Implementing a Data Analytics Solution with Azure Databricks (DP-3011)
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Implementing a Data Analytics Solution with Azure Databricks (DP-3011)
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Get started with data engineering on Azure Databricks
Description
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.
Subjects
Module 1: Explore Azure DatabricksAzure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.
After completing this module, you will be able to:
- Provision an Azure Databricks workspace.
- Identify core workloads and personas for Azure Databricks.
- Describe key concepts of an Azure Databricks solution.
Azure Databricks is built on Apache Spark and enables data eng…
Veelgestelde vragen
Er zijn nog geen veelgestelde vragen over dit product. Als je een vraag hebt, neem dan contact op met onze klantenservice.
Meer weten over de onderwerpen die aan bod komen en de vereiste voorkennis? Neem vrijblijvend contact met ons op.
Get started with data engineering on Azure Databricks
Description
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.
Subjects
Module 1: Explore Azure DatabricksAzure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.
After completing this module, you will be able to:
- Provision an Azure Databricks workspace.
- Identify core workloads and personas for Azure Databricks.
- Describe key concepts of an Azure Databricks solution.
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.
After completing this module, you will be able to:
- 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.
Delta Lake is an open source relational storage area for Spark that you can use to implement a data lakehouse architecture in Azure Databricks.
After completing this module, you will be able to:
- 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.
Azure Databricks provides SQL Warehouses that enable data analysts to work with data using familiar relational SQL queries.
After completing this module, you will be able to:
- Create and configure SQL Warehouses in Azure Databricks.
- Create databases and tables.
- Create queries and dashboards.
Using pipelines in Azure Data Factory to run notebooks in Azure Databricks enables you to automate data engineering processes at cloud scale.
After completing this module, you will be able to:
- 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.
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