000			02737nam a2200337 4500
003			OSt
005			20251117101029.0
008			251106b |||||||| |||| 00| 0 eng d
020			_a9789355421548 _q(paperback)
040			_erda _aSAIU _dSAIU
082			_223 _a005.743
100	1	0	_aReis, Joe _q(Joseph), _eauthor
245	1	0	_aFundamentals of data engineering : _bplan and build robust data systems / _cJoe Reis and Matt Housley.
250			_aFirst edition. _b7th Indian reprint 2025
260			_aIndia : _bShroff / O'Reilly Media, _c2022.
264	1		_aIndia : _bShroff / O'Reilly Media, _c2022.
264	4		_c©2022.
300			_axiii, 407 pages : _b illustrations (black and white) ; _c24 cm.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
504			_aIncludes bibliographical references and index.
505	0		_aData engineering described -- The data engineering lifecycle -- Designing good data architecture -- Choosing technologies across the data engineering lifecycle -- Data generation in source systems -- Storage -- Ingestion -- Queries, modeling, and transformation -- Serving data for analytics, machine learning, and reverse ETL -- Security and privacy -- The future of data engineering.
520			_aData engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you will learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available in the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You will understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, governance, and deployment that are critical in any data environment regardless of the underlying technology. This book will help you: Get a concise overview of the entire data engineering landscape ; Assess data engineering problems using an end-to-end data framework of best practices ; Cut through marketing hype when choosing data technologies, architecture, and processes ; Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle.
650	0		_aDatabase design
650	0		_aComputer architecture
650	0		_aDatabase management
650	0		_aBig data
700	1		_aHousley, Matthew L., _d1977- _eauthor
942			_2ddc _cBK
999			_c6898 _d6898