Enable data and AI workloads with absolute security and scalability

Key Features
● Detailed, step-by-step instructions for every data professional starting a career with data engineering.
● Access to DevOps, Machine Learning, and Analytics wirthin a single unified platform.
● Includes design considerations and security best practices for efficient utilization of Databricks platform.

Description
Starting with the fundamentals of the databricks lakehouse platform, the book teaches readers on administering various data operations, including Machine Learning, DevOps, Data Warehousing, and BI on the single platform.
The subsequent chapters discuss working around data pipelines utilizing the databricks lakehouse platform with data processing and audit quality framework. The book teaches to leverage the Databricks Lakehouse platform to develop delta live tables, streamline ETL/ELT operations, and administer data sharing and orchestration. The book explores how to schedule and manage jobs through the Databricks notebook UI and the Jobs API. The book discusses how to implement DevOps methods on the Databricks Lakehouse platform for data and AI workloads. The book helps readers prepare and process data and standardizes the entire ML lifecycle, right from experimentation to production.
The book doesn't just stop here; instead, it teaches how to directly query data lake with your favourite BI tools like Power BI, Tableau, or Qlik. Some of the best industry practices on building data engineering solutions are also demonstrated towards the end of the book.

What you will learn
● Acquire capabilities to administer end-to-end Databricks Lakehouse Platform.
● Utilize Flow to deploy and monitor machine learning solutions.
● Gain practical experience with SQL Analytics and connect Tableau, Power BI, and Qlik.
● Configure clusters and automate CI/CD deployment.
● Learn how to use Airflow, Data Factory, Delta Live Tables, Databricks notebook UI, and the Jobs API.
Who this book is for
This book is for every data professional, including data engineers, ETL developers, DB administrators, Data Scientists, SQL Developers, and BI specialists. You don't need any prior expertise with this platform because the book covers all the basics.

Table of Contents
1. Getting started with Databricks Platform
2. Management of Databricks Platform
3. Spark, Databricks, and Building a Data Quality Framework
4. Data Sharing and Orchestration with Databricks
5. Simplified ETL with Delta Live Tables
6. SCD Type 2 Implementation with Delta Lake
7. Machine Learning Model Management with Databricks
8. Continuous Integration and Delivery with Databricks
9. Visualization with Databricks
10. Best Security and Compliance Practices of Databricks

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