Data Pipelines in Microsoft Fabric

  Data Pipelines in Microsoft Fabric

Microsoft Fabric offers a range of features to elevate your data analytics experience, including the Data Pipeline, built from Azure Data Factory. Even if you're used to working with Power Query Dataflows, the addition of Data Pipelines brings substantial value and enriches the overall experience. In this piece, we will delve into what a Data Pipeline entails within Microsoft Fabric Data Factory and present a straightforward example to demonstrate its application.

What is a Data Pipeline or Data Factory Pipeline?

A Data Pipeline or Data Factory Pipeline, originating from Azure Data Factory, is essentially a set of activities organized into a workflow. For instance, you could design a process that entails running a Dataflow repeatedly until a specific condition is satisfied. Based on the outcome of each iteration, you can automate tasks such as sending an email, copying data, or executing a stored procedure.


If you're acquainted with SQL Server Integration Services (SSIS), you can think of the Data Pipeline as the Control Flow and the Dataflow as the Data Flow.

While Data Pipelines have similarities with Power Automate, they provide capabilities that are not easily attainable in Power Automate. Specifically designed for Data Factory, Data Pipelines enable large-scale data movement and encompass activities like Dataflow Gen2, data deletion, and the execution of Fabric Notebook or Spark job definitions.


Data Factory with Transformation Engine of Dataflow

The integration of Data Pipelines with Dataflow's transformation engine is a significant advantage. Previously, Data Factory was mainly used for data ingestion and, although it could handle transformations using SSIS, it lacked the simplicity of Power Query transformations. However, in Microsoft Fabric, it is now possible to create a Data Factory Pipeline that incorporates a Dataflow Gen2 as one of its activities, in addition to other tasks.

Data Pipeline vs. Dataflow

I'm sure you're curious about the distinction between a Data Pipeline and a Dataflow. They serve different purposes, and it's important to understand their roles.

When it comes to data transformation, Dataflows take the lead. They use Power Query to shape and format data from the source to the destination. On the other hand, Data Pipelines are responsible for managing the execution flow. They use control flow activities like loops and conditions to orchestrate a larger ETL process.

Summary 

Yet another extremely significant component of Microsoft Fabric Data Factory is the Data Pipelines. These play a vital role in managing the control flow of execution, complementing the functions of Dataflows which primarily focus on data transformations. Data Pipelines main function involves orchestration of these transformations within a larger ETL (Extract, Transform, Load) process. Offering capabilities for looping, conditional execution, and integration of various data-related tasks, Data Pipelines are an indispensable part of executing complex data workflows. Keep an eye out for more in-depth articles on the functionalities and features of Data Pipelines

Comments

Post a Comment

Popular posts from this blog

Embedding Power BI Reports in Websites and Applications: A Complete Guide

Embedding Power BI Reports in Websites and Applications: A Complete Guide Introduction Embedding Power BI reports in websites and applications is a powerful way to share interactive data visualizations with a broader audience. Whether you want to embed a report for internal users, customers, or the general public, Power BI provides several options to make this process seamless. In this guide, we will walk you through the various embedding options and the steps required to embed Power BI reports in your web pages or custom apps. 1. Why Embed Power BI Reports? Embedding Power BI reports offers several benefits: Increased Accessibility : Allow users to view and interact with reports without needing to access Power BI Service directly. Enhanced User Experience : Integrate data insights directly within your application or website for a seamless user experience. Improved Data Sharing : Share valuable insights with users outside your organization, such as clients or partners, in a secure and ...
Read more

Import Microsoft Planner Data into Power BI Using Power Automate and SharePoint

Image
  In this guide, we’ll cover how to bring task data from Microsoft Planner into Power BI for analysis, using Power Automate and SharePoint as intermediaries. Since Power BI currently lacks a direct Planner connector, Power Automate lets us fetch the data from Planner and save it to SharePoint as a JSON file, enabling Power BI to access it on a scheduled basis. Prerequisites Before you begin, ensure you have: Access to Microsoft Planner  – to view and retrieve Planner task data. Power Automate License  – access to create and run Power Automate flows. SharePoint Access  – a SharePoint site to store JSON files. Using SharePoint simplifies the setup, as it doesn’t require a premium Power BI or Power Automate license. Power BI Desktop  – to set up and configure the JSON data connection and publish the report. Microsoft 365 Account  – for authentication, as we’ll be connecting Power BI to SharePoint through your organization’s Microsoft 365 credentials. Step 1: S...
Read more

Difference between Append and Merge in Power BI

Image
Append vs. Merge in Power BI: Key Differences and Use Cases Introduction When working with data in Power BI, combining datasets is often essential for creating comprehensive reports. Power BI offers two primary methods for combining tables: Append and Merge . While both are used to consolidate data from multiple sources, they work in different ways and are suited for different scenarios. In this blog, we will explore the key differences between Append and Merge in Power BI, along with practical examples to help you choose the right approach for your data needs. 1. What is Append in Power BI? Append in Power BI is used to combine rows from two or more tables into a single table. The structure of the tables being appended should be similar (i.e., they should have the same columns), as Append simply stacks the rows on top of each other. Key Features of Append: Row Combination : Append combines rows from multiple tables, one after the other. No Column Matching : Columns are not matched b...
Read more