Implementing a Data Analytics Solution with Azure Databricks (DP-3011)
Course 8685
1 DAY COURSE

This one-day, hands-on course introduces learners to building scalable data analytics solutions using Azure Databricks and Apache Spark. Participants will explore how to ingest, transform, and analyze large datasets using Spark DataFrames, Spark SQL, and PySpark. The course emphasizes practical skills in managing distributed data processing, optimizing Delta Lake tables, and orchestrating workloads with Lakeflow Jobs and pipelines.

Learners will also gain experience in data governance and security using Unity Catalog and Microsoft Purview, ensuring their solutions are secure and production-ready. Through guided exercises and collaborative notebooks, participants will build confidence in designing ETL pipelines, enforcing data quality, and automating analytics workflows.

Implementing a Data Analytics Solution with Azure Databricks (DP-3011) Benefits

• In this course, you will:

  • Navigate the Azure Databricks workspace and identify key workloads.
  • Ingest and explore data using Spark DataFrames and collaborative notebooks.
  • Transform and analyze data at scale using Apache Spark in Databricks.
  • Manage data consistency and versioning with Delta Lake features.
  • Build and deploy Lakeflow pipelines and jobs for automated data processing.
  • Apply governance and security practices using Unity Catalog and Purview.

• Training Prerequisites

  • Familiarity with Python and SQL (basic scripting and query writing).
  • Understanding of common data formats (CSV, JSON, Parquet).
  • Experience with Azure portal and services such as Azure Storage.
  • Awareness of data concepts like batch vs. streaming and structured vs. unstructured data.

• Who Should Attend

  This course is ideal for:

  • Data Analysts looking to scale their analytics workflows using Spark and Databricks.
  • Data Engineers seeking to build and automate ETL pipelines in Azure.
  • Technical Professionals working with large datasets and cloud-based analytics platforms.

  Participants will gain practical skills to manage data pipelines, perform advanced analysis, and ensure secure data operations in Azure Databricks.

Implementing a Data Analytics Solution with Azure Databricks (DP-3011) Training Outline

Module 1: Explore Azure Databricks

• Understand the purpose and architecture of Azure Databricks.
• Identify common workloads and key concepts in the Databricks environment.
• Explore data governance features using Unity Catalog and Microsoft Purview.
• Navigate the workspace and complete a guided hands-on exercise.

Module 2: Perform Data Analysis with Azure Databricks

• Ingest data from sources such as Azure Data Lake and Azure SQL Database.
• Use collaborative notebooks for exploratory data analysis (EDA).
• Visualize and manipulate data using DataFrame APIs.
• Uncover patterns and anomalies through guided analysis exercises.

Module 3: Use Apache Spark in Azure Databricks

• Create and manage Spark clusters within Databricks.
• Run Spark jobs to transform and analyze large datasets.
• Visualize data using built-in tools and notebooks.
• Apply Spark to real-world data files in a hands-on lab.

Module 4: Manage Data with Delta Lake

• Create and optimize Delta tables for scalable data storage.
• Enforce schema consistency and handle schema evolution.
• Use time travel and versioning to manage historical data.
• Ensure data integrity through ACID transactions and validation.

Module 5: Build Lakeflow Declarative Pipelines

• Design scalable data pipelines using Lakeflow’s declarative approach.
• Integrate real-time and batch data ingestion workflows.
• Implement advanced Delta Lake features in pipeline design.
• Complete a hands-on exercise to build a Lakeflow pipeline.

Module 6: Deploy Workloads with Lakeflow Jobs

• Understand the components and benefits of Lakeflow Jobs.
• Automate complex data processing and analytics tasks.
• Deploy and monitor workloads using Databricks orchestration tools.
• Create and run a Lakeflow Job in a guided exercise.