Remove Data Ingestion Remove Data Lake Remove Data Management Remove Unstructured Data
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Discover And De-Clutter Your Unstructured Data With Aparavi

Data Engineering Podcast

Summary Unstructured data takes many forms in an organization. From a data engineering perspective that often means things like JSON files, audio or video recordings, images, etc. Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. In fact, while only 3.5%

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Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

AltexSoft

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. What is a data lake?

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What Are the Best Data Modeling Methodologies & Processes for My Data Lake?

phData: Data Engineering

With the amount of data companies are using growing to unprecedented levels, organizations are grappling with the challenge of efficiently managing and deriving insights from these vast volumes of structured and unstructured data. What is a Data Lake? Consistency of data throughout the data lake.

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Top Data Lake Vendors (Quick Reference Guide)

Monte Carlo

Data lakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption.

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5 Layers of Data Lakehouse Architecture Explained

Monte Carlo

Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. Table of Contents What is data lakehouse architecture? The 5 key layers of data lakehouse architecture 1.

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Data Lakehouse Architecture Explained: 5 Layers

Monte Carlo

Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. Table of Contents What is data lakehouse architecture? The 5 key layers of data lakehouse architecture 1.

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How to Keep Track of Data Versions Using Versatile Data Kit

Towards Data Science

One such tool is the Versatile Data Kit (VDK), which offers a comprehensive solution for controlling your data versioning needs. VDK helps you easily perform complex operations, such as data ingestion and processing from different sources, using SQL or Python. Use VDK to build a data lake and merge multiple sources.