Microsoft Fabric collapsed a stack of separate services into one platform, and the data engineering role changed with it. Instead of stitching together pipelines, storage, and compute across different Azure products, you now build in a single environment on OneLake. This course teaches you to do that properly.

Across four days, you will learn the methods and practices behind real data engineering work in Fabric: designing data loading patterns, choosing the right data architecture for the workload, and building orchestration that holds up in production. You will ingest data in both batch and streaming form, transform it with SQL, PySpark, or KQL depending on what the job needs, and work across lakehouses, warehouses, and eventhouses. Beyond building, you will cover the parts that separate a working prototype from an enterprise solution: securing workspaces and data, managing version control and deployment pipelines, and monitoring and tuning performance when something slows down or breaks.

The course prepares you for Exam DP-700 and the Microsoft Certified: Fabric Data Engineer Associate credential.

Mode of Training

🏫 Classroom 💻 Live Online 🧪 Blended 👨‍👩‍👧‍👦 Private Group

Upcoming Schedules

Start Date	Time	Duration	Mode	Price
Sep 22, 2026	9:00 am - 5:00 pm	4 Days	online	$2195	GTR
Oct 06, 2026	9:00 am - 5:00 pm	4 Days	online	$2195	GTR
Oct 20, 2026	9:00 am - 5:00 pm	4 Days	online	$2195	GTR

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What you will learn

Design effective data loading patterns and data architectures in Microsoft Fabric
Implement and manage an analytics solution, including workspace configuration and security
Ingest data from a range of sources, in both batch and streaming scenarios
Transform data using SQL, PySpark, or KQL across lakehouses, warehouses, and eventhouses
Build and orchestrate pipelines and dataflows that run reliably in production
Apply version control, deployment pipelines, and lifecycle management to Fabric solutions
Secure and govern data engineering solutions at enterprise scale
Monitor solutions, troubleshoot failures, and optimize performance

Who Should Attend This Course?

Data professionals with hands-on extract, transform, and load experience who need to build and deploy data engineering solutions in Microsoft Fabric for enterprise-scale analytics. That includes:

Data engineers building ingestion and transformation pipelines
ETL and data warehouse developers moving onto the Fabric platform
Analytics engineers and BI developers who own the data layer beneath their reports
Azure data professionals transitioning from Synapse, Data Factory, or Databricks into Fabric

Prerequisites

This is an intermediate course and it assumes real experience, not just familiarity.

You should have:

Practical experience with data extraction, transformation, and loading
Some background in data integration and orchestration
Working ability to manipulate and transform data in at least one of SQL, PySpark, or KQL

That last requirement is the one to take seriously. The course teaches Fabric, not the query languages, so if none of those three are comfortable yet, build that skill first. Foundational data concepts, structured versus semi-structured data, batch versus streaming, and analytical versus transactional workloads, should also be familiar territory before you attend.

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Certification

This course aligns to Exam DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric, leading to the Microsoft Certified: Fabric Data Engineer Associate credential.

The exam measures three functional areas, each carrying roughly a third of the weight: implementing and managing an analytics solution, ingesting and transforming data, and monitoring and optimizing an analytics solution. Because the weighting is nearly even, no domain can be safely skipped in preparation. The exam includes case studies and interactive question formats, and it tests architectural judgment and troubleshooting rather than definition recall.

The certification renews annually through a free online assessment on Microsoft Learn.

📘 DP-700T00: Implement Data Engineering Solutions Using Microsoft Fabric Outline

Ingest Data with Dataflows Gen2 in Microsoft Fabric
Orchestrate processes and data movement with Microsoft Fabric
Use Apache Spark in Microsoft Fabric
Work with real-time data in an Eventhouse in Microsoft Fabric

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
Orchestrate processes and data movement with Microsoft Fabric
Organize a Fabric lakehouse using medallion architecture design

Get started with Real-Time Intelligence in Microsoft Fabric
Use Eventstream in Microsoft Fabric
Work with real-time data in an Eventhouse in Microsoft Fabric
Create Real-Time Dashboards with Microsoft Fabric
Use Activator in Microsoft Fabric

Introduction to end-to-end analytics using Microsoft Fabric
Get started with data warehouses in Microsoft Fabric
Load data into a Microsoft Fabric data warehouse
Query a data warehouse in Microsoft Fabric
Get started with Copilot in Microsoft Fabric for Data Warehouse
Monitor a Microsoft Fabric data warehouse
Secure a Microsoft Fabric data warehouse

Implement continuous integration and continuous delivery (CI/CD) in Microsoft Fabric
Monitor activities in Microsoft Fabric
Secure data access in Microsoft Fabric
Administer a Microsoft Fabric environment

❓ Frequently Asked Questions

They cover the same platform from different angles. DP-600 (Fabric Analytics Engineer Associate) focuses on modeling and preparing data for analysis and reporting, closer to the BI end. DP-700 is the engineering side: ingestion, transformation, orchestration, security, and performance of the data layer itself. If you build the pipelines that feed the semantic models, DP-700 is your certification. Some professionals eventually hold both.

The Azure Data Engineer path built on Synapse and Data Factory has given way to Fabric as Microsoft's strategic analytics platform, and DP-700 is the current data engineering certification. The underlying skills transfer well: pipelines are still pipelines and Spark is still Spark. What's new is Fabric-specific, OneLake, shortcuts, mirroring, Direct Lake, and Fabric's own deployment and CI/CD model, which is exactly where this course concentrates.

No. Microsoft's requirement is competence in at least one. In practice, SQL is the most common entry point, PySpark matters most for lakehouse and notebook work, and KQL comes into play for real-time and eventhouse scenarios. The course exposes you to all three; you'll be more comfortable in whichever you already use, and the others become follow-up study.

Yes. Data engineering is not a spectator subject, and the course is structured around working in Fabric directly, building the ingestion, transformation, and orchestration patterns you'll be expected to deliver on the job.

That depends on your starting point, but plan on additional hands-on practice in a Fabric workspace regardless. A four-day course covers the ground; the exam rewards people who have actually built and broken things in the platform. Candidates coming from a different stack typically need the most extra time on Fabric-specific features rather than on general data engineering concepts.

Still have questions?

Reach out to our learning advisors for personalized guidance on choosing the right course, group training, or enterprise packages.

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