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  • DP - 601T00 : Implement a Lakehouse with Microsoft Fabric

Bridge the gap between data lakes and data warehouses! In Implement a Lakehouse with Microsoft Fabric (DP-601) course, delve into Microsoft Fabric and learn how to create a unified data platform. Explore data ingestion, transformation, and analytics. Whether it’s batch processing or real-time insights, become proficient in building robust Lakehouse architecture.

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DP - 601T00 : Implement a Lakehouse with Microsoft Fabric Training

Curriculum

Master DP-601T00: Implement a Lakehouse with Microsoft Fabric Training. Learn to design, build & optimize lakehouse solutions for real-world success.

Ratings

( 4.5 Ratings )

Live Online Classes starting on 01 January, 1970

DP - 601T00 : Implement a Lakehouse with Microsoft Fabric

The DP-601T00: Implement a Lakehouse with Microsoft Fabric Course teaches participants how to build, manage, and query Microsoft Fabric lakehouses. You will learn to ingest, analyze, and transform data using Apache Spark and Delta Lake. This course also covers data pipelines and Dataflows Gen2 for efficient data management. Enroll in the DP-601 training to master end-to-end analytics with Microsoft Fabric and gain Microsoft Fabric certification.

 

Course Objectives:

  • Introduction to end-to-end analytics using Microsoft Fabric

  • Get started with lakehouses in Microsoft Fabric

  • Use Apache Spark in Microsoft Fabric

  • Work with Delta Lake tables in Microsoft Fabric

  • Ingest Data with Dataflows Gen2 in Microsoft Fabric

  • Use Data Factory pipelines in Microsoft Fabric

 

Target Audience:

  • Data Engineers

  • Data Scientists

  • Data Analysts

  • Solutions Architects

  • IT Professionals with a focus on data solutions

  • Database Administrators

  • Software Developers who specialize in data-oriented applications

  • Cloud Technology Specialists

  • Business Intelligence Professionals

  • Enterprise Architects

  • Technical Team Leads managing data projects

  • Professionals seeking to understand Microsoft's data ecosystem

  • Technical Consultants involved in data platform services

  • Data Platform Engineers

 

Course Outline:

Module: Introduction to End-to-End Analytics using Microsoft Fabric

1. Introduction to End-to-End Analytics

  • Describe end-to-end analytics in Microsoft Fabric:
    Understand how Microsoft Fabric provides a unified platform for ingesting, storing, analyzing, and visualizing data using integrated services.

2. Get Started with Lakehouses in Microsoft Fabric
Key Concepts:

  • Core Features & Capabilities:

    • Combines data warehouse and data lake features

    • Supports open formats like Parquet and Delta

    • Built-in support for Spark and SQL queries

  • Create a Lakehouse:

    • Use the Fabric UI to define a lakehouse with structured and unstructured data zones

  • Ingest Data:

    • Load data into files and tables using built-in tools, pipelines, or notebooks

  • Query with SQL:

    • Run T-SQL or Spark SQL queries on lakehouse tables

Lab: Create a Microsoft Fabric lakehouse

3. Use Apache Spark in Microsoft Fabric
Key Concepts:

  • Configure Spark in a Fabric workspace

  • Identify scenarios best suited for Spark notebooks and Spark jobs

  • Use DataFrames to manipulate and transform data

  • Query data using Spark SQL

  • Visualize data within notebooks using display libraries

Lab: Analyze data with Apache Spark

4. Work with Delta Lake Tables
Key Concepts:

  • Understand what Delta Lake and delta tables are in Fabric

  • Create and manage delta tables using Spark

  • Optimize delta tables using compaction and Z-ordering

  • Transform and query delta tables using Spark

  • Use Spark structured streaming to process data in real time

Lab: Use delta tables in Apache Spark

5. Ingest Data with Dataflows Gen2
Key Concepts:

  • Dataflow Capabilities:

    • No-code/low-code ETL tool

    • Built for data transformation and ingestion

  • Create Dataflow Solutions:

    • Build reusable transformation pipelines with Power Query-like interface

  • Include in Pipelines:

    • Integrate Dataflows into larger orchestrated processes

(4.5 Ratings)

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