If you’re struggling to figure out which of the data integration and transformation tools in the Microsoft Power BI platform is the right one for your organization, we can help. Our comparison chart of five Microsoft options summarizes cost, ease of use, cloud/on-prem, product maturity, feature set, use cases and more.
Organizing data to work for your reporting needs
Power BI composite models is a game changer, fundamentally changing how organizations will merge self-service and corporate business intelligence. Authors can bring in their own local data sources while still leveraging the authoritative Power BI or Azure analysis services datasets created by others in their organization.
Learn the basics, including the software and plotting libraries
Choosing the right tool for the job
Demo: Senturus Analytics Connector
Source and prepare your data for Power BI
Enable data transformations and enterprise sharing of datasets
Flawed data and bad decisions result from poor data models
Overview and mechanics of data analysis expressions
Get an easily digestible synopsis of Power BI dataflows and Azure Data Factory, two more popular cloud-based options from Microsoft for enterprise data integration, data prep and data transformation. We provide information for each including the typical audience, the editor used, methods for preparing and transforming data, how the technology works and pricing.
Power BI allows for a number of different, but complementary, ways to organize and model self-service data. Two of these mechanisms are called Dataflows and Datasets.
Power BI offers many ways to pull in data for analysis. But what approach should your organization take? The answer depends on considerations that include whether or not your organization has an existing data warehouse, your need for real-time dashboarding and the ease-of-use factor for your analysts. We explore four options, outlining the pros and cons of each.
