Extract, Transform , & Load with Azure Data Factory

What is ETL?

ETL (extract, transform, load) is one of the most widely used data integration methodologies.

ETL consists of three steps:

 Firstly, Data is extracted from a source location, such as a file or database.

 Next, the data is changed from its original format to meet the schema of the target site.

Final step is to load the modified data into a destination place, such as a data warehouse, where it may be used for analytics and reporting.

The data you require for your analytics tasks could be in a variety of formats and places, both inside and outside your company. This data should be maintained in a centralised repository, such as a data warehouse, for best efficiency. ETL is an important aspect of the data transfer process since it makes integrating several data sources easier and more efficient.

Finally, ETL and ELT, set of data integration paradigm, are closely related to each other. The order in which ETL and ELT complete the “Load” and “Transform” processes differs. To put it another way, ELT transforms data that has already been stored into the data warehouse. When consuming vast amounts of unstructured data, ELT allows data professionals to pick and select the data they wish to transform, saving time. Addend Analytics’ experts can help you with the right choice!

What is Azure Data Factory (ADF)?

Azure Data Factory (ADF) is a service that enables developers to combine data from various sources. It’s a Microsoft Azure platform for resolving issues with data sources, integration, and storage of relational and non-relational data. Azure Data Factory’s job is to build data factories on the cloud. To put it another way, ADF is a cloud-based managed service designed for complicated hybrid extract-transform-load (ETL), extract-load-transform (ELT), and data integration projects. ADF also includes an always-up-to-date monitoring dashboard, so you can deploy your data pipelines and immediately see them in your monitoring dashboard. Also, HD Insight, Hadoop, Spark, Azure Data Lake, and other computing services are supported by Azure Data Factory. Addend Analytics can help you maneuver ADF with ease!

ETL with Azure
Microsoft SSIS vs Azure Data Factory
Microsoft SSIS (SQL Server Integration Services) is an on-premises data migration and integration tool that is part of the Microsoft SQL Server database software. SSIS first appeared in SQL Server 2005 as a replacement for Microsoft’s Data Transformation Services (DTS) toolkit. Before the introduction of Azure Data Factory, SSIS was the dominant tool for building data integration and transformation pipelines to and from an SQL Server.
SSIS has a wide range of capabilities, including the following:
SSIS or ADF for ETL?

Azure Data Factory is a mature and reliable solution for combining structured, semi-structured, and unstructured data from Microsoft SQL Server, Azure SQL Database, Azure Blob Storage, and Azure Table Storage sources. It also works nicely with Microsoft's business intelligence and analytics tools, such as Power BI and Azure HDInsight.

While SSIS was formerly Microsoft's favored tool for developing ETL data pipelines, Azure Data Factory's own Mapping Data Flows functionality now gives it a run for its money. To determine which data transfer option is better for your needs, weigh the advantages and disadvantages of Mapping Data Flows and SSIS.

Lastly, despite the launch of Azure Data Factory, SSIS isn't going away anytime soon—the two tools enjoy a friendly rivalry if you will. The Integration Runtime is a feature in newer versions of Azure Data Factory that allows data to be integrated across multiple network settings. This functionality enables Azure Data Factory to run SSIS packages (automated import and export pipelines between different data sources).

Key Benefits of Azure Data Factory

ADF service allows businesses to transform all of their raw big data from relational, non-relational, and other storage systems into actionable information. 

For convenience of use, the ADF service has a drag-and-drop interface. 

You can iteratively create, debug, deploy, operationalize, and monitor your big data pipelines using visual tools. 

Companies may use Azure Data Factory to construct and plan data-driven workflows, known as pipelines, that can ingest data from a variety of sources.