Azure Data Factory

It is a cloud-based ETL (Extract, Transform, Load) service designed for serverless data integration and transformation at scale. Key Features: Serverless Data Integration: Automates and orchestrates data workflows across various data sources. Scalability: Processes large-scale data with a scalable and cost-efficient model. Hybrid Data Movement: Supports both on-premises and cloud data sources. Data Transformation: Enables data ingestion, transformation, and processing. Components Datasets: CSV Dataset: Refers to the Blob Storage linked service and defines the schema of the CSV file (columns, data types, etc.). SQL Table Dataset: Refers to the SQL Database linked service and defines the schema of the target SQL table. Activities: It is defining a dataset involves specifying the schema and location of the data you want to interact with. Ex: Source Dataset: Define a dataset pointing to an on-premises SQL Server. Sink Dataset: Define a dataset pointing to an Azure SQL Database. Copy Activity: Configure the Copy Activity to read from the source dataset and write to the sink dataset with necessary column mappings. Execution: Run the pipeline to start the data copy process. Data Flow: It provides a way to transform data at scale without any coding required. Data transformation jobs can be designed in the data flow designer by constructing a series of transformations. Flowlet: A reusable component in data flows that simplifies complex transformations Stay Connected! If you enjoyed this post, don’t forget to follow me on social media for more updates and insights: Twitter: madhavganesan Instagram: madhavganesan LinkedIn: madhavganesan

Mar 27, 2025 - 03:12

It is a cloud-based ETL (Extract, Transform, Load) service designed for serverless data integration and transformation at scale.

Serverless Data Integration: Automates and orchestrates data workflows across various data sources.
Scalability: Processes large-scale data with a scalable and cost-efficient model.
Hybrid Data Movement: Supports both on-premises and cloud data sources.
Data Transformation: Enables data ingestion, transformation, and processing.

CSV Dataset: Refers to the Blob Storage linked service and defines the schema of the CSV file (columns, data types, etc.).

SQL Table Dataset: Refers to the SQL Database linked service and defines the schema of the target SQL table.

It is defining a dataset involves specifying the schema and location of the data you want to interact with.

Ex:
Source Dataset: Define a dataset pointing to an on-premises SQL Server.

Sink Dataset: Define a dataset pointing to an Azure SQL Database.

Copy Activity: Configure the Copy Activity to read from the source dataset and write to the sink dataset with necessary column mappings.

Execution: Run the pipeline to start the data copy process.

It provides a way to transform data at scale without any coding required.
Data transformation jobs can be designed in the data flow designer by constructing a series of transformations.

A reusable component in data flows that simplifies complex transformations

Stay Connected!
If you enjoyed this post, don’t forget to follow me on social media for more updates and insights:

Twitter: madhavganesan
Instagram: madhavganesan
LinkedIn: madhavganesan