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Capital One Offers Cost Controls for Cloud Data Warehouses

David Menninger's Analyst Perspectives

As adoption has grown, some enterprises found that the theoretical advantages of data processing in the cloud can be more challenging to deliver in practice, with constant monitoring and manual intervention required to optimize resources and realize potential savings.

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Accelerate SQL code migration from Google BigQuery to Amazon Redshift using BladeBridge

AWS Big Data

BladeBridge offers a comprehensive suite of tools that automate much of the complex conversion work, allowing organizations to quickly and reliably transition their data analytics capabilities to the scalable Amazon Redshift data warehouse. times better price performance than other cloud data warehouses.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. All ML projects are software projects.

IT 363
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Incremental refresh for Amazon Redshift materialized views on data lake tables

AWS Big Data

Amazon Redshift is a fast, fully managed cloud data warehouse that makes it cost-effective to analyze your data using standard SQL and business intelligence tools. One such optimization for reducing query runtime is to precompute query results in the form of a materialized view. Enrico holds a M.Sc.

Data Lake 105
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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. Data store – The data store used a custom data model that had been highly optimized to meet low-latency query response requirements.

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Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud

AWS Big Data

While customers can perform some basic analysis within their operational or transactional databases, many still need to build custom data pipelines that use batch or streaming jobs to extract, transform, and load (ETL) data into their data warehouse for more comprehensive analysis. or a later version) database.

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Optimize your workloads with Amazon Redshift Serverless AI-driven scaling and optimization

AWS Big Data

The current scaling approach of Amazon Redshift Serverless increases your compute capacity based on the query queue time and scales down when the queuing reduces on the data warehouse. In this post, we describe how Redshift Serverless utilizes the new AI-driven scaling and optimization capabilities to address common use cases.