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001			23049307
005			20250114020005.0
008			230405s2022 caua b 001 0 eng d
010			_a 2023275148
015			_aGBC2A6016 _2bnb
016	7		_a020652890 _2Uk
020			_a9781098108304
020			_a1098108302
035			_a(OCoLC)on1334138491
040			_aUKMGB _beng _cUKMGB _erda _dOCLCF _dJRZ _dSISPL _dOCO _dVP@ _dJCX _dYDX _dTOH _dOCL _dDLC
042			_alccopycat
050	0	0	_aQA76.9.D26 _b.R45 2022
082	0	4	_a005.743 _223
100	1		_aReis, Joe _q(Joseph), _eauthor. _927305
245	1	0	_aFundamentals of data engineering : _bplan and build robust data systems / _cJoe Reis and Matt Housley.
250			_aFirst edition.
300			_axix, 422 pages : _billustrations (black and white) ; _c24 cm
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. _959
650		0	_aComputer architecture. _91384
650		0	_aDatabase management. _960
650		0	_aBig data.
650		7	_aCOMPUTERS / Data Science / Data Modeling & Design. _2bisacsh _927306
650		7	_aDatabase management. _2fast _960
650		7	_aBig data. _2fast
650		7	_aComputer architecture. _2fast _91384
650		7	_aDatabase design. _2fast _959
700	1		_aHousley, Matthew L., _d1977- _eauthor. _927307
906			_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg
942			_2lcc _cBK _kQA76.9 .D26 _m.R45 2022 _01
999			_c9397 _d9397