Learn Microsoft Fabric Basic to Advance with Practical

Create a Microsoft Fabric account with an organizational email, verify and start the 60-day free trial in Power BI, then explore Fabric features.

Explore Microsoft Fabric's lakehouse and integrated features for data engineering, data science, and real-time analytics, including notebooks, SQL endpoint, and Power BI integration.

Discover Microsoft Fabric architecture and its one lake approach that unifies data warehousing, data engineering, data science, and real-time analytics in a single SaaS platform.

Explore data lake concepts, including data lake, one lake, lake house, and parquet formats, then build a lake house, use the SQL endpoint for processing, and create visualizations.

Discover how data lake stores structured, unstructured, and semi-structured data in one place and how One Lake unifies lakehouse, parquet, Delta Lake, and time travel for batch and streaming analytics.

Create your first lakehouse in Microsoft Fabric by setting up a workspace, creating the lakehouse, and ingesting data from local CSV files and external shortcuts.

Master the SQL endpoint in lakehouse to run queries and inspect the sales table. Compute top records and total revenue by item using group by and basic SQL features.

Explore the visual query editor to analyze data without writing SQL, drag tables, select columns, and apply group by to compute total revenue.

Create visualizations in the lakehouse by using the model to relate tables, hide sensitive fields, and build a new report with bar charts and tabular data, then export or share.

Learn the essentials of data warehousing with Microsoft Fabric, covering dimensional modeling, star and snowflake schemas, and building a first data warehouse. Query, visualize dashboards, and monitor warehouse performance.

Explore how data warehouses enable analytics and reporting by ingesting and transforming data from lakehouses, organizing it with star or snowflake schemas and dimension and fact tables.

Explore dimensional modeling in data warehouses by building star and snowflake schemas with dimension and fact tables, such as dim_product, dimension_date, and fact_sales, using primary and foreign key relationships.

Set up a data warehouse in Microsoft Fabric, create schemas and tables, run scripts, and build a star schema with dim product, dim date, dim customer, and fact sales.

Write sql queries to compute monthly sales from the fact sales order and date dimensions, then create region wise sales views and visualize the results.

Explore data visualization atop a data warehouse using Power BI, building region-wise and product-wise sales charts, hiding unused keys, and saving the report in a destination workspace.

Monitor data warehouse performance by running queries and using the system tables sis.dm_exec_connections, sis.dm_exec_sessions, and sis.dm_exec_requests; identify long-running queries and optimize operations in Microsoft Fabric and Power BI.

Explore large-scale data analytics with Apache Spark, notebooks, dataframes, and data transformation using PySpark and Pi SQL, plus Delta table partitioning, Spark SQL API, data visualization, and GIS.

Explore big data analytics and handling voluminous structured, unstructured, and semi-structured data beyond relational databases using distributed processing with Apache Spark.

Explore how Apache Spark enables distributed parallel processing for large-scale data analytics across a Spark cluster. Learn Spark SQL, PySpark, Java, Scala, and R within Microsoft Fabric.

Enable spark settings in Microsoft Fabric by configuring the spark compute starter pool, a memory-optimized, medium-node setup with spark 3.3, delta 2.2, and libraries and properties for notebooks.

Load data into a dataframe with PySpark using spark.read.load or spark.read.format csv, header true, from files/products.csv, and use magic to run Pi spark.

Transform your data with PySpark by cleaning, selecting columns, and filtering with where, then perform aggregation with group by and count. Save and partition the transformed data for practical workflows.

Save cleaned data to Parquet files in Spark, using dataframe write with overwrite mode, and partition by category to improve query performance by reading from specific partitions.

Explore the Spark SQL API in PySpark to create tables, save delta tables from data frames, and run SQL queries for category-based aggregations.

Learn to visualize spark notebook outputs with built-in charts, switch between pie and bar charts, save images, and use matplotlib with pyspark to create custom visuals from spark sql.

Explore how Apache Spark integrates with Microsoft Fabric, focusing on Delta Lake tables, parquet formats, and managed versus external Delta tables, plus time travel and streaming data into the lakehouse.

Explore Parquet file format and Delta Lake concepts, contrasting row-based and columnar storage. Learn how Delta Lake adds acid properties, time travel, and streaming and batch data support.

Learn to create managed and external delta tables in a lakehouse with spark; load data, save as delta tables, and use the delta table builder api or spark sql.

Create an external delta table and insert data using a data frame with spark context parallelize, then append via spark sql and verify with a select.

Update delta table values using SQL and Spark, then explore Delta table time travel and version history to retrieve previous data by version or timestamp.

Explore streaming to handle high-velocity data from IoT devices, transforming json files from lake house into a delta table while automating processing as new data arrives.

Create a streaming pipeline in Microsoft Fabric by building source and sink folders, defining json schema, reading streams with max files per trigger, and writing to delta table with checkpointing.

Explore ETL by extracting data from diverse sources, transforming it with cleaning and aggregation, and loading into a Delta table or data warehouse. Automate with data pipelines for continuous integration.

Create a data pipeline in data factory, add copy data, delete data, and notebook activities, configure http source to lakehouse, run, validate, and perform transformation.

Learn how to pass dynamic table names from a data pipeline to a notebook using a parameter cell and base parameters, enabling sales_july to sales_august in the lakehouse.

Validate pipelines to catch errors, run them manually, and view run history with start times and IDs; schedule weekly executions with start and end times, and review code and status.

Explore data flow gen two and data pipelines to transform data with Power Query Online, load into Lakehouse, and automate ETL using Azure services.

Explore real time data analytics in Microsoft Fabric using the K SQL database and KQL, enabling log analytics and IoT data processing from industries like e-commerce, agriculture, and health IT.

Create a lakehouse, upload a sales CSV, and configure a real-time sql database using the Kusto query language to query and visualize data.

Learn to create and populate a products table, filter and sort data, and use summarize with aggregate functions to analyze sales in a KQL query set.

visualize kql query results in power bi by aggregating sales data and creating charts, tables, and kpis for real-time analytics in fabric.

Discover how to run data science experiments in Microsoft Fabric, including predictive analytics, steps to gather requirements, prepare data, train and evaluate models, and features like notebook, experiment, and run.

Explore regression, classification, and forecasting models and learn how notebooks, experiments, and runs in Microsoft Fabric enable model building, evaluation, and comparison with mlflow.

Launch a notebook in microsoft fabric, load the diabetes prediction dataset into a pandas dataframe, and prepare features with one-hot encoding for categorical columns like gender and smoking history. Then split the data into training and testing sets to build a machine learning model.

Create and manage a mlflow experiment in Microsoft Fabric, enable auto logging, and compare two models—logistic regression and a decision tree classifier—by logging parameters, metrics, and artifacts.

Learn to compare two models or experiment runs in the data science dashboard, view metrics like accuracy and F1 score, visualize comparisons with bar charts, and save the selected model.