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From data lakes to insights: dbt adapter for Amazon Athena now supported in dbt Cloud

AWS Big Data

With the dbt adapter for Athena adapter now supported in dbt Cloud, you can seamlessly integrate your AWS data architecture with dbt Cloud, taking advantage of the scalability and performance of Athena to simplify and scale your data workflows efficiently.

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Simplify data ingestion from Amazon S3 to Amazon Redshift using auto-copy

AWS Big Data

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze your data using standard SQL and your existing business intelligence (BI) tools. Data ingestion is the process of getting data to Amazon Redshift.

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Migrate a petabyte-scale data warehouse from Actian Vectorwise to Amazon Redshift

AWS Big Data

Amazon Redshift is a fast, scalable, and fully managed cloud data warehouse that allows you to process and run your complex SQL analytics workloads on structured and semi-structured data. Solution overview Amazon Redshift is an industry-leading cloud data warehouse.

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Accelerate your data workflows with Amazon Redshift Data API persistent sessions

AWS Big Data

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that you can use to analyze your data at scale. This persistent session model provides the following key benefits: The ability to create temporary tables that can be referenced across the entire session lifespan.

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Run Apache XTable in AWS Lambda for background conversion of open table formats

AWS Big Data

This post was co-written with Dipankar Mazumdar, Staff Data Engineering Advocate with AWS Partner OneHouse. Data architecture has evolved significantly to handle growing data volumes and diverse workloads. Moreover, they can be combined to benefit from individual strengths.

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How to Future-Proof Your Business Systems with a Data Warehouse

Jet Global

Interestingly, you can address many of them very effectively with a data warehouse. First of all, many companies have accumulated quite a lot of historical data. The process of exporting the data, filtering them, cleansing them, and reformatting them for the new system is time-consuming and costly. Probably not.

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Build an Amazon Redshift data warehouse using an Amazon DynamoDB single-table design

AWS Big Data

In traditional databases, we would model such applications using a normalized data model (entity-relation diagram). A key pillar of AWS’s modern data strategy is the use of purpose-built data stores for specific use cases to achieve performance, cost, and scale. These types of queries are suited for a data warehouse.