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Advanced Techniques with Spark Declarative Pipelines

This course explores Databricks' Lakeflow Spark Declarative Pipelines (SDP) for building production-grade streaming pipelines. You will learn advanced design patterns, robust data quality enforcement, and cross-platform integration essential for real-world lakehouse engineering.


Throughout the course, you will dive into modern data ingestion and processing techniques, mastering tools like Liquid Clustering for layout optimization and the Multiplex Streaming pattern for mixed-schema events. By the end of the modules, you will know how to confidently handle schema evolution, automate Change Data Capture (CDC), and ensure data integrity.


Through lectures and hands-on demos, you will:

• Build multi-flow pipelines to ingest multi-source data into a unified Bronze table.

• Apply Liquid Clustering and Data Quality Expectations across Silver and Gold layers.

• Implement the Multiplex pattern with Iceberg UniForm for cross-platform data access.

• Automate SCD Type 2 history tracking using AUTO CDC INTO.

• Design zero-data-loss quarantine pipelines to audit and manage invalid records.


Note:

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

2. Databricks Academy is transitioning to a notebook-based format for classroom sessions within the Databricks environment, discontinuing the use of slide decks for lectures. You can access the lecture notebooks in the Vocareum lab environment.


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

Skill Level
Professional
Duration
4h
Prerequisites

The content was developed for participants with these skills/knowledge/abilities:  

• Spark Declarative Pipelines — Completion of the "Build Data Pipelines with Lakeflow Spark Declarative Pipelines" course, or familiarity with CREATE OR REFRESH STREAMING TABLE, CONSTRAINTS, and the Pipelines UI

• Delta Lake Fundamentals — Understanding of Delta tables and how Delta manages data files and transaction logs

• Streaming Concepts — Knowledge of micro-batch streaming, checkpointing, and event-time processing in SDP

• SQL Proficiency — Ability to read and write SQL, including SELECT, JOIN, MERGE, CASE WHEN, and common aggregate functions

• Python in Databricks Notebooks — Comfort with reading and running Python code in Databricks notebooks

• Unity Catalog Basics — Understanding of catalogs, schemas, tables, and volumes in Unity Catalog

Outline

• Introduction to Multi Flows, Expectation and Liquid Clustering in SDP

• Demo: Multi Flow SDP with Liquid Clustering and Data Quality

• Introduction to Multiplex Streaming, Delta Sinks and  Iceberg Reads

• Demo: Multiplex Streaming SDP with Delta Sinks and Iceberg Reads

• Change Data Capture (CDC) Review

• Demo: Automating SCD Type 2 with AUTO CDC in Lakeflow Spark Declarative Pipelines

• Advanced Data Quality Checks and Expectations in SDP

• Demo: Advanced Data Quality Checks and Expectation in SDP

• Lab - Building Multi-Source Ecommerce Pipeline with SDP

Upcoming Public Classes

Date
Time
Your Local Time
Language
Price
May 15
11 AM - 03 PM (Asia/Singapore)
-
English
$750.00
May 15
09 AM - 01 PM (America/New_York)
-
English
$750.00
Jun 09
01 PM - 05 PM (Europe/London)
-
English
$750.00
Jun 11
08 AM - 12 PM (Asia/Kolkata)
-
English
$750.00
Jul 15
01 PM - 05 PM (Australia/Sydney)
-
English
$750.00
Jul 15
09 AM - 01 PM (America/New_York)
-
English
$750.00

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Upcoming Public Classes

Apache Spark Developer

Apache Spark™ Programming with Databricks

This course serves as an appropriate entry point to learn Apache Spark Programming with Databricks. 

Below, we describe each of the four, four-hour modules included in this course.

Introduction to Apache Spark

This course offers essential knowledge of Apache Spark, with a focus on its distributed architecture and practical applications for large-scale data processing. Participants will explore programming frameworks, learn the Spark DataFrame API, and develop skills for reading, writing, and transforming data using Python-based Spark workflows. 

Developing Applications with Apache Spark

Master scalable data processing with Apache Spark in this hands-on course. Learn to build efficient ETL pipelines, perform advanced analytics, and optimize distributed data transformations using Spark’s DataFrame API. Explore grouping, aggregation, joins, set operations, and window functions. Work with complex data types like arrays, maps, and structs while applying best practices for performance optimization.

Stream Processing and Analysis with Apache Spark

Learn the essentials of stream processing and analysis with Apache Spark in this course. Gain a solid understanding of stream processing fundamentals and develop applications using the Spark Structured Streaming API. Explore advanced techniques such as stream aggregation and window analysis to process real-time data efficiently. This course equips you with the skills to create scalable and fault-tolerant streaming applications for dynamic data environments.

Monitoring and Optimizing Apache Spark Workloads on Databricks

This course explores the Lakehouse architecture and Medallion design for scalable data workflows, focusing on Unity Catalog for secure data governance, access control, and lineage tracking. The curriculum includes building reliable, ACID-compliant pipelines with Delta Lake. You'll examine Spark optimization techniques, such as partitioning, caching, and query tuning, and learn performance monitoring, troubleshooting, and best practices for efficient data engineering and analytics to address real-world challenges.

Languages Available: English | 日本語 | 한국어

Paid
16h
Lab
instructor-led
Associate

Questions?

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