Remove Analytics Remove Data Transformation Remove Data Warehouse Remove Metadata
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How HR&A uses Amazon Redshift spatial analytics on Amazon Redshift Serverless to measure digital equity in states across the US

AWS Big Data

This cut down significantly on analytical turnaround times. The CARTO Analytics Toolbox for Redshift is composed of a set of user-defined functions and procedures organized in a set of modules based on the functionality they offer. These table definitions are used as the metadata repository for external tables in Amazon Redshift.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes. Industry-wide, the positive ROI on quality data is well understood. 2 – Data profiling. 4 – Data Reporting.

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BMW Cloud Efficiency Analytics powered by Amazon QuickSight and Amazon Athena

AWS Big Data

BMW Cloud Efficiency Analytics (CLEA) is a homegrown tool developed within the BMW FinOps CoE (Center of Excellence) aiming to optimize and reduce costs across all these accounts. In this post, we explore how the BMW Group FinOps CoE implemented their Cloud Efficiency Analytics tool (CLEA), powered by Amazon QuickSight and Amazon Athena.

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Enforce fine-grained access control on Open Table Formats via Amazon EMR integrated with AWS Lake Formation

AWS Big Data

Incremental query refers to a query strategy that focuses on processing and analyzing only the new or updated data within a data lake since the last query. The key idea behind incremental queries is to use metadata or change tracking mechanisms to identify the new or modified data since the last query.

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Alation and dbt Unlock Metadata and Increase Modern Data Stack Visibility

Alation

Data analysts and engineers use dbt to transform, test, and document data in the cloud data warehouse. Yet every dbt transformation contains vital metadata that is not captured – until now. Data Transformation in the Modern Data Stack. How did the data transform exactly?

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The Modern Data Stack Explained: What The Future Holds

Alation

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse.