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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
4h
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

Upcoming Public Classes

Date
Time
Language
Price
Jul 17
09 AM - 01 PM (America/New_York)
English
$750.00

Public Class Registration

If your company has purchased success credits or has a learning subscription, please fill out the Training Request form. Otherwise, you can register below.

Private Class Request

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Registration options

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Self-paced and weekly instructor-led sessions for every style of learner to optimize course completion and knowledge retention. Go to Subscriptions Catalog tab to purchase

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Scale

Skills@Scale

Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details

Upcoming Public Classes

Data Engineer

Data Ingestion with Lakeflow Connect

This course provides a comprehensive introduction to Lakeflow Connect as a scalable and simplified solution for ingesting data into Databricks from a variety of data sources. You will begin by exploring the different types of connectors within Lakeflow Connect (Standard and Managed), learn about various ingestion techniques, including batch, incremental batch, and streaming, and then review the key benefits of Delta tables and the Medallion architecture.

From there, you will gain practical skills to efficiently ingest data from cloud object storage using Lakeflow Connect Standard Connectors with methods such as CREATE TABLE AS (CTAS), COPY INTO, and Auto Loader, along with the benefits and considerations of each approach. You will then learn how to append metadata columns to your bronze level tables during ingestion into the Databricks data intelligence platform. This is followed by working with the rescued data column, which handles records that don’t match the schema of your bronze table, including strategies for managing this rescued data.

The course also introduces techniques for ingesting and flattening semi-structured JSON data, as well as enterprise-grade data ingestion using Lakeflow Connect Managed Connectors.

Finally, learners will explore alternative ingestion strategies, including MERGE INTO operations and leveraging the Databricks Marketplace, equipping you with foundational knowledge to support modern data engineering ingestion.

Free
2h
Associate

Questions?

If you have any questions, please refer to our Frequently Asked Questions page.