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Implement a Data Engineering Solution with Azure Databricks

Harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run large data engineering workloads in the cloud.

Top-Rated 2512+ Learners
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    What You Will Learn

    • Build scalable data engineering solutions using Azure Databricks and Apache Spark.
    • Perform real-time and incremental data processing with Spark Structured Streaming.
    • Design streaming architectures using Delta Live Tables.
    • Optimize data pipelines for performance and cost efficiency.
    • Apply change data capture (CDC) and query tuning techniques.
    • Implement CI/CD workflows with version control, testing, and rollback strategies.
    • Automate and orchestrate workflows using Azure Databricks Jobs.
    • Integrate Databricks with Azure Data Factory for end-to-end pipeline automation.
    • Manage data governance, security, and privacy using Unity Catalog.
    • Use SQL Warehouses to create queries, dashboards, and analytics-ready datasets.
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    Prerequisites

    • No prior technical experience is required
    • The program is designed to be suitable for beginners as well.

    The KnowledgeHut Advantage

    Superior Outcomes

    Focus on skill-based outcomes with advanced insights from our learning platform.

    Immersive Learning

    Go beyond just videos and learn hands-on with guided exercises, assignments and more.

    World-Class Instructors

    Course instructors and designers from top businesses including Google, Amazon, Twitter and IBM.

    Real-World Learning

    Get an intimate, insider look at leading companies in the field through real-world case studies.

    Industry-Vetted Curriculum

    Curriculum primed for industry relevance and developed with guidance from industry advisory boards.

    Curriculum

    1.

    Learning Objective:

    Explore different features and tools to help you understand and work with incremental processing with spark structured streaming.

    Topics
    • Introduction
    • Set up real-time data sources for incremental processing
    • Optimize Delta Lake for incremental processing in Azure Databricks
    • Handle late data and out-of-order events in incremental processing
    • Monitoring and performance tuning strategies for incremental processing in Azure Databricks
    • Exercise - Real-time ingestion and processing with Delta Live Tables with Azure Databricks
    • Module assessment
    • Summary
    2.

    Learning Objective:

    Explore different features and tools to help you develop architecture patterns with Azure Databricks Delta Live Tables.

    Topics
    • Introduction
    • Event driven architectures with Delta Live tables
    • Ingest data with structured streaming
    • Maintain data consistency and reliability with structured streaming
    • Scale streaming workloads with Delta Live tables
    • Exercise - end-to-end streaming pipeline with Delta Live tables
    • Module assessment
    • Summary
    3.

    Learning Objective:

    Optimize performance with Spark and Delta Live Tables

    Topics
    • Introduction
    • Optimize performance with Spark and Delta Live Tables
    • Perform cost-based optimization and query tuning
    • Use change data capture (CDC)
    • Use enhanced autoscaling
    • Implement observability and data quality metrics
    • Exercise - optimize data pipelines for better performance in Azure Databricks
    • Module assessment
    • Summary
    4.

    Learning Objective:

    Implement CI/CD workflows in Azure Databricks to automate the integration and delivery of code changes.

    Topics
    • Introduction
    • Implement version control and Git integration
    • Perform unit testing and integration testing
    • Manage and configure your environment
    • Implement rollback and roll-forward strategies
    • Exercise - Implement CI/CD workflows
    • Module assessment
    • Summary
    5.

    Learning Objective:

    Orchestrate and schedule data workflows with Azure Databricks Jobs. Define and monitor complex pipelines, integrate with tools like Azure Data Factory and Azure DevOps, and reduce manual intervention, leading to improved efficiency, faster insights, and adaptability to business needs.

    Topics
    • Introduction
    • Implement job scheduling and automation
    • Optimize workflows with parameters
    • Handle dependency management
    • Implement error handling and retry mechanisms
    • Explore best practices and guidelines
    • Exercise - Automate data ingestion and processing
    • Module assessment
    • Summary
    6.

    Learning Objective:

    Explore different features and approaches to help you secure and manage your data within Azure Databricks using tools, such as Unity Catalog.

    Topics
    • Introduction
    • Implement data encryption techniques in Azure Databricks
    • Manage access controls in Azure Databricks
    • Implement data masking and anonymization in Azure Databricks
    • Use compliance frameworks and secure data sharing in Azure Databricks
    • Use data lineage and metadata management
    • Implement governance automation in Azure Databricks
    • Exercise - Practice the implementation of Unity Catalog
    • Module assessment
    • Summary
    7.

    Learning Objective:

    Azure Databricks provides SQL Warehouses that enable data analysts to work with data using familiar relational SQL queries.

    Topics
    • Introduction
    • Get started with SQL Warehouses
    • Create databases and tables
    • Create queries and dashboards
    • Exercise - Use a SQL Warehouse in Azure Databricks
    • Module assessment
    • Summary
    8.

    Learning Objective:

    Using pipelines in Azure Data Factory to run notebooks in Azure Databricks enables you to automate data engineering processes at cloud scale.

    Topics
    1. Introduction
    2. Understand Azure Databricks notebooks and pipelines
    3. Create a linked service for Azure Databricks
    4. Use a Notebook activity in a pipeline
    5. Use parameters in a notebook
    6. Exercise - Run an Azure Databricks Notebook with Azure Data Factory
    7. Module assessment
    8. Summary

    Frequently Asked Questions

    Yes, you will experience KnowledgeHut's immersive learning in an on-demand format. This will include e-learning material to help you:

    • LEARN with quizzes, case studies, and more
    • ASSESS your skills progression with diagnostic, module-level, and final assessments
    • GAIN INSIGHTS with real-time reports and analytics on how you're progressing, your learning challenges, and suggestions of sections to revisit to build competency in the required areas.

    Yes, our online course is designed to give you flexibility to skill up as per your convenience. The course is delivered in a Self-Paced mode so that you can balance your work and learning as per your schedule.

    Yes! Upon passing this online course, you will receive a signed certificate of completion from KnowledgeHut. Thousands of KnowledgeHut alumni use their course certificate to demonstrate skills to employers and their networks.

    KnowledgeHut’s online courses is well-regarded by industry experts, who contribute to our curriculum and use our tech programs to train their own teams.

    You can cancel your enrolment and receive refunds in line with our Cancellations and Refunds policy found at https://www.knowledgehut.com/refund-policy. 

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