SQL Programming and Procedural Logic in Databricks

This course covers both the foundational SQL logic and advanced procedural logic in Databricks. Students will learn to leverage SQL constructs such as CTEs, temporary views, and user-defined functions, along with dynamic SQL and parameterized queries to enhance security and efficiency. The course also delves into procedural logic, where students will explore how to replicate traditional stored procedures using Lakeflow Jobs (Workflows), manage task dependencies, and automate workflows. Additionally, students will discover strategies for migrating legacy stored procedures to Databricks.

Skill Level

Associate

Duration

Prerequisites

Platform Proficiencies

⇾ SQL Workspaces and Warehouses

⇾ Notebooks (SQL, Python, markdown)
⇾ Lakehouse (inc. Delta Lake)
⇾ Unity Catalog
⇾ Workflows

Real-World Experience
⇾ Querying and analyzing data sets using SQL
⇾ Relational databases
⇾ Stored procedures in any SQL-based platform
⇾ Data migration or data modernization projects
⇾ Dynamic or parameterized queries

Broader Domain Knowledge
⇾ Core DWH concepts (ETL, schemas, data modeling)
⇾ SQL standards (ANSI SQL, SQL:2016 or later)
⇾ Data pipelines and orchestration workflows
⇾ BI and reporting concepts
⇾ Data governance and compliance principles

Outline

SQL Constructs in Databricks

- Common Table Expressions (CTEs)

- Temporary Views

- SQL User Defined Functions (UDFs)

- EXECUTE IMMEDIATE

- Parameterized SQL

- SQL Constructs

SQL Scripting in Databricks

- Compound Statements

- Variables

- Conditional Logic

- Control Flow with Looping Constructs

- Error and Exception Handling

- SQL Scripting

Procedural Logic in Databricks

- Introduction to Lakeflow Jobs

- Lakeflow Jobs

- Stored Procedure Migration Strategies

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Upcoming Public Classes

Data Architect

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This course offers a deep dive into designing data models within the Databricks Lakehouse environment, and understanding the data products lifecycle. Participants will learn to align business requirements with data organization and model design leveraging Delta Lake and Unity Catalog for defining data architectures, and techniques for data integration and sharing.

Languages Available: English | 日本語 | Português BR | 한국어

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In this course, you’ll learn how to optimize workloads and physical layout with Spark and Delta Lake and and analyze the Spark UI to assess performance and debug applications. We’ll cover topics like streaming, liquid clustering, data skipping, caching, photons, and more.

Note: This course is part of the 'Advanced Data Engineering with Databricks' course series.

Languages Available: English | 日本語 | Português BR | 한국어

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This course consists of two four-hour modules.

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You will also understand your role in managing data objects and how to manipulate them within the Databricks Data Intelligence Platform, using tools such as Notebooks, the SQL Editor, and Databricks SQL.

Additionally, you will learn about the importance of Unity Catalog in managing data assets and the overall platform. Finally, the course will provide an overview of how Databricks facilitates performance optimization and teach you how to access Query Insights to understand the processes occurring behind the scenes when executing SQL analytics on Databricks.

AI/BI for Data Analysts

In this course, you’ll learn how to use the features Databricks provides for business intelligence needs: AI/BI Dashboards and AI/BI Genie. As a Databricks Data Analyst, you will be tasked with creating AI/BI Dashboards and AI/BI Genie Spaces within the platform, managing the access to these assets by stakeholders and necessary parties, and maintaining these assets as they are edited, refreshed, or decommissioned over the course of their lifespan. This course intends to instruct participants on how to design dashboards for business insights, share those with collaborators and stakeholders, and maintain those assets within the platform. Participants will also learn how to utilize AI/BI Genie Spaces to support self-service analytics through the creation and maintenance of these environments powered by the Databricks Data Intelligence Engine.

Languages Available: English | 日本語 | Português BR | 한국어