The Power BI Workflow

A typical Power BI project moves through five stages. First, connect to one or more sources using Get Data. Second, transform the raw data in Power Query — filtering rows, splitting columns, fixing data types, and merging queries. Third, model the cleaned tables by defining relationships and writing DAX measures. Fourth, visualize the model by building report pages with charts, tables, and slicers. Fifth, share the finished report by publishing it to a workspace in the Power BI Service, where scheduled refresh keeps it current.

Every action taken in the Power Query Editor — removing a column, filtering rows, splitting a delimiter, changing a type — is recorded as a discrete, reorderable Applied Step written under the hood in the M language. Steps can be renamed, reordered, or deleted individually in the Query Settings pane, making transformations auditable and repeatable on refresh. When the source supports it (most relational databases), Power BI uses query folding to push these steps back to the source as a single generated SQL statement, rather than pulling all the raw data across the network first and filtering it locally.

A well-designed Power BI model typically follows a star schema: a central fact table of transactional events (like sales orders) surrounded by dimension tables (products, customers, dates) connected through one-to-many relationships. Once relationships exist, DAX measures such as Total Revenue = SUMX(Sales, Sales[Quantity] * Sales[UnitPrice]) recalculate on the fly for whatever filter context a visual applies, in contrast to a calculated column, which is computed once per row at refresh time and stored in the model, taking up memory and never adapting to filters afterward.

Publishing a report sends the .pbix's report and dataset to a chosen workspace in the Power BI Service, where teammates with access can view, comment on, and build new reports against the shared dataset. If the underlying source is cloud-hosted, scheduled refresh can be configured directly in the Service to re-import the data automatically on a set cadence. If any source lives on-premises — a local SQL Server, a file share, an on-prem SAP system — an on-premises data gateway must be installed and configured to act as the secure bridge that lets the cloud-hosted dataset pull fresh data from inside the corporate network.