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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Azure Databricks is a unified collaborative platform for performing scalable analytics in an interactive environment. The Azure Databricks Cookbook provides recipes to get hands-on with the analytics process, including ingesting data from various batch and streaming sources and building a modern data warehouse.
The book starts by teaching you how to create an Azure Databricks instance within the Azure portal, Azure CLI, and ARM templates. You’ll work through clusters in Databricks and explore recipes for ingesting data from sources, including files, databases, and streaming sources such as Apache Kafka and EventHub. The book will help you explore all the features supported by Azure Databricks for building powerful end-to-end data pipelines. You'll also find out how to build a modern data warehouse by using Delta tables and Azure Synapse Analytics. Later, you’ll learn how to write ad hoc queries and extract meaningful insights from the data lake by creating visualizations and dashboards with Databricks SQL. Finally, you'll deploy and productionize a data pipeline as well as deploy notebooks and Azure Databricks service using continuous integration and continuous delivery (CI/CD).
By the end of this Azure book, you'll be able to use Azure Databricks to streamline different processes involved in building data-driven apps.
Das E-Book können Sie in Legimi-Apps oder einer beliebigen App lesen, die das folgende Format unterstützen:
Seitenzahl: 363
Veröffentlichungsjahr: 2021
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Azure Databricks Cookbook
Accelerate and scale real-time analytics solutions using the Apache Spark-based analytics service
Phani Raj
Vinod Jaiswal
BIRMINGHAM—MUMBAI
Azure Databricks Cookbook
Copyright © 2021 Packt Publishing
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First published: September 2021
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ISBN 978-1-78980-971-8
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Contributors
About the authors
Phani Raj is an Azure data architect at Microsoft. He has more than 12 years of IT experience and works primarily on the architecture, design, and development of complex data warehouses, OLTP, and big data solutions on Azure for customers across the globe.
Vinod Jaiswal is a data engineer at Microsoft. He has more than 13 years of IT experience and works primarily on the architecture, design, and development of complex data warehouses, OLTP, and big data solutions on Azure using Azure data services for a variety of customers. He has also worked on designing and developing real-time data processing and analytics reports from the data ingested from streaming systems using Azure Databricks.
About the reviewers
Ankur Nayyar is an architect who builds and deploys advanced, customer-specific big data analytics solutions using his advanced knowledge of data architecture, data mining, machine learning, computer programming, and emerging open source technologies such as Apache Spark. He stays up to date with the recent advancements in the machine learning/AI industry, such as TensorFlow, and actively prototypes with these open source frameworks. He has participated in internal and external technology and data science forums, competitions, conferences, and discussions.
Alan Bernardo Palacio is a data scientist and an engineer with vast experience in different engineering fields. His focus has been the development and application of state-of-the-art data products and algorithms in several industries. He has worked for companies such as Ernst and Young, Globant, and now holds a data engineer position at Ebiquity Media helping the company to create a scalable data pipeline. Alan graduated with a mechanical engineering degree from the National University of Tucuman in 2015, participated as the founder in startups, and later on earned a master's degree from the Faculty of Mathematics in the Autonomous University of Barcelona in 2017. Originally from Argentina, he now works and resides in the Netherlands.
