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The Top Three Entangled Trends in Data Architectures: Data Mesh, Data Fabric, and Hybrid Architectures

Cloudera

Each of these trends claim to be complete models for their data architectures to solve the “everything everywhere all at once” problem. Data teams are confused as to whether they should get on the bandwagon of just one of these trends or pick a combination. First, we describe how data mesh and data fabric could be related.

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Large Language Models and Data Management

Ontotext

I did some research because I wanted to create a basic framework on the intersection between large language models (LLM) and data management. But there are also a host of other issues (and cautions) to take into consideration. Another concern relates to the definition of ‘data constraints.’

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Delivering Power Platform Projects: Truths from the Field

Jen Stirrup

It is hard to get a definitive answer. However, you still need to think properly about data. Logic Apps are hosted in Azure and have a code view rather than a ‘business user coder’ view. Perception. The perception from the Microsoft advertising is that the technology is super easy and point and click.

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Announcing the 2020 Data Impact Award Winners

Cloudera

The technological linchpin of its digital transformation has been its Enterprise Data Architecture & Governance platform. It hosts over 150 big data analytics sandboxes across the region with over 200 users utilizing the sandbox for data discovery.

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Migrate Microsoft Azure Synapse Analytics to Amazon Redshift using AWS SCT

AWS Big Data

Grant VIEW DEFINITION and VIEW DATABASE STATE to each schema you are trying to convert to the database user used for migration. Create a Redshift Serverless data warehouse In this step, we create a Redshift Serverless data warehouse with a workgroup and namespace. Select Redshift data agent , then choose OK.

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For IT leaders, operationalized gen AI is still a moving target

CIO Business Intelligence

And not only do companies have to get all the basics in place to build for analytics and MLOps, but they also need to build new data structures and pipelines specifically for gen AI. And for some use cases, an expensive, high-end commercial LLM might not be required since a locally-hosted open source model might suffice.

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A guide to efficient Oracle implementation

IBM Big Data Hub

According to Oracle , best practices for the planning process include five categories of information: Project definition: This is the blueprint that will include relevant information for an implementation project. Oracle provides a variety of templates to facilitate item definition as data is catalogued and moved to the cloud.

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