97 Things Every Data Engineer Should Know - Collective Wisdom from the Experts

"Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges."--

Info autore

Tobias Macey hosts the Data Engineering Podcast and Podcast.\_\_init\_\_ where he discusses the tools, topics, and people that comprise the data engineering and Python communities respectively. His experience across the domains of infrastructure, software, cloud, and data engineering allows him to ask informed questions and bring useful context to the discussions. The ongoing focus of his career is to help educate people, through designing and building platforms that power online learning, consulting with companies and investors to understand the possibilities of emerging technologies, and leading teams of engineers to help them grow professionally.

Riassunto

With this in-depth book, current and aspiring engineers will learn powerful, real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, StitchFix, Microsoft and Capital One share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges.

Dettagli sul prodotto

• Autori: Tobias Berlin, Tobias Macey
• Editore: O'Reilly
• Lingue: Inglese
• Formato: Tascabile
• Pubblicazione: 30.06.2021
• EAN: 9781492062417
• ISBN: 978-1-4920-6241-7
• Dimensioni: 152 mm x 229 mm x 18 mm
• Peso: 392 g
• Categorie: Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Informatica; Data Mining, Data Warehousing, COMPUTERS / Data Science / Data Visualization, COMPUTERS / Data Science / Data Analytics, COMPUTERS / Database Administration & Management, Data capture and analysis, Data Capture & Analysis, COMPUTERS / Data Science / Data Warehousing, Software testing and verification, Software testing & verification