It’s often true that “a picture is worth a thousand words” but sometimes a picture can be such an effectively communication medium that no more words are necessary. Such is the case with this year’s Gartner Magic Quadrant rating of leading Business Intelligence vendors. Gartner is the undisputed authority on IT industry ratings. In years past, Microsoft was often seen as the “underdog”, the “challenger”, the “up-and-comer” and the less expensive consolation for customers who couldn’t afford the best solutions. No more…Just look at the top-most rating in the leaders/able to execute quadrant. Yea, that’s right. We bad!
How Lakehouse Architecture is Revolutionizing Business Intelligence
The Lakehouse is the evolution of the earlier cloud data platform in many pieces that came with “some assembly required”. All of the components are modern, mature and capable but complicated and require specialized skills. Imagine that the new version is easier to assemble with instructions that are only a few pages with stick figures, and it comes with an Allen wrench.
We’re seeing consulting customers putting Lakehouse and BI solutions on-line in just a few weeks. Then they iterate to scale-up their modern data warehouse/BI platform as they train their Center of Excellence champions and Data Governance organizations as they progress.
DevOps & CI/CD for Power BI: Real-World Example
In a recent blog post, Paul discussed that DevOps, and specifically CI/CD, principles are essential for modern software development, and Power BI is no exception. Power BI CI/CD for enterprise class projects can be achieved using Azure DevOps, with steps including source control, automated testing, and build and release pipelines.
Getting Data Into Shape for Reporting with Power BI
Even for small, informal BI projects, shaping the data into a dimensional model alleviates complexity, speeds up slow calculations and reduces the data model storage size. I conclude this post by reviewing seven data architectures and the data shaping methods with different degrees of scale.
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I’m engaged in an on-site Oracle Exadata POC for data warehousing. This is obviously the exception, but apparently it does happen.