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Microsoft Certified: Fabric Data Engineer Associate - Coggle Diagram
Microsoft Certified: Fabric Data Engineer Associate
Securing and managing an analytics solution.
Monitoring and optimizing an analytics solution.
Ingesting and transforming data.
Ingest Data with Dataflows Gen2 in Microsoft Fabric
: Data ingestion is crucial in analytics. Microsoft Fabric's Data Factory offers Dataflows for visually creating multi-step data ingestion and transformation using Power Query Online
Orchestrate processes and data movement with Microsoft Fabric
: Microsoft Fabric includes Data Factory capabilities, including the ability to create pipelines that orchestrate data ingestion and transformation tasks.
Use Apache Spark in Microsoft Fabric
: Apache Spark is a core technology for large-scale data analytics. Microsoft Fabric provides support for Spark clusters, enabling you to analyze and process data in a Lakehouse at scale.
Work with real-time data in an Eventhouse in Microsoft Fabric
: An eventhouse in Microsoft Fabric is a container that houses one or more KQL databases for storing and analyzing real-time data.
Implement a Lakehouse with Microsoft Fabric
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Introduction to end-to-end analytics using Microsoft Fabric
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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
Implement lifecycle management in Fabric
Configure version control
Implement database projects
Create and configure deployment pipelines
Configure security and governance
Implement workspace-level access controls
Implement item-level access controls
Implement row-level, column-level, object-level, and folder/file-level access controls
Implement dynamic data masking
Apply sensitivity labels to items
Endorse items
Implement and use workspace logging
Orchestrate processes
Choose between a pipeline and a notebook
Design and implement schedules and event-based triggers
Implement orchestration patterns with notebooks and pipelines, including parameters and dynamic expressions
Ingest and transform data (30–35%)
Design and implement loading patterns
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
Ingest and transform batch data
Choose an appropriate data store
Choose between dataflows, notebooks, KQL, 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
Ingest and transform streaming data
Choose an appropriate streaming engine
Choose between native storage, mirrored storage, or shortcuts in Real-Time Intelligence
Process data by using eventstreams
Process data by using Spark structured streaming
Process data by using KQL
Create windowing functions
Monitor and optimize an analytics solution (30–35%)
Monitor Fabric items
Monitor data ingestion
Monitor data transformation
Monitor semantic model refresh
Configure alerts
Identify and resolve errors
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 performance
Optimize a lakehouse table
Optimize a pipeline
Optimize a data warehouse
Optimize eventstreams and eventhouses
Optimize Spark performance
Optimize query performance