After building a pipeline, we can add triggers to run our processes automatically at certain periods or in reaction to changing circumstances. We can also use pipelines to execute one or more tasks and access data sources or services via linked services. We are adding fast copy (data movement) capabilities to dataflows and pipelines with Data Factory in Microsoft Fabric.
What is Microsoft Fabric?
Image source: https://learn.microsoft.com/en-us/fabric/data-factory/pricing-overview
For companies that want a unified, integrated solution, Microsoft Fabric Consulting offers a complete analytics and data platform. Event routing in real time, data transit, processing, transformation, ingestion, and report generation are all included. Data Engineering, Data Factory, Data Warehouse, Real-Time Analytics, Data Science, and Databases are among its extensive range of services.
What is Data Factory?
Data Factory provides a modern data integration experience for ingesting, processing, and transforming data from several data sources. You may connect to on-premises and cloud data sources using more than 200 native connectors, and Power Query’s user-friendliness is incorporated.
Features of the Data Factory
You can transfer data across your preferred data repositories incredibly quickly with Fast Copy. You can transfer data across your preferred data repositories incredibly quickly with Fast Copy. Above all, Fast Copy allows you to import data for analytics into your Lakehouse and Data Warehouse in Microsoft Fabric.
High Level Features of Data Factory
Image source https://www.softwebsolutions.com/resources/fabric-data-analytics.html
- Dataflows
Compared to existing data conversion tools, the Dataflows designer offers over 300 transformations, including sophisticated AI-based data transformations, making it simpler and more versatile. Large-scale data transformations are made possible by Dataflow Gen2, which also supports a number of output destinations which write to Lakehouse, Azure SQL Database, Data Warehouse, and other locations.
More than 300 transformations, including AI-based choices, are available in the dataflows editor, which provides greater flexibility and ease of use than any other tool. Power Query’s Data Extraction by Example makes use of artificial intelligence (AI) to streamline the process, whether you’re modifying an existing table in the editor or extracting data from an unstructured data source like a web page.
- Data Pipelines
With data pipelines, you can create adaptable data workflows that satisfy your business requirements by utilizing the innovative rich data orchestration capabilities. Tasks like data extraction, loading into desired data storage, SQL script execution, notebook execution, and more may be combined into flexible data orchestration workflows with the help of data pipelines.
Strong metadata-driven data pipelines that automate tedious activities can be swiftly constructed. For instance, iterating across several Azure Blob Storage containers, importing and extracting data from various database tables, and more. Data pipelines also make it possible to access data from Microsoft 365 using the Microsoft Graph Data Connection (MGDC) connector.
- Copy Job
Copy jobs move data at petabyte-scale from any source to any destination, making the data intake process more straightforward and easier to use. Data can be copied using a variety of data delivery techniques, such as batch copy, incremental copy, and more.
- Apache Airflow Job
Directed Acyclic Graphs (DAGs) can be easily executed at scale using this straightforward and effective method of creating and managing Apache Airflow orchestration jobs. With the help of code, Apache Airflow gives you the ability to ingest, process, transform, and orchestrate data from a variety of data sources.
- Database Mirroring
Database Mirroring in Fabric is a low-cost, low-latency solution that was developed utilizing open standards (such the Delta Lake table format). It lets you swiftly duplicate metadata and data from several platforms. You can continually copy your data estate into Microsoft Fabric OneLake for analytical purposes by using Database Mirroring. You may now streamline the process of beginning your analytics needs with a highly integrated, user-friendly interface.
- Investment areas
Data Factory in Microsoft Fabric will expand its connectivity options in the upcoming months, and the extensive library of transformations and data pipeline activities will keep expanding. It also enables you to bring high-performance data into the lake for analysis by replicating it in real-time from databases that are currently operating.
Frequently Asked Questions
- What distinguishes the Fabric data engineering tab from the data factory tab?
While data engineering allows you to establish a lake house and use Apache Spark to prepare and convert your data, data factory uses cloud-scale data movement and transformation services to help you handle challenging data integration and ETL problems. The Microsoft Fabric terminology contains the distinctions between each of the Fabric terminologies and experiences.
- How do I keep tabs on the Fabric’s capacity when it comes to the pipelines?
Capacity administrators can learn more about available capacity with the Microsoft Fabric Capacity Metrics tool, sometimes known as the metrics app. With the help of this application, administrators may view the amount of memory, processing time, and CPU usage that dataflows, data pipelines, and other components in their workspaces with Fabric capacity are using. Get insight into the reasons behind overload, periods of high demand, resource usage, and more. You may also quickly determine which items are the most popular or demanding.
- Which method is suggested for role assignment in Fabric’s Data Factory?
Workloads can be divided among workspaces, and roles such as viewer and member can be utilized to create a data engineering workspace that prepares data for a report or AI training workspace. Data from the data engineering workspace can subsequently be consumed using the viewer role.
- Is it possible to access Fabric Data Factory resources which are already Private Endpoint (PE) enabled?
Currently, the virtual network gateway provides a robust approach to use private endpoints to provide secure connections to your data stores by easily integrating into your virtual network using an injective method. It is important to keep in mind that the virtual network gateway is currently limited to processing Fabric dataflows.
Our future plans, however, include extending its functionality to include Fabric pipelines.
- How can I use Fabric Data Factory to connect to on-premises data sources?
With Data Factory in Microsoft Fabric, you can now use dataflows and data pipelines (preview) to connect to on-premises data sources when using the on-premises data gateway. For additional information, see How to use Data Factory to access on-premises data sources.
Conclusion
The most complicated data factory and ETL scenarios can be resolved with the help of Microsoft Fabric’s data factory, which offers cloud-scale data movement and transformation services. Your data factory experience should be powerful, user-friendly, and genuinely enterprise-grade.
