How to use Dataflows Gen2 in Microsoft Fabric: A Guide
Microsoft Fabric
Feb 2, 2024 4:00 PM

How to use Dataflows Gen2 in Microsoft Fabric: A Guide

by HubSite 365 about Microsoft

Software Development Redmond, Washington

Data AnalyticsMicrosoft FabricLearning Selection

Transform Data Easily with Dataflows Gen2 in Microsoft Fabric - Dive into Simple Analytics!

Key insights

Introduction to Dataflows Gen2 in Microsoft Fabric: Dataflows Gen2 in Microsoft Fabric simplifies data ingestion and transformation through Power Query Online. It allows connecting to various data sources, performing transformations, and preparing data for analytical storage or Power BI reporting. A trial Microsoft school or work account is needed to use this feature.

  • Creating a workspace in Microsoft Fabric is the first step towards working with data, enabling users to access Synapse Data Engineering and set up a new workspace with Fabric trial enabled.

  • Creating a lakehouse follows the workspace setup, serving as a destination for ingested data. Utilizing Power Query Online, users can define a Dataflow (Gen2) encapsulating an ETL process for data ingestion.

  • Utilizing Dataflow (Gen2), data is ingested from selected sources like a Text/CSV file using Power Query Online, allowing for data transformation and the addition of new columns through custom formulas.

  • Dataflows can be published and added to pipelines for orchestrated data ingestion and processing, combining them with other operations in a single, scheduled process. This facilitates the creation of tables in lakehouses from ingested data.

  • Lastly, cleaning up resources is an essential final step to avoid unnecessary storage usage, which includes deleting the workspace created for experimenting with dataflows in Microsoft Fabric.

Exploring Dataflows Gen2 in Microsoft Fabric Further

Dataflows Gen2 in Microsoft Fabric represents a significant leap forward in data transformation and ingestion for cloud-based analytics. This solution streamlines the process of connecting to diverse data sources, manipulating and transforming data using the versatile Power Query Online, and funneling this refined data into various analytical repositories for deeper insights. Whether for constructing sophisticated lakehouses, augmenting datasets in Power BI reports, or facilitating intricate data pipelines for scheduled data processing tasks, Dataflows Gen2 emerges as a robust tool that seamlessly integrates within Microsoft's cloud ecosystem.

Its visual design and user-friendly interface democratize data processing capabilities, allowing not just data engineers but also analysts and business professionals to perform complex transformations with minimal coding. The collaborative aspect embedded in Microsoft Fabric enhances team projects and learning, encouraging a broad spectrum of users to engage in data analytics processes.

Moreover, the scalability and efficiency offered by Microsoft's cloud infrastructure ensure that these data transformations are executed swiftly, supporting real-time analytics and enabling businesses to react to insights quicker than ever. As part of the broader suite of tools in Microsoft's analytics and data management offerings, Dataflows Gen2 meshes smoothly with other services like Azure Synapse, Power BI, and more, forming a comprehensive, integrated analysis and reporting environment.

Adoption of Dataflows Gen2 can significantly reduce time-to-insight for organizations, streamline data preparation tasks, and unlock new opportunities for data-driven decision-making. By leveraging the full potential of cloud-based analytics with such advanced tools, businesses are better equipped to navigate the complexities of today's data landscapes, ensuring they remain competitive in a rapidly evolving digital world.

Learn Together: Ingest Data with Dataflows Gen2 in Microsoft Fabric Data ingestion plays a vital role in analytics. Microsoft Fabric's Data Factory introduces Dataflows (Gen2) for building multi-step data ingestion and transformation processes using Power Query Online. This feature connects to various data sources, allowing transformations in Power Query Online before data ingestion into analytical stores or for Power BI reports.

Create a Dataflow (Gen2) in the platform by connecting to data sources and transforming data in Power Query Online. These Dataflows can be incorporated into Data Pipelines for ingesting data into a lakehouse or defining datasets for Power BI reports. This tutorial aims to introduce Dataflows (Gen2) basics rather than complex enterprise solutions, taking around 30 minutes to complete, requiring a Microsoft school or work account.

Begin by creating a workspace in Microsoft Fabric after enabling the trial. Navigate to the Microsoft Fabric home page, select Synapse Data Engineering, and then create a workspace by following the provided steps. Once set up, your new workspace will be ready for use, marking the starting point of working with data in Fabric.

Next, construct a data lakehouse by navigating to the Synapse Data Engineering home page. Creating a lakehouse is a straightforward process, taking just a minute to complete. The creation of an empty lakehouse paves the way for data ingestion.

To ingest data, define a Dataflow (Gen2) that handles extract, transform, and load (ETL) processes. Start by selecting 'New Dataflow Gen2' in your workspace, leading to the Power Query editor. Here, import data from a Text/CSV file, setting up a new data source with specified settings and creating an initial set of query steps to format the data.

In the Power Query editor, add a new custom column in the 'Add column' tab. Set the new column name to 'MonthNo', apply a formula to extract the month number from date, and add this to your query. This addition is visually represented in the data pane, and you can manage transformation steps in the Query Settings pane on the right side.

Adjust the data types for 'OrderDate' and the newly created 'MonthNo' column to Date and Whole Number, respectively. Then, select a data destination for your Dataflow, choosing Lakehouse. Confirm the data destination settings and publish the dataflow, thereby creating it in your workspace.

Include the created dataflow in a pipeline to orchestrate data ingestion and processing, creating a pipeline named 'Load data'. After adding a Dataflow activity to this pipeline and configuring it, run the pipeline. Upon completion, check your lakehouse for the newly created orders table, a result of your dataflow process.

Additionally, leverage Power BI Desktop with a Dataflows connector for further data transformation, publishing, and distribution. When exploration is complete, clean up resources by deleting the workspace created for this exercise. This ensures no unnecessary resources are left in your Microsoft Fabric account.

Understanding Dataflows in Analytics

Dataflows are essential in modern data analytics, providing a flexible and visual approach to data ingestion and transformation. Microsoft Fabric revolutionizes this process with its Dataflows Gen2, leveraging Power Query Online for an accessible and powerful tool for analysts and data scientists. These dataflows simplify connecting to diverse data sources, transforming data according to business needs, and ingesting it into analytical models or reports seamlessly.

The ability to visualize and edit data transformations through Power Query Online not only improves efficiency but also enables a more intuitive understanding of data processes. By incorporating dataflows into Data Pipelines, users can automate and orchestrate complex data ingestion and transformation tasks, streamlining analytics workflows and reducing manual errors. Microsoft Fabric's integration with Power BI further enhances dataflows’ utility, allowing for direct data publishing and reporting.

Moreover, the platform's flexibility in data source connectivity, from CSV files to cloud-based sources, ensures that organizations can handle a wide range of data types and volumes. This adaptability is crucial in an era where data is rapidly expanding and becoming more diverse. Consequently, dataflows serve as a foundational tool in constructing robust analytics environments, enabling organizations to derive meaningful insights from their data more efficiently than ever.

In conclusion, Microsoft Fabric’s Dataflows Gen2 feature is not just a technical improvement in data processing but a strategic asset in the analytics toolkit. By simplifying and automating data ingestion and transformation, organizations can focus more on analytics and insights, driving informed decisions and competitive advantage.

Learn Together: Ingest Data with Dataflows Gen2 in Microsoft Fabric. Data ingestion plays a vital role in analytics. Microsoft Fabric's Data Factory introduces Dataflows (Gen2) for easily managing multi-step data ingestion and transformation through Power Query Online.

--- Create a Dataflow (Gen2) in Microsoft Fabric. Dataflows (Gen2) in Microsoft Fabric allow connections to various data sources and perform transformations in Power Query Online. These can be utilized in Data Pipelines for ingesting data into analytical stores or for creating datasets for Power BI reports. This instructional module aims to highlight the key features of Dataflows (Gen2), focusing on their integration into practical scenarios rather than building complex solutions.

Note: A Microsoft school or work account is necessary to execute this tutorial. If unavailable, one can opt to register for a trial of Microsoft Office 365 E3 or higher to gain access.

Set Up in Microsoft Fabric

Create a workspace - Begin by creating a workspace within Fabric to organize your data efforts. This step involves enabling the Fabric trial and naming your workspace appropriately based on the selected licensing mode.

Create a lakehouse - Following workspace setup, the next step involves establishing a data lakehouse. This component serves as the data’s final repository after ingestion and processing.

Create a Dataflow (Gen2) - With the lakehouse ready, you proceed to define a Dataflow (Gen2) that encapsulates an ETL process. The creation process includes selecting a data source and utilizing Power Query Online for data transformation. Steps covered entail importing data, custom column creation, and aligning data types accordingly.

Add data destination for Dataflow - Finalizing the dataflow involves setting a data destination within the lakehouse. This step is crucial for ensuring the processed data is correctly stored for future access and analysis. Moreover, it highlights the versatility of Microsoft Fabric in facilitating data management tasks.

Add a dataflow to a pipeline - Integrating the completed dataflow into a pipeline marks the final step. This integration emphasizes the orchestration capabilities of Microsoft Fabric, allowing for efficient scheduling and execution of data ingestion and processing activities. The tutorial underscores the practical application of Dataflows (Gen2) in real-world data management scenarios.

  • Creating workspaces and lakehouses simplifies data organization.
  • Defining Dataflows (Gen2) enhances ETL processes with visual tools.
  • Configuring data destinations ensures accurate data storage and accessibility.
  • Incorporating dataflows into pipelines optimizes data management workflows.
  • Microsoft Fabric's capabilities streamline data ingestion and transformation activities.

Understanding Microsoft Fabric and Data Management

Microsoft Fabric is pivotal in modern data management, offering advanced tools for data ingestion, transformation, and analytics. Its ability to connect to diverse data sources and perform complex transformations through Power Query Online showcases its versatility. Dataflows (Gen2), as introduced in Microsoft Fabric, play an essential role in simplifying ETL processes, enabling users to ingest data into various analytical stores effectively. This functionality is crucial for organizations looking to leverage data for insights and decision-making. Furthermore, the integration of these dataflows into pipelines demonstrates the seamless orchestration of data processing tasks, further emphasizing the robust capabilities of Microsoft Fabric in handling intricate data scenarios.

Microsoft Fabric - Master Dataflows Gen2 in Microsoft Fabric: A Guide

People also ask

Questions and Answers about Microsoft 365

[Begin Question] "How do you ingest data with a pipeline in Microsoft fabric?" [End Question] [Begin Answer] Answer: "Ingesting data can be efficiently done using a Copy Data activity within a pipeline to move data from its source directly into a file stored in the lakehouse. This can be initiated by going to the Home page of your lakehouse, selecting the option to create a new data pipeline, and naming it appropriately, for instance, 'Ingest Sales Data'." [End Answer] [Begin Question] "What is the difference between gen1 and Gen2 dataflows?" [End Question] [Begin Answer] Answer: "Gen 1 dataflows are restricted to using only internal/staging storage for dataset consumption or utilizing a bring-your-own-lake model, while Gen 2 introduces the flexibility to determine the destination of transformed data. This can include options such as the Fabric Lakehouse, Azure Data Explorer (Kusto), or Azure Synapse Analytics (SQL DW)." [End Answer] [Begin Question] "What is dataflow in fabric?" [End Question] [Begin Answer] Answer: "Dataflows in Fabric refer to a cloud-based, self-service data preparation facility that allows users to create, manipulate, and transform data before publishing it. This process is simplified to enable the creation of your first dataflow, sourcing data, transforming it, and then publishing the results." [End Answer] [Begin Question] "How do I get data from dataflow?" [End Question] [Begin Answer] Answer: "To extract data from Dataflows within Power BI Desktop:" [End Answer]


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