AWS Big Data Blog

Category: Database

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 […]

Near-real-time analytics using Amazon Redshift streaming ingestion with Amazon Kinesis Data Streams and Amazon DynamoDB

Amazon Redshift is a fully managed, scalable cloud data warehouse that accelerates your time to insights with fast, easy, and secure analytics at scale. Tens of thousands of customers rely on Amazon Redshift to analyze exabytes of data and run complex analytical queries, making it the widely used cloud data warehouse. You can run and […]

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

November 2023: This post was reviewed and updated to include the latest enhancements in Amazon Aurora MySQL zero-ETL integration with Amazon Redshift on general availability (GA). Amazon Aurora zero-ETL integration with Amazon Redshift was announced at AWS re:Invent 2022 and is now generally available (GA) for Aurora MySQL 3.05.0 (compatible with MySQL 8.0.32) and higher […]

Harmonize data using AWS Glue and AWS Lake Formation FindMatches ML to build a customer 360 view

In today’s digital world, data is generated by a large number of disparate sources and growing at an exponential rate. Companies are faced with the daunting task of ingesting all this data, cleansing it, and using it to provide outstanding customer experience. Typically, companies ingest data from multiple sources into their data lake to derive […]

Build an Amazon Redshift data warehouse using an Amazon DynamoDB single-table design

Amazon DynamoDB is a fully managed NoSQL service that delivers single-digit millisecond performance at any scale. It’s used by thousands of customers for mission-critical workloads. Typical use cases for DynamoDB are an ecommerce application handling a high volume of transactions, or a gaming application that needs to maintain scorecards for players and games. In traditional […]

Federate Amazon QuickSight access with open-source identity provider Keycloak

Amazon QuickSight is a scalable, serverless, embeddable, machine learning (ML) powered business intelligence (BI) service built for the cloud that supports identity federation in both Standard and Enterprise editions. Organizations are working toward centralizing their identity and access strategy across all their applications, including on-premises and third-party. Many organizations use Keycloak as their identity provider […]

Join a streaming data source with CDC data for real-time serverless data analytics using AWS Glue, AWS DMS, and Amazon DynamoDB

Customers have been using data warehousing solutions to perform their traditional analytics tasks. Recently, data lakes have gained lot of traction to become the foundation for analytical solutions, because they come with benefits such as scalability, fault tolerance, and support for structured, semi-structured, and unstructured datasets. Data lakes are not transactional by default; however, there […]

High-level data platform expected behavior

How Novo Nordisk built distributed data governance and control at scale

This is a guest post co-written with Jonatan Selsing and Moses Arthur from Novo Nordisk. This is the second post of a three-part series detailing how Novo Nordisk, a large pharmaceutical enterprise, partnered with AWS Professional Services to build a scalable and secure data and analytics platform. The first post of this series describes the […]

How Huron built an Amazon QuickSight Asset Catalogue with AWS CDK Based Deployment Pipeline

This is a guest blog post co-written with Corey Johnson from Huron. Having an accurate and up-to-date inventory of all technical assets helps an organization ensure it can keep track of all its resources with metadata information such as their assigned owners, last updated date, used by whom, how frequently, and more. It helps engineers, […]

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 […]