Knowledge base

Power BI Premium provides dedicated resources to run Power BI in an organization. It can be a great cost saver to try and use this dedicated capacity as efficiently as possible without paying for unneeded resources. This is where performance tracing can help us analyze system usage and help make informed decisions about scaling up or down.

As data professionals, our role is to extract insight, build AI models and present our findings to users through dashboards, API’s and reports. Although the development phase is often the most time-consuming part of a project, automating jobs and monitoring them is essential to generate value over time. At element61, we’re fond of Azure Data Factory and Airflow for this purpose.

The goal of this document is to go over the considerations and settings needed to create a successful Power BI implementation. We will be using a high-level view and focus on large corporations with many users.

At element61 we love to work with your data. Our philosophy is providing you a Modern Data Platform in which you can do real-time analytics, big data batch analytics as well as apply Machine Learning and AI.

element61 believes Delta features create a big opportunity for anyone starting with a Data Lake or already having a Data Lake.

Azure announced the rebranding of Azure Data Warehouse into Azure Synapse Analytics. Given it's always a bit unclear if a software vendor just changed the name or actually added features, we did your homework and summarized the key findings. Enjoy the read!

Power BI can be a great tool to start visualizing data and DAX is an easy step from Excel to write your own formulas and start creating measures. But once a model starts to grow and the amount of data keeps increasing, reports can become slow.  So, what can we do about it ? How can we find the cause of this decline in performance? And most importantly, how do we solve it ?  Let's take a look ...