Forum Discussion
How should a beginner start learning Data Engineering in Microsoft Fabric?
A beginner can start Data Engineering in Microsoft Fabric by first understanding basic data concepts like ETL, data lakes, and pipelines.
Microsoft Learn modules and hands-on practice with sample datasets can help build a strong foundation.
raginijaiswar19 Start small, but the key is to "start".
Focus on the key concepts like Lakehouse and Dataflows, then move to Pipelines and Notebooks as you grow.Understand Core Concepts
- Learn what data engineering is: building pipelines, transforming data, and enabling analytics.
- Key terms: Lakehouse, Dataflows, Pipelines, and Notebooks (Fabric uses these heavily).
Start with Microsoft Fabric Basics
- Explore Lakehouse for storing structured and unstructured data.
- Learn Dataflows Gen2 for ingesting and transforming data without heavy coding.
- Understand Pipelines for orchestration and scheduling.
Learn SQL and Python
- SQL is essential for querying data in Fabric Lakehouse and Warehouse.
- Python is useful for advanced transformations in Fabric Notebooks (powered by Spark).
Hands-On Practice in Fabric
- Create a Lakehouse and load sample data.
- Build a simple pipeline: ingest data → transform with Dataflow → store in Lakehouse → visualize in Power BI.
- Try Notebooks for Spark-based processing.
Explore Integration with Power BI
- Learn how Fabric enables end-to-end analytics: from raw data to dashboards.
- Practice connecting Lakehouse/Warehouse to Power BI reports.
Resources
- https://learn.microsoft.com/en-us/fabric/ – official tutorials and guided learning paths.
- Community blogs and videos on Fabric Data Engineering.
- Book recommendation: Designing Data-Intensive Applications for foundational concepts.
Appreciate if you can 'Kudos' and/or 'Accept as Solution' if this answered your query
7 Replies
raginijaiswar19 Start small, but the key is to "start".
Focus on the key concepts like Lakehouse and Dataflows, then move to Pipelines and Notebooks as you grow.Understand Core Concepts
- Learn what data engineering is: building pipelines, transforming data, and enabling analytics.
- Key terms: Lakehouse, Dataflows, Pipelines, and Notebooks (Fabric uses these heavily).
Start with Microsoft Fabric Basics
- Explore Lakehouse for storing structured and unstructured data.
- Learn Dataflows Gen2 for ingesting and transforming data without heavy coding.
- Understand Pipelines for orchestration and scheduling.
Learn SQL and Python
- SQL is essential for querying data in Fabric Lakehouse and Warehouse.
- Python is useful for advanced transformations in Fabric Notebooks (powered by Spark).
Hands-On Practice in Fabric
- Create a Lakehouse and load sample data.
- Build a simple pipeline: ingest data → transform with Dataflow → store in Lakehouse → visualize in Power BI.
- Try Notebooks for Spark-based processing.
Explore Integration with Power BI
- Learn how Fabric enables end-to-end analytics: from raw data to dashboards.
- Practice connecting Lakehouse/Warehouse to Power BI reports.
Resources
- https://learn.microsoft.com/en-us/fabric/ – official tutorials and guided learning paths.
- Community blogs and videos on Fabric Data Engineering.
- Book recommendation: Designing Data-Intensive Applications for foundational concepts.
Appreciate if you can 'Kudos' and/or 'Accept as Solution' if this answered your query
Hello raginijaiswar19
There are plenty of materials online on the Microsoft Learn portal. Please use the following links -
Browse all training - Training | Microsoft Learn
These are a collection of all training modules on Microsoft Fabric. You can pick the modules you're interested in, like Data Engineering, and start from there.
GitHub - microsoft/fabric-samples: Samples and data for Microsoft Fabric Learn content
Here's a list of a lot of samples built on Microsoft Fabric. You can take inspiration from these and build something for yourself on Fabric.
Hope this helps, please appreciate using a Kudos or accept it as a Solution.
Hi @raginijaiswar19 ,
Thanks for reaching out to the Microsoft fabric community forum.To get started with Data Engineering in Microsoft Fabric, beginners can refer to the following official Microsoft documentation and learning resources, which provide both conceptual understanding and hands-on guidance:
- Microsoft Fabric –Data Engineering overview
https://learn.microsoft.com/en-us/fabric/data-engineering/
Provides an introduction to Lakehouses, notebooks, Delta tables, and core data engineering concepts in Fabric. - What is Data Engineering in Microsoft Fabric
https://learn.microsoft.com/en-us/fabric/data-engineering/data-engineering-overview
Explains the end-to-end data engineering experience and supported capabilities. - Microsoft Fabric Data Factory documentation
https://learn.microsoft.com/en-us/fabric/data-factory/
Covers pipelines, data ingestion, orchestration, and ETL/ELT patterns. - Create an end-to-end pipeline in Fabric
https://learn.microsoft.com/en-us/fabric/data-factory/tutorial-end-to-end-pipeline
Step-by-step guide for building a basic pipeline using Fabric Data Factory. - Use Dataflows Gen2 in Microsoft Fabric (Microsoft Learn module)
https://learn.microsoft.com/en-us/training/modules/use-dataflow-gen-2-fabric/
Hands-on learning module focused on data ingestion and transformation. - Get started with Microsoft Fabric (Microsoft Learn learning path)
https://learn.microsoft.com/en-us/training/paths/get-started-fabric/
A structured learning path for beginners new to Fabric.
These resources are a good starting point for building foundational knowledge, followed by hands-on practice using sample datasets in a Lakehouse and simple pipelines.
Best Regards,
Community Support TeamHi raginijaiswar19 ,
I hope the above details help you fix the issue. If you still have any questions or need more help, feel free to reach out. We’re always here to support you .
Best Regards,
Community Support Team
- Microsoft Fabric –Data Engineering overview
What is your question?
It is overwhelming learning these technical subjects, and being told to learn 10 things at the same time doesn't help. Start learning Python and SQL. I find the two to be the foundation.
