Microsoft Fabric Data Engineer Associate DP-700

Microsoft Fabric Data Engineer Associate (DP-700)

The Microsoft Fabric Data Engineer Associate (DP-700) course at Linux Training Academy is designed for aspiring data engineers, IT professionals, and learners who want to build expertise in modern data engineering using Microsoft Fabric.

This course focuses on designing, building, and managing scalable data solutions using Microsoft Fabric, including data ingestion, transformation, storage, and processing using industry-standard tools and techniques.

Course Overview

This program provides in-depth training on data engineering workflows, enabling learners to work with large datasets, build data pipelines, and manage data solutions efficiently. Students will gain hands-on experience in creating end-to-end data engineering solutions using Microsoft Fabric.

What You Will Learn

  • Introduction to Microsoft Fabric
  • Data Engineering Concepts
  • Data Ingestion & Pipeline Development
  • Data Transformation using Notebooks
  • Working with Lakehouse Architecture
  • Data Warehousing in Fabric
  • Performance Optimization
  • Data Security and Governance

Course Duration

Duration: 45 to 60 Days

Why Choose This Course?

  • Industry-recognized certification (DP-700)
  • High-demand data engineering skills
  • Hands-on practical training
  • Real-world data pipeline projects
  • Guidance from experienced trainers

Career Opportunities

After completing this course, you can explore roles such as:

  • Data Engineer
  • Cloud Data Engineer
  • ETL Developer
  • Big Data Engineer (Entry Level)
  • Data Platform Engineer

Who Can Join?

  • IT professionals and developers
  • Students interested in data engineering
  • Data analysts looking to upgrade skills
  • Anyone with basic knowledge of databases and programming

Microsoft Fabric Data Engineer Associate [DP-700]

Modules

1. Implement and Manage an Analytics Solution (30–35%)

  • Configure Microsoft Fabric workspace settings
  • Configure Spark workspace settings
  • Configure domain workspace settings
  • Configure OneLake workspace settings
  • Configure data workflow workspace settings
  • Configure version control
  • Implement database projects
  • Create and configure deployment pipelines
  • Implement workspace-level access controls
  • Implement item-level access controls
  • Implement row-level, column-level, object-level, and file-level access controls
  • Implement dynamic data masking
  • Apply sensitivity labels to items
  • Endorse items
  • Implement and use workspace logging
  • Choose between a pipeline and a notebook
  • Design and implement schedules and event-based triggers
  • Implement orchestration patterns with notebooks and pipelines

    • 2. Ingest and Transform Data (30–35%)

  • Design and implement full and incremental data loads
  • Prepare data for loading into a dimensional model
  • Design and implement a loading pattern for streaming data
  • Choose an appropriate data store
  • Choose between dataflows, notebooks, and T-SQL for data transformation
  • Create and manage shortcuts to data
  • Implement mirroring
  • Ingest data by using pipelines
  • Transform data by using PySpark, SQL, and KQL
  • Denormalize data
  • Group and aggregate data
  • Handle duplicate, missing, and late-arriving data
  • Choose an appropriate streaming engine
  • Process data by using eventstreams
  • Process data by using Spark structured streaming
  • Process data by using KQL
  • Create windowing functions

    • 3. Monitor and Optimize an Analytics Solution (30–35%)

  • Monitor data ingestion
  • Monitor data transformation
  • Monitor semantic model refresh
  • Configure alerts
  • Identify and resolve pipeline errors
  • Identify and resolve dataflow errors
  • Identify and resolve notebook errors
  • Identify and resolve eventhouse errors
  • Identify and resolve eventstream errors
  • Identify and resolve T-SQL errors
  • Optimize a lakehouse table
  • Optimize a pipeline
  • Optimize a data warehouse
  • Optimize eventstreams and eventhouses
  • Optimize Spark performance
  • Optimize query performance

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