DP-601 Implementing a Lakehouse with Microsoft Fabric
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Duration
1 day
Location
Course Language English
Training Solutions Online Live
Analytical requirements are constantly growing, as is the complexity of the underlying data landscapes. Modern platform concepts combine processing, analysis, and visualization in a unified environment.
Key topics
- Implementation of a lakehouse structure.
- End-to-end data processes with Microsoft Fabric.
- Uniform storage and use of data.
- Efficient processing of large amounts of data.
- Analysis and reporting on a shared basis.
Prerequisite
Practical knowledge of BI, data analysis, or cloud-related technologies.
Target group
Specialists from data & analytics, BI, data engineering, and data-oriented IT roles.
A holistic view of modern data architectures supports sustainable analysis concepts and data-based decisions.
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Course content
End-to-end analytics
- End-to-end analytics with Microsoft Fabric
- Data teams and Microsoft Fabric
- Enabling and using Microsoft Fabric
Lake house
- Discover Microsoft Fabric Lakehouse
- Working with Lakehouse in Microsoft Fabric
- Explore and transform data in Lakehouse
Apache Spark
- Preparing for Apache Spark
- Running Spark code
- Working with Spark dataframes
- Working with Spark SQL
- Visualizing data in the Spark notebook
Delta Lake tables
- Understanding Delta Lake
- Creating delta tables
- Working with Delta Tables in Spark
- Using delta tables for streaming data
Data Flows Gen2
- Understanding Dataflows Gen2 in Microsoft Fabric
- Explore Dataflows Gen2 in Microsoft Fabric
- Integrating Dataflows Gen2 and Pipelines
Data Factory pipelines
- Understanding pipelines
- Use the "Copy Data" activity
- Apply pipeline templates
- Execute and monitor pipelines
What are the specific benefits of a Lakehouse with Microsoft Fabric?
- A unified data platform reduces complexity, speeds up analysis, and provides a direct basis for decision-making from raw data.
Who is this topic particularly relevant for?
- Relevant for roles in data engineering, analytics, and BI where scalable data architectures are required.
What problems does this solve?
- Data silos, slow pipelines, and inconsistent data are eliminated through an integrated architecture.
What knowledge is required?
- A solid foundation in data processing, SQL, and cloud concepts makes it easier to get started right away and speeds up implementation.
How practical is the content?
- Focus on real-world scenarios: data integration, transformation, and analysis using concrete use cases from everyday work.
Why is Microsoft Fabric so popular right now?
- Growing data volumes and real-time requirements are increasing the need for modern platforms with centralized control.
What career opportunities does this offer?
- In-demand skills in data engineering and analytics increase market value and open up new project opportunities.
How does this knowledge support specific projects?
- Structured data flows and optimized architectures ensure faster results and better data quality.
