When you look at a dashboard or report, that data has likely taken many steps from its source on its way to Power BI. Data Engineering is the work that brings data from disparate sources and cleans, transforms, and organizes it to enable you to visualize your data accurately and easily.
Think of your data as raw materials and your Power BI reports and dashboards as your finished product; in this case, a house. You need processes and tools to extract the raw materials, a factory to transform those raw materials into useable components, and a warehouse to pull from when you’re ready to build.
In this analogy, your data are your raw materials. They seldom come from one source and are often found in various locations. Raw materials are usually not ready to use to build a home and need to go through some process in order to become a useable product, like a door or window.
We use Microsoft Azure tools and services to build a robust process that extracts data from their various sources as raw materials. All that data then goes into a factory to be cleaned, sorted, and organized to be ready to use.
It’s important to take the time to transform those raw materials into useable components to build your home. Just like a real factory, there are often assembly lines and manufacturing processes to ensure accuracy and quality.
Using Azure Data Factory, all your data from its various sources get shaped, validated, cleaned, and correlated, then stored in a data warehouse (Azure SQL Database) to draw from when you’re ready to produce reports and dashboards in Power BI.
Sometimes it is better to store the data in a “data lake” which is a set of files in Azure Storage, and then query it using Azure Synapse as if it is a SQL database. This makes the data lake files function as a virtual data warehouse.
To build a home, you need to make sure you have the right materials, tools, and measurements. If you don’t, you’ll end up with an unreliable, unpredictable, and unstable home.
Our goal is to build a Power BI data model with the right tables, right names, right columns, and right calculations that can be published once and refreshed regularly without any manual intervention.
Data Engineering ensures that your Power BI dashboards and reports are as accurate as possible. Taking the time to create highly organized and accurate data warehouses allows your team to discover insights like never before.
It can be daunting to try to get data from a cloud-based application on your own, but it’s what we do regularly. We’ve helped clients with data quality issues and take the time to help identify them, mitigate them, and work around them.
Imaginet has helped some of the largest companies around the globe with all of their data engineering needs.
Imaginet helped this energy safety regulator make instant and more informed business decisions from their data sprawl.
Imaginet built Power BI dashboards that provided new business insights that revolutionized this wine company’s decision making.
This governing health and safety organization improved the quality, agility, and adoption of their data with Imaginet’s guidance.
This Canadian drilling and oil company needed a better way to analyze data from multiple sources so they could focus on the highest producing regions.
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