Learn Azure Data Factory with 15+ Real time based projects

Explore the business use case of moving image data from Amazon S3 to Azure Blob using a Data Factory pipeline, covering architecture, setup, and a scheduled practical workflow.

Set up the project by creating an S3 image, an Azure blob container, and a data factory to copy data from S3 to Azure, via a link service, then schedule.

Log into your account, create an Amazon S3 bucket and upload the superstore file, then set up Azure blob storage and a data factory pipeline in Data Factory Studio.

Build and run an Azure Data Factory pipeline that copies data from Amazon S3 to Azure Blob storage, using link services, data sets, and a copy activity.

Move data from a blob storage container to an output container using an Azure Data Factory pipeline, perform a copy activity, and delete files after the copy completes.

Set up Azure blob storage with input and output containers, build a data factory pipeline to move data from the label container to the output container, then delete source files.

Log into the Azure portal, create a blob storage account and two containers (raw and output), then set up a data factory and build a data pipeline to transfer data.

Build and run Azure Data Factory pipeline that copies CSV data from a raw blob to an output container, then publish, monitor, and delete raw files with a second pipeline.

Demonstrates building an Azure Data Factory pipeline to move data from an SQL database to Azure Blob storage in CSV format for daily analytics.

Learn project setup in Azure Data Factory: create database and blob storage, connect link services and datasets, and build a pipeline to transform data from Azure database to blob storage.

Set up Azure SQL database and server, configure authentication and networking, create a storage container, and build a Data Factory pipeline to move sample data to CSV in blob storage.

Create a data pipeline in Azure Data Factory by configuring a source dataset, link services, and a csv file sink in blob storage, then publish, trigger, and monitor.

Identify how to move only a specific column from an Azure SQL table to Azure Blob storage using a Data Factory pipeline, enabling focused analysis in CSP format.

Set up project by creating a database, explore queries and tables, and configure Azure Blob Storage with an Azure Data Factory to move data via link service, dataset, and pipeline.

In this project setup lab, configure an Azure SQL database, a storage account, and an Azure Data Factory pipeline to move data from the sample database to the output container.

Create a data factory pipeline to copy selected SQL columns to Azure Blob storage as CSV, then run, monitor, and validate product ID, color, standard cost, and list price.

Explore the business use case of ingesting external data into Azure blob storage, and convert CSV data into a table-formatted Azure database.

Set up an Azure Data Factory project by creating a storage account and blob container, configuring a database and server, performing validations, and provisioning a Data Factory pipeline.

Build an Azure Data Factory data pipeline that copies CSV data from Azure Blob Storage to an Azure SQL database, creating source and sink datasets, linked services, and monitoring.

Learn to build data pipeline in Azure Data Factory that ingests CSV files from Azure cloud storage, joins on IDs, performs transformation and aggregation, and stores results in Azure Mysteries.

Set up Azure Blob Storage and create raw one, raw two, and output containers, then configure an Azure Data Factory pipeline with a data flow for CSV data.

Create a data pipeline in Azure Data Factory by building a data flow that joins two sources, maps schemas, and outputs the transformed data to blob storage.

In this introduction, learn to extend an Azure Data Factory data pipeline by uniting two files, performing transformations like aggregation, and preserving the final output across multiple transformations.

Set up an Azure data factory project by creating a blob storage with three containers (raw one, raw two, output) and building a data pipeline and data flow.

Train with Azure Data Factory by building a data flow that ingests from blob storage, performs union, uppercase transformation, sorting, and aggregation, and writes results to an output container.

Explore data flow transformations and data cleaning in Azure Data Factory, using multiple options to turn unstructured data into meaningful information and load it into blob storage via a pipeline.

Create a storage account and a data factory, set up raw and output containers, and upload an employee csv to prepare a data pipeline in Azure Data Factory.

Create and debug a data flow in Azure Data Factory to transform CSV data from blob storage, apply filters, sorts, and aggregates, then output CSV results.

Explore incremental data load in Azure Data Factory, loading only new daily data into Azure Blob Storage from source systems, illustrating practical business use cases for avoiding reloading previous data.

Set up Azure Data Factory projects by creating a storage account and blob storage, provisioning a SQL database, and building a Data Factory pipeline that loads data incrementally.

Learn how to create an incremental data pipeline in Azure Data Factory by configuring data flow, source, dataset, linked service, and blob storage output, including incremental extraction and testing.