Palacio Distributed Data Systems with Azure Databricks

Create, deploy, and manage enterprise data pipelines
1. Auflage 2021
ISBN: 978-1-83864-269-3
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 414 Seiten

No detailed description available for "Distributed Data Systems with Azure Databricks".

Autoren/Hrsg.

Palacio, Alan Bernardo

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Warehouse
• Mathematik | Informatik EDV | Informatik Business Application Groupware
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse

Inhaltsverzeichnis

Table of Contents - Introduction to Azure Databricks core concepts
- Creating an Azure Databricks workspace
- Creating an ETL with Databricks
- Delta Lake with Databricks
- Introducing Delta Engine
- Structured Streaming

- Azure Databricks integration with Popular Python Libraries
- Databricks Runtime for Machine Learning

- Databricks Runtime for Deep Learning
- Model tuning, deployment and control Using DataBricks AutoML
- MLFlow on Azure Databricks
- Distributed Deep Learning with Horovod

Über Autor(innen)

Palacio Alan Bernardo:

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.