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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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Complexity Drives Costs: A Look Inside BYOD and Azure Data Lakes

Jet Global

Ostensibly, the new product represents Microsoft’s transition to a newer, more cloud-friendly ERP for midsized enterprises. Reporting will change in D365 F&SCM, and those changes could significantly increase complexity and total cost of ownership. That works reasonably well for traditional reporting functions.

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

Jet Global

Consultants and developers familiar with the AX data model could query the database using any number of different tools, including a myriad of different report writers. The SQL query language used to extract data for reporting could also potentially be used to insert, update, or delete records from the database.

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5 misconceptions about cloud data warehouses

IBM Big Data Hub

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics, that enable faster decision making and insights.

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Data Lakes: What Are They and Who Needs Them?

Jet Global

The sheer scale of data being captured by the modern enterprise has necessitated a monumental shift in how that data is stored. What was at first a data stream has morphed into a data river as enterprise businesses are harvesting reams of data from every conceivable input across every conceivable business function.

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Implementing a Pharma Data Mesh using DataOps

DataKitchen

Data mesh and DataOps provide the organization, enterprise architecture, and workflow automation that together enable a relatively small data team to address the analytics needs of hundreds of active business users. Figure 1: Data requirements for phases of the drug product lifecycle. The new Recipes run, and BOOM!

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Integrating Data Governance and Enterprise Architecture

erwin

Why should you integrate data governance (DG) and enterprise architecture (EA)? Two of the biggest challenges in creating a successful enterprise architecture initiative are: collecting accurate information on application ecosystems and maintaining the information as application ecosystems change.