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Introduction to Data Lakes: Definition and purpose of Data Lakes. Distinction between Data Lakes and other data storage architectures (e.g., Data Warehouses). Components of a Data Lake: Understanding the essential components such as storage, processing, and metadata management.
A data lake is an agile storage platform that can be easily configured for any given data model, structure, application, or query. Data lake agility enables multiple and advanced analytical methods to interpret the data. Being a schema on read makes a data lake scalable and flexible.
Data lakes allow you to store relational data like operational databases and data from line of business applications, and non-relational data like mobile apps, IoT devices, and social media. They also give you the ability to understand what data is in the lake through crawling, cataloging, and indexing of data.
Explore data lakes for storing large volumes of raw data. Learn to design and manage data lakes for scalable and flexible data storage.
15 lip 2024 · Explore the essence of data lakes in modern data architecture: scalable repositories storing diverse data types for agile analytics and innovation, transforming business insights across industries.
30 sie 2024 · Solutions for: Preview. Complete Guide to Data Lakes and Lakehouses. With Thalia Barrera. Duration: 5h 39m Skill level: Advanced Released: 8/30/2024. Start my 1-month free trial Buy for my...
This is an in-depth course designed to equip you with the knowledge and skills to harness the full potential of data lakes. Dive into the world of modern data management, understand the benefits and limitations of data lakes, and learn to leverage them for enhanced business intelligence.
