AWS Big Data Blog

Category: Amazon Redshift

Share and publish your Snowflake data to AWS Data Exchange using Amazon Redshift data sharing

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. You can start with just a few hundred gigabytes of data and scale to a petabyte or more. Today, tens of thousands of AWS customers—from Fortune 500 companies, startups, and everything in between—use Amazon Redshift to run mission-critical business intelligence (BI) dashboards, […]

Simplify data analysis and collaboration with SQL Notebooks in Amazon Redshift Query Editor V2.0

Amazon Redshift Query Editor V2.0 is a web-based analyst workbench that you can use to author and run queries on your Amazon Redshift data warehouse. You can visualize query results with charts, and explore, share, and collaborate on data with your teams in SQL through a common interface. With SQL Notebooks, Amazon Redshift Query Editor […]

Automate Amazon Redshift Serverless data warehouse management using AWS CloudFormation and the AWS CLI

Amazon Redshift Serverless makes it simple to run and scale analytics without having to manage the instance type, instance size, lifecycle management, pausing, resuming, and so on. It automatically provisions and intelligently scales data warehouse compute capacity to deliver fast performance for even the most demanding and unpredictable workloads, and you pay only for what […]

Fine-grained entitlements in Amazon Redshift: A case study from TrustLogix

This post is co-written with Srikanth Sallaka from TrustLogix as the lead author. TrustLogix is a cloud data access governance platform that monitors data usage to discover patterns, provide insights on least privileged access controls, and manage fine-grained data entitlements across data lake storage solutions like Amazon Simple Storage Service (Amazon S3), data warehouses like […]

Cross-account streaming ingestion for Amazon Redshift

As AWS’s fast, petabyte-scale cloud data warehouse delivering the best price-performance, Amazon Redshift makes it simple and cost-effective to analyze all your data using standard SQL, your existing ETL (extract, transform, and load), business intelligence (BI), and reporting tools quickly and securely. Tens of thousands of customers use Amazon Redshift to analyze exabytes of data […]

Use Amazon Redshift Spectrum with row-level and cell-level security policies defined in AWS Lake Formation

Data warehouses and data lakes are key to an enterprise data management strategy. A data lake is a centralized repository that consolidates your data in any format at any scale and makes it available for different kinds of analytics. A data warehouse, on the other hand, has cleansed, enriched, and transformed data that is optimized […]

Easy analytics and cost-optimization with Amazon Redshift Serverless

Amazon Redshift Serverless makes it easy to run and scale analytics in seconds without the need to setup and manage data warehouse clusters. With Redshift Serverless, users such as data analysts, developers, business professionals, and data scientists can get insights from data by simply loading and querying data in the data warehouse. With Redshift Serverless, […]

Convert Oracle XML BLOB data using Amazon EMR and load to Amazon Redshift

In legacy relational database management systems, data is stored in several complex data types, such XML, JSON, BLOB, or CLOB. This data might contain valuable information that is often difficult to transform into insights, so you might be looking for ways to load and use this data in a modern cloud data warehouse such as […]

How Fannie Mae built a data mesh architecture to enable self-service using Amazon Redshift data sharing

This post is co-written by Kiran Ramineni and Basava Hubli, from Fannie Mae. Amazon Redshift data sharing enables instant, granular, and fast data access across Amazon Redshift clusters without the need to copy or move data around. Data sharing provides live access to data so that users always see the most up-to-date and transactionally consistent […]

Set up federated access to Amazon Athena for Microsoft AD FS users using AWS Lake Formation and a JDBC client

Tens of thousands of AWS customers choose Amazon Simple Storage Service (Amazon S3) as their data lake to run big data analytics, interactive queries, high-performance computing, and artificial intelligence (AI) and machine learning (ML) applications to gain business insights from their data. On top of these data lakes, you can use AWS Lake Formation to […]