AWS Big Data Blog

Integrate Etleap with Amazon Redshift Streaming Ingestion (preview) to make data available in seconds

Amazon Redshift is a fully managed cloud data warehouse that makes it simple and cost-effective to analyze all your data using SQL and your extract, transform, and load (ETL), business intelligence (BI), and reporting tools. Tens of thousands of customers use Amazon Redshift to process exabytes of data per day and power analytics workloads. Etleap […]

Announcing Amazon EMR Serverless (Preview): Run big data applications without managing servers

Today we’re happy to announce Amazon EMR Serverless, a new option in Amazon EMR that makes it easy and cost-effective for data engineers and analysts to run petabyte-scale data analytics in the cloud. With EMR Serverless, you can run applications built using open-source frameworks such as Apache Spark and Hive without having to configure, manage, […]

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Effective data lakes using AWS Lake Formation, Part 2: Creating a governed table for streaming data sources

February 2023: The content of this blog post can be now be found on AWS Lake Formation public documentation. Please refer to it instead. We announced the general availability of AWS Lake Formation transactions, row-level security, and acceleration at AWS re:Invent 2021. In Part 1 of this series, we explained how to set up a […]

Amazon QuickSight: 2021 in review

With AWS re:Invent just around the corner, we at the Amazon QuickSight team have put together this post to provide you with a handy list of all the key updates this year. We’ve broken this post into three key sections: insights for every user, embedded analytics with QuickSight, scaling and governance. Insights for every user […]

Simplify Snowflake data loading and processing with AWS Glue DataBrew

May 2024: Connecting to Snowflake as a data source is now supported natively. To learn more, visit our documentation. 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 […]

Enforce customized data quality rules in AWS Glue DataBrew

GIGO (garbage in, garbage out) is a concept common to computer science and mathematics: the quality of the output is determined by the quality of the input. In modern data architecture, you bring data from different data sources, which creates challenges around volume, velocity, and veracity. You might write unit tests for applications, but it’s […]

Iterate confidently on Amazon QuickSight datasets with new Dataset Versions capability

Amazon QuickSight allows data owners and authors to create and model their data in QuickSight using datasets, which contain logical and semantic information about the data. Datasets can be created from a single or multiple data sources, and can be shared across the organization with strong controls around data access (object/row/column level security) and metadata […]

Embed interactive dashboards in your apps and portals in minutes with Amazon QuickSight’s new 1-click embedding feature

Amazon QuickSight is a fully-managed, cloud-native business intelligence (BI) service that makes it easy to connect to your data, create interactive dashboards, and share these with tens of thousands of users, either directly within a QuickSight application, or embedded in web apps and portals. QuickSight Enterprise Edition now supports 1-click embedding, a feature that allows […]

Send custom branded email reports from Amazon QuickSight

Amazon QuickSight is a fully-managed, cloud-native business intelligence (BI) service that makes it easy to connect to your data, create interactive dashboards, and share these with tens of thousands of users, either directly within QuickSight application, or embedded in web apps and portals. QuickSight Enterprise Edition now supports the ability to send custom branded email […]

Security considerations for Amazon Redshift cross-account data sharing

Data driven organizations recognize the intrinsic value of data and realize that monetizing data is not just about selling data to subscribers. They understand the indirect economic impact of data and the value that good data brings to the organization. They must democratize data and make it available for business decision makers to realize its […]