AWS Big Data Blog

AWS serverless services, including but not limited to AWS Lambda, AWS Glue, AWS Fargate, Amazon EventBridge, Amazon Athena, Amazon Simple Notification Service (Amazon SNS), Amazon Simple Queue Service (Amazon SQS), and Amazon Simple Storage Service (Amazon S3), have become the building blocks for any serverless data lake, providing key mechanisms to ingest and transform data […]

Modern applications apply security controls across many systems and their subsystems. Keeping all of these systems in sync would be a major undertaking if you tried to implement it separately. Centralized identity management is the way to maintain a single identity provider (IdP) that can authenticate actors and manage and distribute their rights. OpenSearch is […]

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to power their analytics workloads. Many businesses use different software tools to analyze historical data and past patterns to forecast future demand and trends to make more […]

Today’s modern data lakes span multiple accounts, AWS Regions, and lines of business in organizations. Companies also have employees and do business across multiple geographic regions and even around the world. It’s important that their data solution gives them the ability to share and access data securely and safely across Regions. The AWS Glue Data […]

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.

Today, more than 400 organizations have signed The Climate Pledge, a commitment to reach net-zero carbon by 2040. Some of the drivers that lead to setting explicit climate goals include customer demand, current and anticipated government relations, employee demand, investor demand, and sustainability as a competitive advantage. AWS customers are increasingly interested in ways to […]

This post was co-written by Sheel Saket, Senior Data Science Manager at FIS, and Rupesh Tiwari, Senior Architect at Amazon Web Services. Do you ever find yourself grappling with multiple defect logging mechanisms, scattered project management tools, and fragmented software development platforms? Have you experienced the frustration of lacking a unified view, hindering your ability […]

April 2025: This post was reviewed and updated for accuracy. Amazon Kinesis Data Streams is a serverless data streaming service that makes it easy to capture, process, and store streaming data at any scale. As customers collect and stream more types of data, they have asked for simpler, elastic data streams that can handle variable and […]

In the world of software engineering and development, organizations use project management tools like Atlassian Jira Cloud. Managing projects with Jira leads to rich datasets, which can provide historical and predictive insights about project and development efforts. Although Jira Cloud provides reporting capability, loading this data into a data lake will facilitate enrichment with other […]

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) […]