Analyze data with Apache Spark in Microsoft Fabric Lakehouse
In this comprehensive tutorial, we'll guide you through the essential steps of leveraging Microsoft Fabric for your data engineering needs. Starting with an introduction to the basics and benefits of using Fabric, we'll walk you through creating a Fabric Workspace and a Lakehouse, and uploading your data. You'll learn how to set up and use notebooks within Fabric for data processing, load data into a DataFrame, and define schemas using StructType. We'll cover methods for retrieving count and distinct count metrics, filtering and cleaning data, and performing aggregation operations. Additionally, you'll discover techniques for adding new columns, splitting columns, and saving your processed data as parquet files in the Fabric Lakehouse. We'll demonstrate how to read and utilize parquet files, partition data for optimized performance, and manage partitioned data efficiently. You'll also learn how to write DataFrame data into tables, query Delta table data using Spark-SQL, and visualize your data directly within Fabric notebooks. By the end of this tutorial, you'll have a solid foundation in using Microsoft Fabric for efficient data management, processing, and analysis. Don't forget to like, share, and subscribe for more in-depth tutorials!
=====⏱️Timestamps⏱️=====
00:07 Introduction
00:25 Create Fabric Workspace
01:15 Create Lakehouse and upload Data into Fabric lakehouse
03:00 Create a new Notebook in Fabric
03:42 How to load data into Dataframe
06:42 Define Schema using StructType
09:40 Get Count and Distinct count from Dataframe
11:35 Filter data in the Dataframe
12:32 Aggregate Dataframe data
13:21 How to add new column for the DataFarame
15:27 Split columns in the Dataframe
17:02 Save Dataframe data as parquet file in the Fabric Lakehouse
18:40 Read parquet data from Fabric Lakehouse
19:15 Partition data in the Fabric Lakehouse
20:55 Read Partitoned data from the Fabric Lakehouse
22:09 Write data into table using Dataframe data
23:20 Query Delta table data using Spark-SQL
25:10 Visualize dataframe data in the Notebook
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