You're reading from Modern Data Architectures with Python A practical guide to building and deploying data pipelines, data warehouses, and data lakes with Python

Product type Paperback

Published in Sep 2023

Publisher Packt

ISBN-13 9781801070492

Length 318 pages

Edition 1st Edition

Languages

Python

Tools

MLflow

Concepts

Data Science

Author (1):

Brian Lipp

View More author details

Table of Contents (19) Chapters

Preface

1. Part 1:Fundamental Data Knowledge

2. Chapter 1: Modern Data Processing Architecture FREE CHAPTER

3. Chapter 2: Understanding Data Analytics

4. Part 2: Data Engineering Toolset

5. Chapter 3: Apache Spark Deep Dive

6. Chapter 4: Batch and Stream Data Processing Using PySpark

7. Chapter 5: Streaming Data with Kafka

8. Part 3:Modernizing the Data Platform

9. Chapter 6: MLOps

10. Chapter 7: Data and Information Visualization

11. Chapter 8: Integrating Continous Integration into Your Workflow

12. Chapter 9: Orchestrating Your Data Workflows

13. Part 4:Hands-on Project

14. Chapter 10: Data Governance

15. Chapter 11: Building out the Groundwork

16. Chapter 12: Completing Our Project

17. Index

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Databricks SQL analytics

Databricks SQL analytics is an evolved section of Databricks adapted for SQL-only analysis and BI access. What makes SQL analytics unique is the tight integration between all the other tooling. So, when your Databricks pipelines publish tables, SQL analytics will be able to access all those artifacts.

Accessing SQL analytics

At the time of writing, Databricks SQL analytics is offered with only premium-tier accounts. Once you have switched to the premium tier, you will see SQL in the area drop-down menu.

Once you have enabled the premium tier or higher, to access SQL analytics, use the dropdown at the top left of the screen. You will be given three choices – Data Science & Engineering, Machine Learning, and SQL. For now, we will use SQL analytics, but switch to Data Science & Engineering if you need access to your notebooks anytime.