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Mastering MongoDB 7.0 - Fourth Edition

You're reading from  Mastering MongoDB 7.0 - Fourth Edition

Product type Book
Published in Jan 2024
Publisher Packt
ISBN-13 9781835460474
Pages 434 pages
Edition 4th Edition
Languages
Concepts
Authors (7):
Marko Aleksendrić Marko Aleksendrić
Profile icon Marko Aleksendrić
Arek Borucki Arek Borucki
Profile icon Arek Borucki
Leandro Domingues Leandro Domingues
Profile icon Leandro Domingues
Malak Abu Hammad Malak Abu Hammad
Profile icon Malak Abu Hammad
Elie Hannouch Elie Hannouch
Profile icon Elie Hannouch
Rajesh Nair Rajesh Nair
Profile icon Rajesh Nair
Rachelle Palmer Rachelle Palmer
Profile icon Rachelle Palmer
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Table of Contents (20) Chapters

Preface 1. Chapter 1: Introduction to MongoDB 2. Chapter 2: The MongoDB Architecture 3. Chapter 3: Developer Tools 4. Chapter 4: Connecting to MongoDB 5. Chapter 5: CRUD Operations and Basic Queries 6. Chapter 6: Schema Design and Data Modeling 7. Chapter 7: Advanced Querying in MongoDB 8. Chapter 8: Aggregation 9. Chapter 9: Multi-Document ACID Transactions 10. Chapter 10: Index Optimization 11. Chapter 11: MongoDB Atlas: Powering the Future of Developer Data Platforms 12. Chapter 12: Monitoring and Backup in MongoDB 13. Chapter 13: Introduction to Atlas Search 14. Chapter 14: Integrating Applications with MongoDB 15. Chapter 15: Security 16. Chapter 16: Auditing 17. Chapter 17: Encryption 18. Index 19. Other Books You May Enjoy

Atlas Data Lake

MongoDB Atlas Data Lake is an analytics-optimized object storage service designed for extracted data. It provides an analytic storage service optimized for both flat and nested data, ensuring low-latency query performance.

Essentially, the data lake capability enables you to run a single query that will route to either object storage or a database. This allows for more advantageous data storage use cases, including the ability to handle data stored in various formats outside of JSON and BSON, such as CSV, TSV, Parquet files, and the like.

Atlas Data Lake requires a paid tier cluster usage with backup enabled. It supports collection snapshots from Atlas clusters as a data source for extracted data. The service automatically ingests data from the snapshots, partitions it, and stores it in an analytics-optimized format.

Data storage and optimization

Atlas Data Lake stores data in Parquet files, an analytic-oriented format based on open source standards, with...

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