DP-600T00 - Microsoft Fabric Analytics Engineer

This course covers methods and practices for implementing and managing enterprise-scale data analytics solutions using Microsoft Fabric. Students will build on existing analytics experience and will learn how to use Microsoft Fabric components, including lakehouses, data warehouses, notebooks, dataflows, data pipelines, and semantic models, to create and deploy analytics assets. This course is best suited for those who have the PL-300 certification or similar expertise in using Power BI for data transformation, modeling, visualization, and sharing. Also, learners should have prior experience in building and deploying data analytics solutions at the enterprise level.

Duration: 4.0 days

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Schedule

Virtual ILT | 20 May 2024 - 23 May 2024 India
Virtual ILT | 27 May 2024 - 30 May 2024 Thailand

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Objectives

  • Describe Dataflow capabilities in Microsoft Fabric
  • Create Dataflow solutions to ingest and transform data
  • Include a Dataflow in a pipeline
  • Ingest external data to Fabric lakehouses using Spark
  • Configure external source authentication and optimization
  • Load data into lakehouse as files or as Delta tables
  • Describe pipeline capabilities in Microsoft Fabric
  • Use the Copy Data activity in a pipeline
  • Create pipelines based on predefined templates
  • Run and monitor pipelines
  • Describe core features and capabilities of lakehouses in Microsoft Fabric
  • Create a lakehouse
  • Ingest data into files and tables in a lakehouse
  • Query lakehouse tables with SQL
  • Describe the principles of using the medallion architecture in data management.
  • Apply the medallion architecture framework within the Microsoft Fabric environment.
  • Analyze data stored in the lakehouse using DirectLake in Power BI.
  • Describe best practices for ensuring the security and governance of data stored in the medallion architecture.
  • Configure Spark in a Microsoft Fabric workspace
  • Identify suitable scenarios for Spark notebooks and Spark jobs
  • Use Spark dataframes to analyze and transform data
  • Use Spark SQL to query data in tables and views
  • Visualize data in a Spark notebook
  • Understand Delta Lake and delta tables in Microsoft Fabric
  • Create and manage delta tables using Spark
  • Use Spark to query and transform data in delta tables
  • Use delta tables with Spark structured streaming
  • Describe data warehouses in Fabric
  • Understand a data warehouse vs a data Lakehouse
  • Work with data warehouses in Fabric
  • Create and manage fact tables and dimensions within a data warehouse
  • Learn different strategies to load data into a data warehouse in Microsoft Fabric.
  • Learn how to build a data pipeline to load a warehouse in Microsoft Fabric.
  • Learn how to load data in a warehouse using T-SQL.
  • Use SQL query editor to query a data warehouse.
  • Explore how visual query editor works.
  • Learn how to connect and query a data warehouse using SQL Server Management Studio.
  • Monitor capacity unit usage with the Microsoft Fabric Capacity Metrics app.
  • Monitor current activity in the data warehouse with dynamic management views.
  • Monitor querying trends with query insights views.
  • Describe the importance of building scalable data models
  • Implement Power BI data modeling best practices
  • Use the Power BI large dataset storage format
  • Understand how model relationship work.
  • Set up relationships.
  • Use DAX relationship functions.
  • Understand relationship evaluation.
  • Optimize queries using performance analyzer.
  • Troubleshoot DAX performance using DAX Studio.
  • Optimize a data model using Tabular Editor.
  • Restrict access to Power BI model data with RLS.
  • Restrict access to Power BI model objects with OLS.
  • Apply good development practices to enforce Power BI model security.

Content

1. Ingest Data with Dataflows Gen2 in Microsoft Fabric

Data ingestion is crucial in analytics. Microsoft Fabric's Data Factory offers Dataflows for visually creating multi-step data ingestion and transformation using Power Query Online.

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2. Ingest data with Spark and Microsoft Fabric notebooks

Discover how to use Apache Spark and Python for data ingestion into a Microsoft Fabric lakehouse. Fabric notebooks provide a scalable and systematic solution.

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3. Use Data Factory pipelines in Microsoft Fabric

Microsoft Fabric includes Data Factory capabilities, including the ability to create pipelines that orchestrate data ingestion and transformation tasks.

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4. Get started with lakehouses in Microsoft Fabric

Lakehouses merge data lake storage flexibility with data warehouse analytics. Microsoft Fabric offers a lakehouse solution for comprehensive analytics on a single SaaS platform.

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5. Organize a Fabric lakehouse using medallion architecture design

Explore the potential of the medallion architecture design in Microsoft Fabric. Organize and transform your data across Bronze, Silver, and Gold layers of a lakehouse for optimized analytics.

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6. Use Apache Spark in Microsoft Fabric

Apache Spark is a core technology for large-scale data analytics. Microsoft Fabric provides support for Spark clusters, enabling you to analyze and process data in a Lakehouse at scale.

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7. Work with Delta Lake tables in Microsoft Fabric

Tables in a Microsoft Fabric lakehouse are based on the Delta Lake storage format commonly used in Apache Spark. By using the enhanced capabilities of delta tables, you can create advanced analytics solutions.

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8. Get started with data warehouses in Microsoft Fabric

Data warehouses are analytical stores built on a relational schema to support SQL queries. Microsoft Fabric enables you to create a relational data warehouse in your workspace and integrate it easily with other elements of your end-to-end analytics solution.

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9. Load data into a Microsoft Fabric data warehouse

Data warehouse in Microsoft Fabric is a comprehensive platform for data and analytics, featuring advanced query processing and full transactional T-SQL capabilities for easy data management and analysis.

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10. Query a data warehouse in Microsoft Fabric

Data warehouse in Microsoft Fabric is a comprehensive platform for data and analytics, featuring advanced query processing and full transactional T-SQL capabilities for easy data management and analysis.

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11. Monitor a Microsoft Fabric data warehouse

A data warehouse is a vital component of an enterprise analytics solution. It's important to learn how to monitor a data warehouse so you can better understand the activity that occurs in it.

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12. Understand scalability in Power BI

Scalable data models enable enterprise-scale analytics in Power BI. Implement data modeling best practices, use large dataset storage format, and practice building a star schema to design analytics solutions that can scale.

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13. Create Power BI model relationships

Power BI model relationships form the basis of a tabular model. Define Power BI model relationships, set up relationships, recognize DAX relationship functions, and describe relationship evaluation.

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14. Use tools to optimize Power BI performance

Use tools to develop, manage, and optimize Power BI data model and DAX query performance.

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15. Enforce Power BI model security

Enforce model security in Power BI using row-level security and object-level security.

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Audience

The primary audience for this course is data professionals with experience in data modeling, extraction, and analytics. DP-600 is designed for professionals who want to use Microsoft Fabric to create and deploy enterprise-scale data analytics solutions.

Prerequisites

This course is best suited for those who have the Microsoft Certified: Power BI Data Analyst Associate certification or similar expertise in using Power BI for data transformation, modeling, visualization, and sharing. Also, learners should have prior experience in building and deploying enterprise data analytics solutions.

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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