Power BI delivers dataflows, enterprise reporting, and major updates to Power BI Desktop

As organizations embrace a data culture to drive business decisions, they need an enterprise business intelligence platform that can meet their sophisticated needs – from self-service BI to full enterprise governance, from paginated reports to full interactive data exploration, and from small data sets to petabytes of data.

In July, we laid out the roadmap for Power BI to help organizations unify modern and traditional BI on one enterprise platform, and empower business analysts by expanding self-service data prep for big data.  Since then, we’ve shipped a number of capabilities that deliver on this roadmap: Premium multi-geo allows customers to address data residency requirements, aggregations enable data analysis over petabyte sized datasets with trillions of rows of data, and the new Power BI Home landing page and dashboard commenting make it easier to get to your most important content and collaborate across the enterprise.

Today, we’re announcing the availability of several new capabilities that we laid out in our July roadmap.

Dataflows expand self-service data prep in Power BI

Power BI already includes robust self-service data preparation capabilities in Power BI Desktop through the familiar Power Query based experiences that are used by millions of users worldwide. We are excited to announce the public preview of dataflows in Power BI, taking self-service data preparation to the next level.

• Dataflows enable business analysts to create data preparation logic that can be reused across multiple Power BI reports and dashboards.
• Dataflows can be linked together to create sophisticated data transformation pipelines that enable business analysts to build on each other’s work. A new recalculation engine automatically tracks dependencies and recomputes data as new data is ingested.
• Dataflows can be configured to store the data in the customer’s Azure Data Lake Storage Gen2 instance, fueling collaboration across roles. Business analysts can seamlessly operate on data stored in Azure Data Lake Storage, taking advantage of its scale, performance, and security. Meanwhile, data engineers and data scientists can extend insights with advanced analytics and AI from complementary Azure Data Services like Azure Machine Learning, Azure Databricks, and Azure SQL Data Warehouse.
• Dataflows support the Microsoft Common Data Model, giving organizations the ability to leverage a standardized and extensible collection of data schemas (entities, attributes and relationships)
• https://powerbi.microsoft.com/en-us/