Microsoft fabric beginner to intermediate

Create and log into a Microsoft Fabric account by provisioning an Azure-based intra ID user, enabling authentication with the Microsoft authenticator app, and starting the 60-day Fabric free trial.

Discover how Microsoft Fabric enables a unified analytics platform with one lake, one copy of data, and serverless compute, uniting data engineering, data science, and Power BI.

Explore Microsoft Fabric One Lake, a unified logical data lake across Azure Data Lake Gen2 or Amazon S3, with multiple workspaces, access control, and data shortcuts that avoid copying.

Explore how Microsoft Fabric workspace serves as a container inside a Fabric account, enabling multiple workspaces, file uploads, and the creation of data engineering, data science, and real-time analytics projects.

Create a Microsoft Fabric lakehouse in a workspace to store delta tables and files, and get a SQL analytics endpoint with a Power BI semantic model.

Upload files to the lake house, create subfolders, and load CSV data into delta tables with transaction logs and parquet storage, while JSON may require a pipeline for table creation.

Load data into the lake house with dataflow gen2, transform json via Power Query, source from Snowflake or other systems, and publish as delta tables in the lake house.

Navigate the sql analytics endpoint in fabric to connect via the lakehouse, run and save sql queries, preview and visualize data, and monitor query activity across data flows.

Build a visual query in Microsoft Fabric by dragging tables, transforming and renaming columns, and merging queries to inner join data, with optional SQL view and saving as a view.

Learn to build shortcuts in Microsoft Fabric that point to data in S3, ADLS Gen2, Dataverse, and Google Cloud without moving data, creating table-like shortcuts in a lake house.

Set up and run a data pipeline in Microsoft Fabric using Data Factory and Data Flow Gen2, orchestrating copy data from sources to a lake house.

Set up data flow Gen2 in fabric inside a data pipeline to orchestrate data movement and transformation. Load transformed data into the lake house and publish the pipeline.

Learn to schedule and monitor data pipelines in Microsoft Fabric, including setting triggers, reviewing run history, and inspecting activity details in the monitor hub.

Learn about data warehouse in fabric, a lake-centric sas on a single lake that enables cross-database querying and seamless Power BI integration for tabular data.

Learn to create and manage tables in a Microsoft Fabric warehouse using SQL templates, insert data, and run select and update queries.

Explore how to alter a table in fabric warehouse using CTAS, creating a new table with cast data and swapping it with the original to change a column type.

Learn to ingest data into a Fabric warehouse table with the copy into command from external storage, using parquet or CSV formats and options like skipping headers.

Learn to copy data into Fabric Warehouse using a data pipeline, from selecting sample data to creating a new table, configuring load settings, and running the pipeline end-to-end.

Learn to reference warehouses in Microsoft Fabric by using lake houses to query across databases without copying data, and create views that point to external tables.

Explore near instant zero-copy table clones in fabric warehouse, which copy only metadata and point to the same data files, with no cross-workspace, cross-warehouse, or schema cloning.

Explore Apache Spark in Fabric, a distributed framework for fast, parallel data processing within Fabric notebooks using PySpark, Spark SQL, or R.

Master how to work with notebooks in fabric to read data from lake house, create spark data frames, and run spark sql queries with drag-and-drop workflows.

Transform and load data into delta tables using fabric notebooks with spark data frames, adding year, quarter, and month, casting types, aggregating sales, and saving as delta format.

Master SQL and temporary views in spark notebooks, transforming data with Spark SQL, creating temp views from dataframes, and performing joins to store results as delta tables.

Schedule Spark notebooks in Microsoft Fabric and integrate them into a pipeline, including daily repeats, time zones, start and end dates, and linking to existing pipelines.

Harness real time intelligence in fabric to ingest streaming data, connect sources via no-code connectors, store in the event house, and power near real time dashboards with Kusto query language.

Create an event house in Fabric and load data to enable real-time analytics; ingest stock data via Event Hub or S3 and query with KQL.

Transform real-time data in fabric using kql to take, project, filter, and summarize stocks data. Summarize with min, max, and average to analyze by ticker and date.

Visualize real-time intelligence data in fabric using Kusto query language (KQL) by rendering stocks data as bar, column, and time charts, and review table and query stats.

Build end-to-end real time data pipelines with fabric event stream by ingesting streaming data, transforming events, and loading into a kql database or lakehouse delta table.

Discover how Microsoft Fabric applies retention policies to event streams, typically storing data for 2–3 days, and learn to delete an event stream to prevent costs.

Identify data activator in fabric as a no-code tool that automatically triggers alerts and workflows when changing data meets set conditions, monitored by reflex in Power BI and event streams.

Build a data activator pipeline in Microsoft Fabric by creating a reflex from an event stream, linking objects and triggers to actions such as email alerts.