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Enhance data security and governance for Amazon Redshift Spectrum with VPC endpoints

AWS Big Data

Many customers are extending their data warehouse capabilities to their data lake with Amazon Redshift. They are looking to further enhance their security posture where they can enforce access policies on their data lakes based on Amazon Simple Storage Service (Amazon S3).

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Data architecture strategy for data quality

IBM Big Data Hub

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Steps for developing an effective data strategy include: 1.

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Navigating Data Entities, BYOD, and Data Lakes in Microsoft Dynamics

Jet Global

Now, instead of making a direct call to the underlying database to retrieve information, a report must query a so-called “data entity” instead. Each data entity provides an abstract representation of business objects within the database, such as, customers, general ledger accounts, or purchase orders.

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How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

This post also discusses the art of the possible with newer innovations in AWS services around streaming, machine learning (ML), data sharing, and serverless capabilities. Flexible and easy to use – The solutions should provide less restrictive, easy-to-access, and ready-to-use data.

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Migrate your existing SQL-based ETL workload to an AWS serverless ETL infrastructure using AWS Glue

AWS Big Data

This solution decouples the ETL and analytics workloads from our transactional data source Amazon Aurora, and uses Amazon Redshift as the data warehouse solution to build a data mart. We use Amazon Redshift as the data warehouse to implement the data mart solution. Wait for all the jobs to complete.

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The Future of AI in the Enterprise

Jet Global

Which problems do disparate data points speak to? And how can the data collected across multiple touchpoints, from retail locations to the supply chain to the factory be easily integrated? Enter data warehousing.

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The Future of AI in the Enterprise

Jet Global

Which problems do disparate data points speak to? And how can the data collected across multiple touchpoints, from retail locations to the supply chain to the factory be easily integrated? Enter data warehousing.