Query and Explore Data in Databricks SQL
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 1h 15m | 208 MB
Instructor: Lucian Lazar
What you'll learn
Data teams waste hours hunting through unfamiliar tables, writing one-off queries that nobody can find later, and rebuilding the same filtering logic repeatedly because there's no systematic approach to data exploration. Without structured query skills and organized workflows, analysts spend more time searching and recreating work than actually discovering insights.
In this course, Query and Explore Data in Databricks SQL, you'll gain the ability to efficiently query, explore, and organize data within the Databricks lakehouse environment for repeatable analytical workflows.
First, you'll explore how Databricks SQL supports querying and exploration of structured data, understanding how ANSI-compliant queries execute against Delta tables, how query performance and caching differ from traditional databases, and how to use the query editor to iteratively refine your exploration while organizing queries in collaborative workspaces.
Next, you'll discover how to write and refine SQL queries to prepare data for analysis, retrieving specific columns with SELECT statements, filtering with WHERE conditions, performing aggregations with GROUP BY and HAVING, combining tables with JOIN operations, and transforming data using built-in functions for text, numeric, and date manipulation.
Finally, you'll learn how to structure and organize data for repeatable analytical workflows, browsing catalogs and schemas to locate datasets, creating temporary and permanent views to encapsulate query logic, simplifying complex queries with CTEs and subqueries, and managing query history to track your exploratory progress.
When you're finished with this course, you'll have the knowledge of Databricks SQL needed to confidently explore lakehouse data, write refined queries for analysis, and build organized workflows that you and your team can rely on for ongoing analytical work.
Homepage
RapidGator
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