Training Overview
Disclaimer: This course is independently developed and not affiliated with Microsoft. It covers concepts and skills that closely align with the objectives of Microsoft’s DP-DP-3011: Implement a Data Analytics Solution with Azure Databricks training, making it a strong preparatory or complementary learning experience.
Implementing Scalable Data Analytics Solutions with Azure Databricks
This One-Day intermediate-level custom training This intermediate-level training empowers data professionals to design and deploy scalable analytics solutions using Apache Spark on Azure Databricks. Participants will learn to ingest, transform, and analyze large datasets with Spark DataFrames, SQL, and PySpark; manage Delta Lake tables with schema enforcement and time travel; orchestrate ETL workflows using Lakeflow Jobs and declarative pipelines; and apply governance best practices with Unity Catalog and Purview. By course end, learners will be equipped to operate confidently in distributed, production-ready environments. Prior experience with Python, SQL, Azure Storage, and core data concepts is recommended.
Before enrolling in this custom training, participants should already be comfortable with the fundamentals of Python and SQL. This includes being able to write simple Python scripts and work with common data structures, as well as writing SQL queries to filter, join, and aggregate data. A basic understanding of common file formats such as CSV, JSON, or Parquet will also help when working with datasets.
Module Breakdown:
Explore Azure Databricks: Introduces the Databricks workspace and its Spark-based architecture. Covers cluster management and workspace navigation
Perform Data Analysis with Azure Databricks: Teaches data ingestion from sources like Azure Data Lake and SQL DB. Includes exploratory data analysis using notebooks and DataFrame APIs
Use Apache Spark in Azure Databricks: Focuses on running Spark jobs for scalable data transformation, analysis, and visualization
Manage Data with Delta Lake: Covers Delta Lake features like ACID transactions, schema enforcement, and time travel for reliable data management
Build Lakeflow Declarative Pipelines: Enables real-time, scalable data processing using Delta Lake and Lakeflow’s declarative orchestration tools
Deploy Workloads with Lakeflow Jobs: Guides users through automating complex pipelines and ML workflows using Lakeflow Jobs for production deployment