Cloud Private

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

1 day
AzureSpark

Learn to build production-ready data analytics solutions with Azure Databricks, Apache Spark, Delta tables, Lakeflow, Unity Catalog, and Microsoft Purview.

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  • Private class for your team
  • Live expert instructor
  • Online or on‑location
  • Customizable agenda
  • Proposal responses same day as request
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Course Overview

Learn how to develop production-ready data analytics solutions with Azure Databricks and Apache Spark. You will ingest, transform, and analyze large-scale datasets using Spark DataFrames, Spark SQL, and PySpark while working with clusters and optimized Delta tables.

The course also covers ETL pipeline design, schema evolution, data quality, and workload orchestration with Lakeflow Jobs and pipelines. You will explore governance and security with Unity Catalog and Microsoft Purview to support secure, well-managed analytics environments.

Course Benefits

  • Ingest, transform, and analyze large-scale data with Azure Databricks and Apache Spark.
  • Use Spark DataFrames, Spark SQL, and PySpark for distributed data processing.
  • Work with Databricks clusters and create and optimize Delta tables.
  • Design ETL pipelines that address schema evolution and data quality.
  • Automate and manage workloads with Lakeflow Jobs and pipelines.
  • Apply governance and security capabilities using Unity Catalog and Microsoft Purview integration.

Delivery Methods

Private Class
Delivered for your team at your site or online.

Microsoft Certified Partner

Webucator is a Microsoft Certified Partner. This class uses official Microsoft courseware and will be delivered by a Microsoft Certified Trainer (MCT).

Microsoft Certified Partner

Course Outline

  1. Azure Databricks and distributed data processing
    1. Databricks workspaces
    2. Clusters
    3. Apache Spark on Azure
  2. Large-scale data ingestion and analysis
    1. Spark DataFrames
    2. Spark SQL
    3. PySpark
  3. Delta table management
    1. Creating Delta tables
    2. Optimizing Delta tables
  4. Data engineering practices
    1. ETL pipeline design
    2. Schema evolution
    3. Data quality enforcement
  5. Workload orchestration
    1. Lakeflow Jobs
    2. Lakeflow pipelines
    3. Workload automation and management
  6. Governance and security
    1. Unity Catalog
    2. Microsoft Purview integration
    3. Production-ready data management

Class Materials

Each student receives a comprehensive set of materials, including course notes and all class examples.

Class Prerequisites

Experience in the following is required for this Azure class:

  • Ability to write basic Python scripts and work with common Python data structures.
  • Ability to write SQL queries that filter, join, and aggregate data.
  • Foundational knowledge of data analytics and data pipeline concepts.
  • Basic knowledge of Azure Databricks and using Apache Spark within it.

Experience in the following would be useful for this Azure class:

  • Familiarity with common data formats such as CSV, JSON, and Parquet.
  • Experience with the Azure portal and services such as Azure Storage.
  • Awareness of batch and streaming processing and of structured and unstructured data.
  • Experience using Jupyter notebooks.

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