97 things every data engineer should know :
97 things every data engineer should know : collective wisdom from the experts
Ninety-seven things every data engineer should know Things every data engineer should know
edited by Tobias Macey
- Mumbai : Shroff Publishers, ©2021
- xiv, 248 p. : ill. ; 24 cm
Includes index.
Includes bibliographical references and index.
A (book) case for eventual consistency / A/B and how to be / About the storage layer / Analytics as the secret glue for microservice architectures / Automate your infrastructure / Automate your pipeline tests / Be intentional about the batching model in your data pipelines / Beware of silver-bullet syndrome / Building a career as a data engineer / Business dashboards for data pipelines / Caution : data science projects can turn into the emperor's new clothes / Change data capture / Column names as contracts / Consensual, privacy-aware data collection / Cultivate good working relationships with data consumers / Data engineering !=Spark / Data engineering for autonomy and rapid innovation / Data engineering from a data scientist's perspective / Data pipeline design patterns for reusability and extensibility / Data quality for data engineers / Data security for data engineers / Data validation is more than summary statistics / Data warehouses are the past, present, and future / Defining and managing messages in log-centric architectures / Demystify the source and illuminate the data pipeline / Develop communities, not just code / Effective data engineering in the cloud world / Embracing data silos / Engineering reproducible data science projects / Five best practices for stable data processing / Focus on maintainability and break up those ETL tasks / Denise Koessler Gosnell, PhD -- Sonia Mehta -- Julien Le Dem -- Elias Nema -- Christiano Anderson -- Tom White -- Raghotham Murthy -- Thomas Nield -- Vijay Kiran -- Valliappa (Lak) Lakshmanan -- Shweta Katre -- Raghotham Murthy -- Emily Riederer -- Katharine Jarmul -- Ido Shlomo -- Jesse Anderson -- Jeff Magnusson -- Bill Franks -- Mukul Sood -- Katharine Jarmul -- Katharine Jarmul -- Emily Riederer -- James Densmore -- Boris Lublinsky -- Meghan Kwartler -- Emily Riederer -- Dipti Borkar -- Bin Fan and Amelia Wong -- Dr. Tianhui Michael Li -- Christian Lauer -- Chris Moradi
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. Edited by Tobias Macey, host of the popular "Data engineering podcast", this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers.--
9789391043599
2022439040
GBC175691 bnb
020194174 Uk
Big data.
Data sets.
Data collection platforms.
Databases.
Electronic data processing.
Datasets as Topic
Données volumineuses.
Jeux de données.
Plateformes de collecte de données.
Data collection platforms.
Data sets.
QA76.9.D343 / A235 2021
006.312 / MAC-9
Includes index.
Includes bibliographical references and index.
A (book) case for eventual consistency / A/B and how to be / About the storage layer / Analytics as the secret glue for microservice architectures / Automate your infrastructure / Automate your pipeline tests / Be intentional about the batching model in your data pipelines / Beware of silver-bullet syndrome / Building a career as a data engineer / Business dashboards for data pipelines / Caution : data science projects can turn into the emperor's new clothes / Change data capture / Column names as contracts / Consensual, privacy-aware data collection / Cultivate good working relationships with data consumers / Data engineering !=Spark / Data engineering for autonomy and rapid innovation / Data engineering from a data scientist's perspective / Data pipeline design patterns for reusability and extensibility / Data quality for data engineers / Data security for data engineers / Data validation is more than summary statistics / Data warehouses are the past, present, and future / Defining and managing messages in log-centric architectures / Demystify the source and illuminate the data pipeline / Develop communities, not just code / Effective data engineering in the cloud world / Embracing data silos / Engineering reproducible data science projects / Five best practices for stable data processing / Focus on maintainability and break up those ETL tasks / Denise Koessler Gosnell, PhD -- Sonia Mehta -- Julien Le Dem -- Elias Nema -- Christiano Anderson -- Tom White -- Raghotham Murthy -- Thomas Nield -- Vijay Kiran -- Valliappa (Lak) Lakshmanan -- Shweta Katre -- Raghotham Murthy -- Emily Riederer -- Katharine Jarmul -- Ido Shlomo -- Jesse Anderson -- Jeff Magnusson -- Bill Franks -- Mukul Sood -- Katharine Jarmul -- Katharine Jarmul -- Emily Riederer -- James Densmore -- Boris Lublinsky -- Meghan Kwartler -- Emily Riederer -- Dipti Borkar -- Bin Fan and Amelia Wong -- Dr. Tianhui Michael Li -- Christian Lauer -- Chris Moradi
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. Edited by Tobias Macey, host of the popular "Data engineering podcast", this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers.--
9789391043599
2022439040
GBC175691 bnb
020194174 Uk
Big data.
Data sets.
Data collection platforms.
Databases.
Electronic data processing.
Datasets as Topic
Données volumineuses.
Jeux de données.
Plateformes de collecte de données.
Data collection platforms.
Data sets.
QA76.9.D343 / A235 2021
006.312 / MAC-9