000			03838nam a2200349uu 4500
005			20250710182908.0
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024	8	0	_a9781837632114
040			_aPACKT _cPACKT
041			_aen
044			_aGB
100	0		_aDumky De Wilde _eauthor.
700	0		_aFanny Kassapian _eauthor.
700	0		_aJovan Gligorevic _eauthor.
700	0		_aJuan Manuel Perafan _eauthor.
700	0		_aLasse Benninga _eauthor.
700	0		_aRicardo Angel Granados Lopez _eauthor.
700	0		_aTaís Laurindo Pereira _eauthor.
700	0		_aPádraic Slattery _eauthor.
710	2		_aPACKT
773	0		_tFundamentals of Analytics Engineering _dGB,Packt,2024-03-29 _h332
245	0	0	_aFundamentals of Analytics Engineering.
300			_a332.
377			_aen
260			_aGB: _bPackt, _c2024-03-29.
263			_a2024-03-29
264		1	_aGB: _bPackt,
520			_a<p><b>Gain a holistic understanding of the analytics engineering lifecycle by integrating principles from both data analysis and engineering</b></p><h4>Key Features</h4><ul><li>Discover how analytics engineering aligns with your organization's data strategy</li><li>Access insights shared by a team of seven industry experts</li><li>Tackle common analytics engineering problems faced by modern businesses</li><li>Purchase of the print or Kindle book includes a free PDF eBook</li></ul><h4>Book Description</h4>Written by a team of 7 industry experts, Fundamentals of Analytics Engineering will introduce you to everything from foundational concepts to advanced skills to get started as an analytics engineer. After conquering data ingestion and techniques for data quality and scalability, you'll learn about techniques such as data cleaning transformation, data modeling, SQL query optimization and reuse, and serving data across different platforms. Armed with this knowledge, you will implement a simple data platform from ingestion to visualization, using tools like Airbyte Cloud, Google BigQuery, dbt, and Tableau. You'll also get to grips with strategies for data integrity with a focus on data quality and observability, along with collaborative coding practices like version control with Git. You'll learn about advanced principles like CI/CD, automating workflows, gathering, scoping, and documenting business requirements, as well as data governance. By the end of this book, you'll be armed with the essential techniques and best practices for developing scalable analytics solutions from end to end.<h4>What you will learn</h4><ul><li>Design and implement data pipelines from ingestion to serving data</li><li>Explore best practices for data modeling and schema design</li><li>Scale data processing with cloud based analytics platforms and tools</li><li>Understand the principles of data quality management and data governance</li><li>Streamline code base with best practices like collaborative coding, version control, reviews and standards</li><li>Automate and orchestrate data pipelines</li><li>Drive business adoption with effective scoping and prioritization of analytics use cases</li></ul><h4>Who this book is for</h4>This book is for data engineers and data analysts considering pivoting their careers into analytics engineering. Analytics engineers who want to upskill and search for gaps in their knowledge will also find this book helpful, as will other data professionals who want to understand the value of analytics engineering in their organization's journey toward data maturity. To get the most out of this book, you should have a basic understanding of data analysis and engineering concepts such as data cleaning, visualization, ETL and data warehousing.
538			_aData in extended ASCII character set.
538			_aMode of access: Internet.
856	4	0	_uhttps://learning.packt.com/product/470890
999			_c16087 _d16087