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Top 8 Data Engineering Books [Beginners to Advanced]

Knowledge Hut

Key Benefits and Takeaways: Understand data intake strategies and data transformation procedures by learning data engineering principles with Python. Investigate alternative data storage solutions, such as databases and data lakes. Key Benefits and Takeaways: Learn the core concepts of big data systems.

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RocksDB Is Eating the Database World

Rockset

While traditional RDBMS databases served well the data storage and data processing needs of the enterprise world from their commercial inception in the late 1970s until the dotcom era, the large amounts of data processed by the new applications—and the speed at which this data needs to be processed—required a new approach.

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Hands-On Introduction to Delta Lake with (py)Spark

Towards Data Science

Concepts, theory, and functionalities of this modern data storage framework Photo by Nick Fewings on Unsplash Introduction I think it’s now perfectly clear to everybody the value data can have. To use a hyped example, models like ChatGPT could only be built on a huge mountain of data, produced and collected over years.

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Kafka vs RabbitMQ - A Head-to-Head Comparison for 2023

ProjectPro

Kafka is a commit- log/message-processing implementation that stresses data storage and retrieval more, with scalability and data redundancy. It implements an intelligent client/subscriber policy where the consumer is solely responsible for requesting and retrieving data from the broker. What is Kafka?

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Big Data Timeline- Series of Big Data Evolution

ProjectPro

The largest item on Claude Shannon’s list of items was the Library of Congress that measured 100 trillion bits of data. 1960 - Data warehousing became cheaper. 1996 - Digital data storage became cost effective than paper - according to R.J.T. Morris and B.J. Truskowski. US alone will face a shortage of 1.5