Diffrence Between Reference queries and dataflows

 As data volume continues to grow, so does the challenge of transforming that data into wellformed actionable information. We want data that's ready for analytics to populate visuals, reports, and dashboards so we can quickly turn our volumes of data into actionable insights. However, managing and preparing data for analysis can be a complex and time-consuming process. It's important to consider the best approach for your data transformations and analysis. In this video, you will explore how to reference other queries and why a data flow may be more suitable. Choosing between referencing queries and data flows depends on the specific requirements of your scenario. It's important to evaluate factors such as data volume, complexity of transformations, user expertise, and maintenance requirements to determine the best fit for your use case. There are some performance considerations you need to bear in mind with regards to reference queries especially. Reference queries can contribute to slow data refreshes due to the nature of their referencing. When a reference query is refreshed, it needs to ensure that all the referenced queries are also refreshed to maintain data consistency. This can result in longer refresh times, especially if there are multiple layers of referencing involved. Furthermore, reference queries can overburden data sources, particularly when working with large datasets. As reference queries rely on the data from other queries, they need to fetch and process the data from the original sources. This becomes more noticeable when dealing with complex transformations or frequent refreshes. To mitigate these issues, it's important to optimize the design and usage of reference queries. Consider limiting the number of reference layers and optimizing the query's transformations to reduce unnecessary data processing. Additionally, carefully manage the refresh schedule to avoid excessive load on data sources during peak usage times. By implementing these best practices, you can help minimize the impact of reference queries on data refreshes and prevent overburdening your data sources. Now, let's review data flows. Data flows offer a centralized and scalable approach for data preparation. Data flows are designed specifically for data integration and transformation tasks, providing a self service environment for business users to create and manage, extract, transform, and load processes referred to as ETL processes. With Data flows, you can connect to various data sources, perform transformations using a visual interface, and store the prepared data in the Power Bi service. Data flows are a feature available in both Power Bi desktop and Power Bi service. Data flows provide a cloud-based data preparation experience where you can build, manage, and share reusable data entities. In summary, understanding the differences and best use cases between reference queries and data flows is essential for optimizing your data processing workflows in power query. Reference queries in power query is a fundamental concept that allows you to streamline and optimize your data transformation process. By leveraging query references, you can improve reusability, efficiency, and scalability, ultimately, enhancing the overall productivity and effectiveness of your data analysis in Power Bi. Remember, practice makes perfect. Experiment with reference queries in power query to gain hands-on experience and discover the immense value it brings to your data analysis endeavors.