AWS Big Data Blog

Organizations spend significant time developing and maintaining data integration pipelines that hydrate data warehouses, data lakes, and lake houses. As data volume increases, data engineering teams struggle to keep up with new requests from business teams. Although these requests may come from different teams, they’re often similar, such as ingesting raw data from a source […]

Historically, inserting and retrieving data from a given database platform has been easier compared to a multi-platform architecture for the same operations. To simplify bringing data in from a multi-database platform, AWS Glue DataBrew supports bringing your data in from multiple data sources via the AWS Glue Data Catalog. However, this requires you to have […]

Today, with AWS Glue DataBrew, data analysts and data scientists can easily access and visually explore any amount of data across their organization directly from their Amazon Simple Storage Service (Amazon S3) data lake, Amazon Redshift data warehouse, and Amazon Aurora and Amazon Relational Database Service (Amazon RDS) databases. Customers can choose from over 250 […]

The cloud is often seen as advantageous for data lakes because of better security, faster time to deployment, better availability, more frequent feature and functionality updates, more elasticity, more geographic coverage, and costs linked to actual utilization. However, recent studies from Gartner and Harvard Business Review show multi-cloud and intercloud architectures are something leaders need […]

At re:Invent 2020, we announced the general availability of Amazon EMR on Amazon EKS, a new deployment option for Amazon EMR that allows you to automate the provisioning and management of open-source big data frameworks on Amazon Elastic Kubernetes Service (Amazon EKS). With Amazon EMR on EKS, you can now run Spark applications alongside other […]

Amazon Kinesis Data Analytics is now expanding its Apache Flink offering by adding support for Python. This is exciting news for many of our customers who use Python as their primary language for application development. This new feature enables developers to build Apache Flink applications in Python using serverless Kinesis Data Analytics. With Kinesis Data […]

Today we announced Auto-Tune in Amazon Elasticsearch Service (Amazon ES), an innovation undertaken to automatically optimize resources in Elasticsearch clusters to improve its performance and availability. Auto-Tune gives us a unique opportunity of applying our learnings from operating clusters at cloud scale on our service to make the Elasticsearch core engine more performant and resilient. […]

Let’s describe the typical use case where a tracking pixel solution, also known as a web beacon, might help you: Analyzing web traffic is critical to understanding user behavior in order to improve their experience. Let’s think about a company—Example Company Hotels—that embeds a piece of HTML into a high-traffic, third-party website (example.HighTrafficWebsite.com) to have […]

AWS Glue is fully managed service that makes it easier for you to extract, transform, and load (ETL) data for analytics. You can easily create ETL jobs to connect to backend data sources. There are several natively supported data sources, but what if you need to extract data from an unsupported data source? What if […]

We announced the preview of AWS Lake Formation transactions, row-level security, and acceleration at AWS re:Invent 2020. In Part 1 of this series, we explained how to set up a governed table and add objects to it. In this post, we expand on this example, and demonstrate how to ingest streaming data into governed tables using Lake Formation transactions. […]