to S3 data lakes including file formats like CSV, JSON, and Parquet, and ingestion into open table formats like Apache Hudi, Delta, and Apache Iceberg. About the Authors Noritaka Sekiyama is a Principal Big Data Architect on the AWS Glue team.

article thumbnail

Q&A with Greg Rahn – The changing Data Warehouse market

Cloudera

After having rebuilt their data warehouse, I decided to take a little bit more of a pointed role, and I joined Oracle as a database performance engineer. I spent eight years in the real-world performance group where I specialized in high visibility and high impact data warehousing competes and benchmarks. Interesting times.

article thumbnail

Unlock insights on Amazon RDS for MySQL data with zero-ETL integration to Amazon Redshift

AWS Big Data

The extract, transform, and load (ETL) process has been a common pattern for moving data from an operational database to an analytics data warehouse. ELT is where the extracted data is loaded as is into the target first and then transformed. ETL and ELT pipelines can be expensive to build and complex to manage.

article thumbnail

Orchestrate an end-to-end ETL pipeline using Amazon S3, AWS Glue, and Amazon Redshift Serverless with Amazon MWAA

AWS Big Data

As the queries finish running, an UNLOAD operation is invoked from the Redshift data warehouse to the S3 bucket in Account A. The pipeline then starts running stored procedures and SQL commands on Redshift Serverless.

Metadata 106