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Advancing AI: The emergence of a modern information lifecycle

CIO Business Intelligence

A modern information lifecycle management approach Today’s ILM approach recognizes the enterprise value of all digitized and enriched assets , avoiding the habituated, narrow reliance ontraditional structured data. A modern ILM approach helps CIOs and their teams align processes to business objectives and regulatory requirements.

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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

It covers how to use a conceptual, logical architecture for some of the most popular gaming industry use cases like event analysis, in-game purchase recommendations, measuring player satisfaction, telemetry data analysis, and more. Data in Amazon S3 can be easily queried in place using SQL with Amazon Redshift Spectrum.

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

AWS Big Data

You can use AWS Glue Studio to create jobs that extract structured or semi-structured data from a data source, perform a transformation of that data, and save the result set in a data target. AWS Glue Studio is a graphical interface that makes it simple to create, run, and monitor ETL jobs in AWS Glue.

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

This can be achieved using AWS Entity Resolution , which enables using rules and machine learning (ML) techniques to match records and resolve identities. With QuickSight, you can embed dashboards to external websites and applications , and the SPICE engine enables rapid, interactive data visualization at scale.

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Building Better Data Models to Unlock Next-Level Intelligence

Sisense

We’re going to nerd out for a minute and dig into the evolving architecture of Sisense to illustrate some elements of the data modeling process: Historically, the data modeling process that Sisense recommended was to structure data mainly to support the BI and analytics capabilities/users.

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Big Data Fabric Weaves Together Automation, Scalability, and Intelligence

Cloudera

Today’s data landscape is characterized by exponentially increasing volumes of data, comprising a variety of structured, unstructured, and semi-structured data types originating from an expanding number of disparate data sources located on-premises, in the cloud, and at the edge. Source: Cloudera.