Category: Database

The post Archive and Purge Data for Amazon RDS for PostgreSQL and Amazon Aurora with PostgreSQL Compatibility using pg_partman and Amazon S3 proposes data archival as a critical part of data management and shows how to efficiently use PostgreSQL’s native range partition to partition current (hot) data with pg_partman and archive historical (cold) data in […]

We recently announced support for streaming extract, transform, and load (ETL) jobs in AWS Glue version 4.0, a new version of AWS Glue that accelerates data integration workloads in AWS. AWS Glue streaming ETL jobs continuously consume data from streaming sources, clean and transform the data in-flight, and make it available for analysis in seconds. AWS also offers a broad selection of services to support your needs. A database replication service such as AWS Database Migration Service (AWS DMS) can replicate the data from your source systems to Amazon Simple Storage Service (Amazon S3), which commonly hosts the storage layer of the data lake. This post demonstrates how to apply CDC changes from Amazon Relational Database Service (Amazon RDS) or other relational databases to an S3 data lake, with flexibility to denormalize, transform, and enrich the data in near-real time.

Data has become an integral part of most companies, and the complexity of data processing is increasing rapidly with the exponential growth in the amount and variety of data. Data engineering teams are faced with the following challenges: Manipulating data to make it consumable by business users Building and improving extract, transform, and load (ETL) […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]