Remove Building Remove Definition Remove Metadata Remove Structured Data
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Data Warehouse vs Data Lake vs Data Lakehouse: Definitions, Similarities, and Differences

Monte Carlo

Understanding data warehouses A data warehouse is a consolidated storage unit and processing hub for your data. Teams using a data warehouse usually leverage SQL queries for analytics use cases. This same structure aids in maintaining data quality and simplifies how users interact with and understand the data.

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Taking Charge of Tables: Introducing OpenHouse for Big Data Management

LinkedIn Engineering

Co-Authors: Sumedh Sakdeo , Lei Sun , Sushant Raikar , Stanislav Pak , and Abhishek Nath Introduction At LinkedIn, we build and operate an open source data lakehouse deployment to power Analytics and Machine Learning workloads. While functional, our current setup for managing tables is fragmented.

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How Cloudera Data Flow Enables Successful Data Mesh Architectures

Cloudera

Those decentralization efforts appeared under different monikers through time, e.g., data marts versus data warehousing implementations (a popular architectural debate in the era of structured data) then enterprise-wide data lakes versus smaller, typically BU-Specific, “data ponds”.

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Top Data Catalog Tools

Monte Carlo

A data catalog is a constantly updated inventory of the universe of data assets within an organization. It uses metadata to create a picture of the data, as well as the relationships between data assets of diverse sources, and the processing that takes place as data moves through systems.

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What is Data Fabric: Architecture, Principles, Advantages, and Ways to Implement

AltexSoft

What’s more, Gartner identifies data fabric implementation as one of the top strategic technology trends for 2022 and expects that by 2024, data fabric deployments will increase the efficiency of data use while halving human-driven data management tasks. What is data fabric? How data fabric works.

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Data Mesh Implementation: Your Blueprint for a Successful Launch

Ascend.io

For one, data mesh tackles the real headaches caused by an overburdened data lake and the annoying game of tag that’s too often played between the people who make data, the ones who use it, and everyone else caught in the middle. Establish clear data governance policies.

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Creating Value With a Data-Centric Culture: Essential Capabilities to Treat Data as a Product

Ascend.io

Data Ingestion Data in today’s businesses come from an array of sources, including various clouds, APIs, warehouses, and applications. This multitude of sources often causes a dispersed, complex, and poorly structured data landscape. Data stewards are thus the best contributors to the content of data catalogs.