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

Learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run data analytics workloads in a data lakehouse.

Duration: 1.0 day

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Schedule

Virtual ILT | 09 May 2024 - 09 May 2024 India

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Objectives

  • Provision an Azure Databricks workspace.
  • Identify core workloads and personas for Azure Databricks.
  • Describe key concepts of an Azure Databricks solution.
  • Describe key elements of the Apache Spark architecture.
  • Create and configure a Spark cluster.
  • Describe use cases for Spark.
  • Use Spark to process and analyze data stored in files.
  • Use Spark to visualize data.
  • Describe core features and capabilities of Delta Lake.
  • Create and use Delta Lake tables in Azure Databricks.
  • Create Spark catalog tables for Delta Lake data.
  • Use Delta Lake tables for streaming data.
  • Create and configure SQL Warehouses in Azure Databricks.
  • Create databases and tables.
  • Create queries and dashboards.
  • Describe how Azure Databricks notebooks can be run in a pipeline.
  • Create an Azure Data Factory linked service for Azure Databricks.
  • Use a Notebook activity in a pipeline.
  • Pass parameters to a notebook.

Content

1. Explore Azure Databricks

Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.

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2. Use Apache Spark in Azure Databricks

Azure Databricks is built on Apache Spark and enables data engineers and analysts to run Spark jobs to transform, analyze and visualize data at scale.

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3. Use Delta Lake in Azure Databricks

Delta Lake is an open source relational storage area for Spark that you can use to implement a data lakehouse architecture in Azure Databricks.

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4. Use SQL Warehouses in Azure Databricks

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

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5. Run Azure Databricks Notebooks with Azure Data Factory

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

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Audience

Data engineers, data analysts, and data scientists who want to use Azure Databricks for data analytics and machine learning.

Prerequisites

Basic knowledge of Azure services, SQL, and Python. Experience with data engineering and Apache Spark is recommended.

Certification

product-certification

Course Benefits

product-benefits
  • Career growth
  • Broad Career opportunities
  • Worldwide recognition from leaders
  • Up-to Date technical skills
  • Popular Certification Badges

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