Automating Data Pipelines with Microsoft Fabric for Faster Insights
Getting your data where you need it on time in a streamlined process is not always easy! Do you have semantic models with numerous queries and find it challenging to manage the steps needed to transform the data coming from the source to what you need? Or perhaps you need data in your reports as soon as it is captured?
Microsoft Fabric offers a solution. In this article, we dive into Data Pipelines and explore how you can automate your ETL processes. With the capabilities Data Pipelines offers, you can greatly simplify data management, enhance efficiency and ensure timely data availability for your reporting needs.
What are data pipelines in Fabric
Data Pipelines are components in Microsoft Fabric designed to automate the process of moving, transforming, and loading data from a big variety of sources into your desired destinations. They provide a streamlined approach to handling data workflows, allowing you to focus on analyzing data and driving insights. On top of that, it enables you to automate the process, making it possible to get insights more quickly.
Wondering if this won’t be too technical for you? Don’t worry! Data Pipelines are ideal for teams who want to automate and scale standard ETL processes in a low/no-code environment.
Key Components and Steps
Let’s take a look at which steps you need to take to go from your data source to data ready to be visualized in your reports.
Data Ingestion
First of all, you will need to get the data and store it somewhere. This is the first thing you will need to do when you land on the start page of your Data Pipeline.
As you can see, there are already a few options displayed here. You can either start with a blank canvas and pick activities yourself, or you can choose for a bit more guidance and go with one of the following actions: Copy data assistant, Practice with sample data or you can start out with a template.
Copy data assistant
Here you will get a wizard that will guide you through the steps of choosing a data source and a supported destination for the data.
Practice with sample data
This option will help you set up the ingestion of the Contoso sample data into a lakehouse destination of your choosing.
Templates
This option will provide you with a rich collection of templates to help you build the solution you need.
Pipeline activities
Want to get started? You also have the option to select a Pipeline activity of your own choice. There is a library of different activities available, among which the Copy activity and Dataflows Gen2 activity for data ingestion.
Data Transformation
If you completed the data ingestion step, you should’ve found a way to get the data you need into the pipeline. Unfortunately, data hardly ever comes in the shape we need for our reports.
If you choose to use the Dataflows activity, you will be able to do transformations here, using the Power Query interface. For Power BI developers, this can be a great place to start as this is usually already a part of their toolbox. It also offers a low code experience for transforming data, which could be a big benefit to some teams.
The copy data activity is faster than Dataflows but is meant to move data from one place to another without changing it. If you need to do transformations, you will need to use other activities to do these steps.
Data Storage
Depending on your data source, there are various possible destinations to store your data.
Here you can find some examples of available destinations (sinks):
Azure Data Lake Storage (ADLS): Ideal for storing large amounts of unstructured data.
Azure SQL Database: Suitable for relational data storage and supports SQL-based querying.
Azure Synapse Analytics: Provides a powerful analytics service that combines big data and data warehousing.
Azure Blob Storage: Useful for storing binary and text data.
Azure Cosmos DB: A globally distributed, multi-model database service.
Azure Data Explorer: Optimized for fast, ad-hoc data exploration and analytics.
Power BI: Allows you to load data directly into Power BI datasets for reporting and visualization.
Azure Table Storage: A NoSQL store for schemaless storage of structured data.
Something interesting here is that you can use Data pipelines to refresh your semantic models. You will need an activity to store the data into the source used in your semantic model and once the data is there, you can use the semantic model refresh activity to load new data into your model. This activity offers you various options. You can choose for a full model refresh, but can also opt for a partial refresh, targeting only the tables or partitions that need to be refreshed.
Data Orchestration
As mentioned in the previous section, you can use pipelines to automate data refreshes but also for setting up notifications to alert us of failures or successes.
Here are some scenarios where orchestration can be a game changer:
Predictive Maintenance for Equipment: IoT sensors in factories or vehicles collect data on machine performance. Automated pipelines process this data to predict failures early, reducing downtime and improving efficiency.
Dynamic Pricing Models: Airlines, e-commerce platforms, and ride-sharing services use automated pipelines to analyze demand fluctuations. This enables real-time price adjustments based on market conditions.
By being able to orchestrate what needs to happen when new data streams in, data pipelines make it easier to work with real time or near-real time data, without having to rely on Direct Query in your semantic models.
Furthermore, you can use different types of triggers to run your pipeline. It can be triggered manually, but it also offers scheduling and event-driven triggers. Event-driven triggers are very useful when dealing with data that comes in on an irregular basis and needs to be available in your report as soon as possible.
Fabric’s Data Pipelines are a powerful tool for getting faster insights in Power BI. The availability of Dataflows Gen2 as an activity offers users familiar with Power BI a seamless, recognizable experience. And besides that, Dataflows Gen2 also provides less technical users with a way to shape data using low-code transformations, without needing to use code-based notebooks. The orchestration capabilities make it possible to work with near real-time data, without relying on DirectQuery, by refreshing only the affected partitions or tables through the semantic model refresh activity. This flexibility improves performance and simplifies data management. Whether you're building complex data solutions or just getting started, Fabric’s Data Pipelines can streamline your workflow and boost productivity.
Ready to unlock faster, smarter insights?
Contact us today to discover how we can support your journey.
Author: Femke Coenye
Femke Coenye
Hello, my name is Femke. I’m a Power BI enthusiast with several years of experience under my belt. I have a knack for creating insightful, user-friendly reports and a passion for all things code. I take challenges head on and harness my creativity to find the best solutions.
My curiosity leads me to taking more and more steps outside of the Power BI playground and drives me to investigate other tools such as Fabric and gain all the knowledge I can.
When I’m not busy writing DAX or building visuals, I enjoy drawing, discovering the world and a good book. Want to chat? Feel free to reach out!
Anxious to know what Plainsight could mean for you?
Read more “About us”
Consider “your career at Plainsight 🚀”
Any questions? “Contact us”