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.

• Understand Dataflows Gen2 in Microsoft Fabric
• Explore Dataflows Gen2 in Microsoft Fabric
• Integrate Dataflows Gen2 and Pipelines in Microsoft Fabric
• Exercise - Create and use a Dataflow Gen2 in Microsoft Fabric

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.

• Understand pipelines
• Use the Copy Data activity
• Use pipeline templates
• Run and monitor pipelines
• Exercise - Ingest data with a pipeline

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.

• Prepare to use Apache Spark
• Run Spark code
• Work with data in a Spark dataframe
• Work with data using Spark SQL
• Visualize data in a Spark notebook
• Exercise - Analyze data with Apache Spark

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.

• Get started with an Eventhouse
• Use KQL effectively
• Materialized views and stored functions
• Exercise - Work with data in an Eventhouse

Introduction to end-to-end analytics using Microsoft Fabric

Discover how Microsoft Fabric can meet your enterprise's analytics needs in one platform. Learn about Microsoft Fabric, how it works, and identify how you can use it for your analytics needs.

• Explore end-to-end analytics with Microsoft Fabric
• Explore data teams and Microsoft Fabric
• Enable and use Microsoft Fabric

Get started with lakehouses in Microsoft Fabric

Lakehouses in Microsoft Fabric combine data lake storage flexibility with data warehouse analytical capabilities. Learn how to create a lakehouse, ingest and transform data, and query data with SQL and Spark.

• Describe lakehouse features and capabilities
• Ingest and transform data in a lakehouse
• Query and analyze lakehouse data
• Exercise - Create a Microsoft Fabric lakehouse

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.

• Prepare to use Apache Spark
• Run Spark code
• Work with data in a Spark dataframe
• Work with data using Spark SQL
• Visualize data in a Spark notebook
• Exercise - Analyze data with Apache Spark

Work with Delta Lake tables in Microsoft Fabric

Tables in a Microsoft Fabric lakehouse are based on the Delta Lake storage format commonly used in Apache Spark. By using the enhanced capabilities of delta tables, you can create advanced analytics solutions.

• Understand Delta Lake
• Create delta tables
• Optimize delta tables
• Work with delta tables in Spark
• Use delta tables with streaming data
• Exercise - Use delta tables in Apache Spark

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.

• Understand Dataflows Gen2 in Microsoft Fabric
• Explore Dataflows Gen2 in Microsoft Fabric
• Integrate Dataflows Gen2 and Pipelines in Microsoft Fabric
• Exercise - Create and use a Dataflow Gen2 in Microsoft Fabric

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.

• Understand pipelines
• Use the Copy Data activity
• Use pipeline templates
• Run and monitor pipelines
• Exercise - Ingest data with a pipeline

Organize a Fabric lakehouse using medallion architecture design

Explore the potential of the medallion architecture design in Microsoft Fabric. Organize and transform your data across Bronze, Silver, and Gold layers of a lakehouse for optimized analytics.

• Describe medallion architecture
• Implement a medallion architecture in Fabric
• Query and report on data in your Fabric lakehouse
• Considerations for managing your lakehouse
• Exercise - Organize your Fabric lakehouse using a medallion architecture

Get started with Real-Time Intelligence in Microsoft Fabric

Real-Time Intelligence in Microsoft Fabric helps you ingest, process, store, visualize, and act on data in motion to get insights from events as they happen.

• What is real-time data analytics?
• Real-Time Intelligence in Microsoft Fabric
• Ingest and transform real-time data
• Store and query real-time data
• Visualize real-time data
• Automate actions
• Exercise - Get started with Real-Time Intelligence in Microsoft Fabric

Use Eventstream in Microsoft Fabric

Eventstream in Real-Time Intelligence (RTI) enables you to ingest, transform, and route real-time data.

• Components of Eventstream
• Eventstream sources and destinations
• Eventstream transformations
• Exercise - Ingest real-time data with Eventstream in Microsoft Fabric

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.

• Get started with an Eventhouse
• Use KQL effectively
• Materialized views and stored functions
• Exercise - Work with data in an Eventhouse

Create Real-Time Dashboards with Microsoft Fabric

Real-Time Dashboard is a capability in Microsoft Fabric that you can use to create interactive data visualizations.

• Get started with real-time dashboards
• Organize and filter dashboard data
• Dashboard management and optimization
• Exercise - Get started with real-time dashboards

Use Activator in Microsoft Fabric

Fabric Activator is an event detection engine that automatically triggers actions when specific patterns or conditions are detected in data sources.

• Configure Activator for your data
• Create rules in Activator
• Configure actions in Activator
• Exercise - Use Activator in Fabric

Implement continuous integration and continuous delivery (CI/CD) in Microsoft Fabric

Microsoft Fabric implements CI/CD using Git integration and deployment pipelines. These tools help you collaborate with your development team and provide you with an efficient process for delivering and updating content.

• Understand Continuous Integration and Continuous Delivery (CI/CD)
• Implement version control and Git integration
• Implement deployment pipelines
• Automate CI/CD using Fabric APIs
• Exercise: Implement deployment pipelines in Microsoft Fabric

Monitor activities in Microsoft Fabric

Monitoring helps you gain visibility into the health of your data systems. Monitoring Hub in Microsoft Fabric collects and aggregates data from Fabric activities. Microsoft Fabric Activator helps you take actions when patterns or conditions are detected in streaming data.

• Understand monitoring
• Use Microsoft Fabric Monitor Hub
• Take action with Microsoft Fabric Activator
• Exercise - Monitor Fabric activity in the Monitor hub

Secure data access in Microsoft Fabric

Microsoft Fabric uses a multi-layer security model with access controls at different levels.

• Understand the Fabric security model
• Configure workspace and item permissions
• Apply granular permissions
• Exercise: Secure data access in Microsoft Fabric

Administer a Microsoft Fabric environment

Microsoft Fabric is a SaaS solution for end-to-end data analytics. As an administrator, you can configure features and manage access to suit your organization's needs.

• Understand the Fabric Architecture
• Understand the Fabric administrator role
• Manage Fabric security
• Govern data in Fabric

Use Activator in Microsoft FabricFabric Activator is an event detection engine that automatically triggers actions when specific patterns or conditions are detected in data sources.

Learning objectives By the end of this module, you'll be able to:

• Define data objects and properties in Activator
• Create rules that evaluate conditions in your data
• Configure actions that execute when rule conditions are met

Prerequisites

Before starting this module, you should be familiar with the Microsoft Fabric interface and core concepts.

• This module is part of these learning paths
• Implement Real-Time Intelligence with Microsoft Fabric
• Configure Activator for your data
• Create rules in Activator
• Configure actions in Activator
• Exercise - Use Activator in Fabric