Constructing an End-to-End Pipeline in Power BI for large datasets

by Siva Pokala

Welcome to this guide on using Power BI with large datasets! If you deal with a lot of data, especially for business and reporting purposes, you are in the right place. We are going to present an efficient way to handle no less than hundreds of millions of records using Power BI and a data warehouse.??

In our guide, we will derive our example using Snowflake, which is a data warehouse in Cloud, designed to handle huge volumes of data. However, the methods you will learn can be easily transferred applied to other data sources as well. This makes everything we are going to discuss very versatile.?

Our focus is on operational reporting, which means that we are interested in a deep dive in our data. To give you an idea, this is like knowing every single item of every invoice, rather than just the total amount of the invoice. It is detailed, and it is big!?

?In this guide, we will walk you through the entire process, starting with the connection between Power BI and Snowflake, all the way to ensuring that the reports are ready to be used.??

This guide is structured in two parts. The first part that you are about to read presents the connection to the data source and the construction of the data model. It is structured as a tutorial and also gives guidelines and good practices to help you avoid the commonly seen mistakes. Malpractices might seem of little relevance at first, but when you deal with large datasets, this can seriously endanger your entire work! We will advise you on the management of big tables like our fact table, line items and headers.??

Whether you are new to Power BI and Snowflake, or have been using them extensively, this guide is here to help you work confidently with big data sets.??

Let’s get started on mastering Power BI for large datasets!?

Connecting to Your Data: Snowflake and Dataflow

The initial step of our Power BI project is to connect to the primary data source: Snowflake. Here is how we can establish this connection:?

?Connect to Snowflake:? ?

• ?? Open Power BI Desktop.?
• Navigate to “Home” and select “Get Data.”?
• Choose “Snowflake” from the list of data sources.?
• Provide the necessary connection details:?

????? - Server: Enter the Snowflake server URL.?

???? - Warehouse: Specify the warehouse you intend to connect to.?

• Proceed to enter your credentials and establish the connection.?

With the connection to Snowflake successfully established, we now shift our focus to Dataflow within Power BI.

Why Dataflow?

Dataflow serves as a multi-purpose tool in our Power BI environment. Usually, we manage master data tables, creating a Dataflow becomes an efficient strategy to preprocess and clean this data before it hits our reports. Instead of repeatedly connecting to Snowflake, transforming, and cleaning data for each report individually, Dataflow allows us to do this once and reuse the cleaned data across multiple reports. This not only saves time but also ensures consistency in how our data is treated across different analyses.

Creating Relationships and Data Modeling

Once we have successfully connected to our data source and utilized Dataflow for initial data preparation, the next vital step in our Power BI journey is to focus on creating relationships and establishing a robust data model. This is a crucial step, especially when dealing with large datasets, where the fact table contains approximately 300 million records and many dimension tables.

Understanding the Importance of Relationships:?

In Power BI, relationships are used to connect different tables in our data model, allowing us to create meaningful insights and visuals across multiple tables. It is like creating a map that tells Power BI how data in one table is related to data in another table.

Steps to Create Relationships

1. Data Modeling: Once you have loaded your data into Power BI, go to the Data Model view. Here, you will see all your tables represented as blocks.
2. Identifying Keys: Identify the primary keys in your dimension tables and the foreign keys in your fact table. For instance, if you have a "Sales" fact table and a "Customers" dimension table, the "CustomerID" might be your key to connect these tables.
3. Creating Relationships: Drag and drop to create a relationship between the primary key in your dimension table and the foreign key in your fact table. Ensure that the relationship is correctly set up (one-to-many, many-to-one, or many-to-many) depending on your data structure.
4. Bi-directional or Single-directional: Decide on the type of relationship you need. A single-directional relationship allows filtering to flow in one direction, whereas a bi-directional relationship allows filtering to flow in both directions. Be cautious with bi-directional relationships, as they can sometimes lead to ambiguous results if not set up correctly.

Best Practices and Tips

• Avoid Unnecessary Relationships: Only create relationships that are necessary for your analysis. Having too many relationships can make your data model complex and difficult to manage.
• Use Star Schema: Aim to organize your data model in a star schema, where you have one or more fact tables connected to dimension tables, but not dimension tables directly connected to other dimension tables. This ensures better performance and simpler DAX calculations.
• Handling Large Fact Tables: Given the size of our fact table, it is crucial to be mindful of performance. Ensure that your keys are indexed and try to use integer keys instead of string keys to speed up the relationship mapping. When dealing with large numerical keys, such as those with 20 digits, it is advisable to convert them into a text data type to ensure Power BI reads them correctly. This is especially important for maintaining accuracy in your data when the keys are too large to be handled as numbers.

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Validating Relationships

Once you have created your relationships, validate them by creating a few visuals or measures. Ensure that the data is flowing correctly across tables and that your calculations are returning the expected results.?

Creating accurate and efficient relationships is a cornerstone of effective data modeling in Power BI. By following these steps and best practices, you ensure that your large dataset is well-structured, leading to faster performance and more reliable insights. With the relationships in place, you are now set up to move forward to creating visuals and measures, bringing your data analysis to life.

Conclusion

Congratulations! So far, you have learned how to structure your data in a scalable way, relying on a data model that will be a reliable base for your reports! In the next part of the tutorial, you will learn about the reporting itself.

See you very soon to complete our journey

Useful links:

https://learn.microsoft.com/en-us/power-bi/connect-data/incremental-refresh-overview Optimizing?DAX?expressions involving multiple measures - SQLBI

DAX?For Power BI: Optimizing Using Formula Engines in?DAX?Studio ( enterprisedna.co )

DAX?Query Optimization Techniques And Lessons ( enterprisedna.co )