is a fully managed tool that offers a cloud-based solution for integrating data from various sources. It serves as a fully managed and serverless solution that enables ingesting, preparing, and transforming data at scale. Azure Data Factory (ADF) Extract-Transform-Load (ETL) data integration Its power lies in its user-friendly interface, cost-effectiveness, and code-free service. As the world is experiencing exponential growth of data, ADF stands out among other ETL tools due to its scalability and ability to accommodate businesses of all sizes. ADF allows users to build and orchestrate ETL/ELT pipelines without the need for extensive coding. The visual interface enables users to drag and drop components onto a canvas, simplifying the development process. Linked Services Linked services are crucial in ADF as they establish connections between ADF and various data sources, including cloud services and on-premises services. They act as a bridge, holding the connection strings necessary to establish communication between ADF and external systems for data ingestion, transformation, and loading. To illustrate this, imagine a production line in a factory. The linked services are like the suppliers who deliver the raw materials required for the manufacturing process. Similarly, in ADF, linked services are responsible for delivering the data from various sources to enable the data integration workflows. Though a Dataset in ADF can be associated with multiple linked services, it's common to have a one-to-one relationship between a Dataset and a Linked Service. linked services in Azure Data Factory facilitate seamless connectivity between ADF and diverse data sources, ensuring a smooth and efficient data integration process. Datasets Datasets in ADF serve as reference points or views of the actual data to be used in data integration activities. They provide the structure and metadata for the data within ADF. However, to populate a dataset with actual data, a connection to the corresponding data storage is required using a linked service. In terms of our factory analogy, datasets can be compared to the specifications of a product being manufactured. They define the structure and attributes of the data we want to process. Linked services, on the other hand, can be seen as the suppliers who provide the raw data or materials according to the specifications defined by the datasets. Activities Activities in ADF are operations or actions performed on data. They can include data transformations, data movement, data processing, or specific computations. Each activity represents a step in a data workflow that consumes datasets or accesses data directly from storage, performs a specific operation, and generates output data. Activities can include data transformations, data movement, data processing, or running specific computations on the data. Pipelines Activities are logically grouped into pipelines to organize and manage them. A pipeline in ADF represents an end-to-end data workflow, orchestrating the execution of activities in a defined sequence or parallelism. Just like a production line in a factory, a pipeline encompasses the flow of data from its source, through processing stages or transformations, to the final destination. They provide a logical structure to manage and monitor the execution of activities, ensuring the reliable and efficient execution of data workflows. Linked services can be compared to connection strings in SQL Server Integration Services (SSIS), as they serve a similar purpose of establishing connections to data sources. Packages in SSIS are equivalent to pipelines in ADF. Connection Manager in SSIS is like the linked service in ADF. Source and Destination in SSIS are the same in ADF, although the destination is sometimes referred to as "sink" in ADF. Control Flow tasks in SSIS are activities in ADF. Data Flow in SSIS has a similar component in ADF. In summary, ADF combines linked services, datasets, activities, and pipelines to create efficient and scalable data integration workflows. Linked services establish connectivity to data sources, datasets define the structure of data, activities perform operations on this data, and pipelines organize these activities into cohesive workflows. Together, these components of ADF ensure seamless and efficient data operations from start to finish. The lead image for this article was generated by HackerNoon's AI Image Generator via the prompt "data factory"

Walkthroughs, tutorials, guides, and tips. This story will teach you how to do something new or how to do something better.

Azure Data Factory - Datasets and Linked Services

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

AWS Guides: How to Create an Instance in Ec2

10 reasons to give cloud computing a go

10 Microsoft Azure Courses for Beginners to Learn Azure Cloud Computing

The Noonification: Proglogging: The Developers Detective Toolkit (10/9/2023)

Top 10 AI Development Companies in USA

2023 Will Be the Year of Kubernetes (and Other Predictions in the Cloud and Infrastructure Industry)

AWS Guides: How to Create an Instance in Ec2

10 reasons to give cloud computing a go

10 Microsoft Azure Courses for Beginners to Learn Azure Cloud Computing

The Noonification: Proglogging: The Developers Detective Toolkit (10/9/2023)

Top 10 AI Development Companies in USA

2023 Will Be the Year of Kubernetes (and Other Predictions in the Cloud and Infrastructure Industry)

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps