AWS Big Data Blog
Category: Amazon Redshift
Accelerate SQL code migration from Google BigQuery to Amazon Redshift using BladeBridge
This post explores how you can use BladeBridge, a leading data environment modernization solution, to simplify and accelerate the migration of SQL code from BigQuery to Amazon Redshift. BladeBridge offers a comprehensive suite of tools that automate much of the complex conversion work, allowing organizations to quickly and reliably transition their data analytics capabilities to the scalable Amazon Redshift data warehouse.
Simplify data ingestion from Amazon S3 to Amazon Redshift using auto-copy
Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze your data using standard SQL and your existing business intelligence (BI) tools. Tens of thousands of customers today rely on Amazon Redshift to analyze exabytes of data and run complex analytical queries, making it […]
Evaluating sample Amazon Redshift data sharing architecture using Redshift Test Drive and advanced SQL analysis
In this post, we walk you through the process of testing workload isolation architecture using Amazon Redshift Data Sharing and Test Drive utility. We demonstrate how you can use SQL for advanced price performance analysis and compare different workloads on different target Redshift cluster configurations.
How Kaplan, Inc. implemented modern data pipelines using Amazon MWAA and Amazon AppFlow with Amazon Redshift as a data warehouse
Kaplan, Inc. provides individuals, educational institutions, and businesses with a broad array of services, supporting our students and partners to meet their diverse and evolving needs throughout their educational and professional journeys. In this post, we discuss how the Kaplan data engineering team implemented data integration from the Salesforce application to Amazon Redshift. The solution uses Amazon Simple Storage Service as a data lake, Amazon Redshift as a data warehouse, Amazon Managed Workflows for Apache Airflow (Amazon MWAA) as an orchestrator, and Tableau as the presentation layer.
Simplify data streaming ingestion for analytics using Amazon MSK and Amazon Redshift
Towards the end of 2022, AWS announced the general availability of real-time streaming ingestion to Amazon Redshift for Amazon Kinesis Data Streams and Amazon Managed Streaming for Apache Kafka (Amazon MSK), eliminating the need to stage streaming data in Amazon Simple Storage Service (Amazon S3) before ingesting it into Amazon Redshift. Streaming ingestion from Amazon […]
Simplify access management with Amazon Redshift and AWS Lake Formation for users in an External Identity Provider
Many organizations use identity providers (IdPs) to authenticate users, manage their attributes, and group memberships for secure, efficient, and centralized identity management. You might be modernizing your data architecture using Amazon Redshift to enable access to your data lake and data in your data warehouse, and are looking for a centralized and scalable way to […]
Enable Multi-AZ deployments for your Amazon Redshift data warehouse
November 2023: This post was reviewed and updated with the general availability of Multi-AZ deployments for provisioned RA3 clusters. Originally published on December 9th, 2022. Amazon Redshift is a fully managed, petabyte scale cloud data warehouse that enables you to analyze large datasets using standard SQL. Data warehouse workloads are increasingly being used with mission-critical […]
Five actionable steps to GDPR compliance (Right to be forgotten) with Amazon Redshift
The GDPR (General Data Protection Regulation) right to be forgotten, also known as the right to erasure, gives individuals the right to request the deletion of their personally identifiable information (PII) data held by organizations. This means that individuals can ask companies to erase their personal data from their systems and any third parties with […]
Simplify and speed up Apache Spark applications on Amazon Redshift data with Amazon Redshift integration for Apache Spark
Customers use Amazon Redshift to run their business-critical analytics on petabytes of structured and semi-structured data. Apache Spark is a popular framework that you can use to build applications for use cases such as ETL (extract, transform, and load), interactive analytics, and machine learning (ML). Apache Spark enables you to build applications in a variety […]
How AWS Payments migrated from Redash to Amazon Redshift Query Editor v2
AWS Payments is part of the AWS Commerce Platform (CP) organization that owns the customer experience of paying AWS invoices. It helps AWS customers manage their payment methods and payment preferences, and helps customers make self-service payments to AWS. The Machine Learning, Data and Analytics (MLDA) team at AWS Payments enables data-driven decision-making across payments […]