Skip to main content
Ace Skills Consulting
Microsoft logoIntermediate

DP-700: Fabric Data Engineer

Engineer reliable batch and real-time data pipelines in Microsoft Fabric and prepare for the DP-700 Fabric Data Engineer exam.

  • 32 hours of live, hands-on sessions
  • Batch and streaming pipelines built in one Fabric workspace
  • PySpark, T-SQL and KQL used where each fits best
  • Orchestration, monitoring and optimisation labs
  • Recommended path for learners who planned DP-203
Aligned to Microsoft exam DP-700
Duration
4 Days | 32 Hrs
Next batch
Batches on request
Delivery
Live Online · Onsite · Corporate
Level
Intermediate
Exam
DP-700
Language
English

Why learn DP-700 with Ace Skills

  • Batch and real time together

    You build both kinds of pipeline in one workspace and learn when each approach is the better fit.

  • Three languages, used wisely

    PySpark, T-SQL and KQL are each applied where they are strongest, which is exactly how exam scenarios are framed.

  • Pipelines that perform

    Delta maintenance, partitioning and Spark settings are tuned against real workloads so jobs finish on time and within capacity.

  • The successor to DP-203

    If you trained for Azure data engineering before, the course maps those skills onto Fabric and fills the gaps.

Course overview

Data engineering on Microsoft Fabric combines familiar tools — Spark, SQL and pipelines — with newer ones such as eventstreams, eventhouses and OneLake shortcuts. The data engineer’s job is to choose the right tool for each workload, build pipelines that recover gracefully and keep costs and performance under control. This four-day course builds those skills on a realistic end-to-end scenario.

Day one covers implementing and managing an analytics solution: workspace settings, Spark configuration, security, lifecycle management with Git and deployment pipelines, and orchestration with pipelines and notebooks. Day two focuses on batch ingestion and transformation — loading data with pipelines and Dataflows Gen2, transforming it with PySpark and T-SQL, and handling incremental loads and slowly changing dimensions.

Day three is dedicated to real-time data: eventstreams, eventhouses, KQL querysets and Spark structured streaming, with windowing and late-arriving data. Day four covers monitoring and optimisation — the Monitoring hub, capacity metrics, error handling, Delta table maintenance and query performance — followed by exam preparation.

Every lab builds on the same scenario, so you finish with a working lakehouse, warehouse and real-time pipeline that you designed and tuned yourself.

The course is aligned to the DP-700 exam, which is the recommended Fabric path for data engineers following the retirement of DP-203. Fabric evolves quickly, so check the current outline on Microsoft Learn before booking.

Key features

  • Batch and streaming pipelines in one scenario
  • Incremental loading and slowly changing dimensions
  • Eventstreams, eventhouses and KQL labs
  • Orchestration with pipelines and notebooks
  • Delta table maintenance and performance tuning
  • Monitoring hub and error-handling patterns
  • 30 days of post-training doubt support

Who should attend

  • Data engineers moving to Microsoft Fabric
  • ETL and data warehouse developers
  • Learners who were preparing for the retired DP-203 exam
  • Analytics engineers who build ingestion pipelines
  • Developers working with streaming data

Prerequisites

  • Working knowledge of SQL
  • Basic Python or PySpark experience
  • Understanding of data warehousing concepts (DP-900 level)

Curriculum

4 Days · 32 Hours · 13 Modules · 11 Labs

  1. Workspace and Spark configuration

    • Workspace settings and capacities
    • Spark pools, environments and libraries
    • Domains and OneLake organisation
    • Choosing between lakehouse, warehouse and eventhouse
    • Lab: Configure a workspace with a custom Spark environment · 40 min

    Security and lifecycle

    • Workspace and item-level access
    • OneLake data access roles
    • Row-level, column-level and dynamic data masking in the warehouse
    • Git integration and deployment pipelines
    • Lab: Secure warehouse data with masking and row-level security · 45 min

    Orchestration

    • Pipelines vs notebooks for orchestration
    • Parameters and dynamic expressions
    • Schedules and event-based triggers
    • Dependencies and retries
    • Lab: Orchestrate notebooks from a parameterised pipeline · 45 min

  2. Loading data

    • Copy activity and connectors
    • Dataflows Gen2
    • Shortcuts and mirroring
    • Full vs incremental loads
    • Lab: Build an incremental load with a watermark · 50 min

    Transforming with PySpark

    • DataFrames and Spark SQL
    • Joins, aggregations and window functions
    • Writing Delta tables
    • Handling schema drift
    • Lab: Transform raw files into silver Delta tables · 50 min

    Transforming with T-SQL

    • Warehouse loading patterns
    • Slowly changing dimensions
    • Stored procedures in pipelines
    • Lab: Implement a type 2 slowly changing dimension · 45 min

  3. Eventstreams

    • Sources and destinations
    • In-stream transformations
    • Routing events to multiple stores
    • Lab: Stream device telemetry with an eventstream · 45 min

    Eventhouses and KQL

    • KQL databases and tables
    • KQL queries and functions
    • Materialised views and update policies
    • Real-Time dashboards
    • Lab: Query and visualise streaming data with KQL · 55 min

    Spark structured streaming

    • Streaming DataFrames
    • Windowing and watermarks
    • Late-arriving data
    • Checkpoints and exactly-once processing
    • Lab: Process a stream into Delta with structured streaming · 45 min

  4. Monitoring

    • Monitoring hub
    • Capacity metrics
    • Alerts with Activator
    • Diagnosing failed runs
    • Lab: Investigate and fix failing pipeline runs · 45 min

    Error handling

    • Retry and failure paths in pipelines
    • Data validation checks
    • Logging patterns in notebooks

    Optimisation

    • OPTIMIZE, VACUUM and V-Order
    • Partitioning and file sizes
    • Spark performance settings
    • Warehouse and KQL query tuning
    • Lab: Tune a slow Spark job and maintain Delta tables · 50 min

    Exam readiness

    • Exam structure and case studies
    • Practice questions with review
Download full syllabus (PDF)

Skills you will gain

  • Microsoft Fabric
  • OneLake
  • Data pipelines
  • Dataflows Gen2
  • PySpark
  • Spark SQL
  • Delta Lake
  • T-SQL
  • Slowly changing dimensions
  • Eventstreams
  • KQL
  • Structured streaming
  • Monitoring
  • Performance tuning

Tools & technologies

  • Microsoft Fabric
  • Fabric notebooks
  • Fabric Data Factory
  • Real-Time Intelligence
  • Visual Studio Code
  • Git

Certification exam

Exam details
Exam nameMicrosoft Certified: Fabric Data Engineer Associate
Exam codeDP-700
Duration100 minutes
Passing score700 / 1000 (scaled)
Voucher includedNo — book directly with the vendor
Question formatMultiple choice, Multiple response, Drag and drop, Case study
DeliveryOnline proctored or Test centre
Validity & renewal1 year (free online renewal assessment)

Skill areas measured

  1. Implement and manage an analytics solution
  2. Ingest and transform data
  3. Monitor and optimize an analytics solution

Exam details are pending verification. Vendors change exams; confirm on the official vendor page before booking. Official exam page (opens in a new tab)

Schedule & Pricing

Live Online

Individual

Price on request

  • 32 hours of live, trainer-led sessions
  • 11 hands-on labs
  • Ace Skills course completion certificate

Onsite

At your office or our centre

Pricing depends on location, group size and dates.

  • Delivered at your premises or an Ace centre
  • Same curriculum and labs as live online
  • Dates planned around your team

Corporate / Team

Private batch for your organisation

A private batch with content adapted to your environment and goals.

  • Customised curriculum
  • Private batch, online or onsite
  • Progress and assessment reports
  • GST invoice and PO-based billing

No public batches scheduled — Talk to an Advisor for a private batch.

Projects & labs you will build

  • Day 2 · 50 min

    Build an incremental load with a watermark

  • Day 3 · 55 min

    Query and visualise streaming data with KQL

Your certificate

Certificate of Completion — Ace Skills Consulting

Awarded on completing the course. It records the course title and training hours, and you can share it on your LinkedIn profile.

The Microsoft certification itself is awarded by Microsoft when you pass exam DP-700.

Frequently asked questions

I studied for DP-203. Is DP-700 the right next step?

Yes. DP-203 has retired, and DP-700 is Microsoft’s data engineering certification for Fabric. Many Spark and SQL skills carry over directly.

How much coding is involved?

Quite a lot: PySpark, T-SQL and KQL appear every day. Starter code is provided, so basic experience is enough.

Do I need my own Fabric capacity?

No. Lab access is arranged for the course, and the details are shared before Day 1.

What happens if Microsoft updates the DP-700 exam?

We map the curriculum to the current published skills outline and revise it when Microsoft changes the exam. Always check the latest outline on Microsoft Learn before booking.

Do you run weekend batches?

Yes. Weekend batches cover the same 32 hours across consecutive Saturdays and Sundays, with timings in IST that also suit learners in the Gulf.

Can you run a private batch for our team?

Yes. We run private online or onsite batches for corporate teams, and we can weight the labs towards your existing data sources and pipelines. Request a corporate quote with your team size and preferred dates.

Related courses

Explore more

All Microsoft Data, 365 & Security courses Microsoft certifications

Microsoft, Azure, Microsoft 365, Power BI and Microsoft Entra are trademarks of the Microsoft group of companies.

Train your team on DP-700

Private batches online or at your office in India and the GCC, with a curriculum adapted to your environment.