Implementing a data analytics solution with Azure Databricks

Unlock the potential of cloud-based data engineering with our course on Implementing a Data Analytics Solution with Azure Databricks, tailored for Azure data engineers looking to leverage Apache Spark for large-scale data workloads.

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Essential Skills Gained

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Harness the capabilities of Azure Databricks for scalable data analytics.

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Integrate Apache Spark for efficient data processing in the cloud.

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Implement Delta Lake for a robust data lakehouse architecture.

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Automate data pipelines using Azure Data Factory.

Format

  • Instructor-led
  • 1 days with lectures and hands-on labs.

Audience

  • Azure Data Engineers
  • Cloud Solution Architects
  • Data Analysts
  • IT Professionals specializing in Microsoft Azure

Description

This course is designed to be delivered in one full day. Learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run large data engineering workloads in the cloud.

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Upcoming Course Dates

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Course Outline

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Explore Azure Databricks

  1. Get started with Azure Databricks

  2. Identify Azure Databricks workloads

  3. Understand key concepts

  4. Exercise: Explore Azure Databricks

Use Apache Spark in Azure Databricks

  1. Get to know Spark

  2. Create a Spark cluster

  3. Use Spark in notebooks

  4. Use Spark to work with data files

  5. Visualize data

  6. Exercise: Use Spark in Azure Databricks

Use Delta Lake in Azure Databricks

  1. Get Started with Delta Lake

  2. Create Delta Lake tables

  3. Create and query catalog tables

  4. Use Delta Lake for streaming data

  5. Exercise: Use Delta Lake in Azure Databricks

Use SQL Warehouses in Azure Databricks

  1. Get started with SQL Warehouses

  2. Create databases and tables

  3. Create queries and dashboards

  4. Exercise: Use a SQL Warehouse in Azure Databricks

Run Azure Databricks Notebooks with Azure Data Factory

  1. Understand Azure Databricks notebooks and pipelines

  2. Create a linked service for Azure Databricks

  3. Use a Notebook activity in a pipeline

  4. Use parameters in a notebook

  5. Exercise: Run an Azure Databricks Notebook with Azure Data Factory

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