Remove Data Analytics Remove Data Warehouse Remove Unstructured Data
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Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

The market for data warehouses is booming. While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes. Data Warehouse.

Data Lake 135
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Domo Addresses Data Products and Agentic AI

David Menninger's Analyst Perspectives

The scope spans data integrationbuilding on more than 1,000 pre-built connectors and supported by data governance and data securitythrough BI and AI functionality for both data analysts and business users to application development, workflow automation and data sharing.

Metrics 130
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Understanding Structured and Unstructured Data

Sisense

Different types of information are more suited to being stored in a structured or unstructured format. Read on to explore more about structured vs unstructured data, why the difference between structured and unstructured data matters, and how cloud data warehouses deal with them both.

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Building a Beautiful Data Lakehouse

CIO Business Intelligence

Applying artificial intelligence (AI) to data analytics for deeper, better insights and automation is a growing enterprise IT priority. But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI.

Data Lake 119
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The Lakehouse Isn’t The End Game — Here’s What Comes Next

Data Virtualization

Reading Time: 2 minutes The data lakehouse has emerged as a powerful and popular data architecture, combining the scale of data lakes with the management features of data warehouses. It promises a unified platform for storing and analyzing structured and unstructured data, particularly for.

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Acquisitions on the Horizon in BI and Data Analytics Industry?

Sisense

2019 can best be described as an era of modern cloud data analytics. Convergence in an industry like data analytics can take many forms. We have seen industry rollups in which firms create a collection of analytical tools under one brand. Realizing a Flexible, Multi-Cloud, Open-Platform, Data Hub-Driven Future.

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The DataOps Vendor Landscape, 2021

DataKitchen

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Amaterasu — is a deployment tool for data pipelines.

Testing 304