In this blog post, I provide an introduction to the five filter actions in Flow. Filter actions are functions which select a specific subset of records from a designated data collection based on some target match criteria. This blog post introduces the different types of filters and provides a comprehensive worked video example demonstrating how to configure and implement these filters against a sample data collection. The filter actions are some of the most elementary and integral operations in the Flow computing framework. Mastering the different types of filters is key to data processing, data analytics, and business intelligence workflow design.
In this blog post, I demonstrate how to build a hypercube-based autonomous BI dashboard. I explain the current-state landscape of BI reporting and data analytics technologies. I provide details on the current limitations of existing BI approaches to automated reporting. I then define the characteristics required for a next-generation BI analytics and reporting framework capable of meeting the current and emerging reporting requirements that most businesses face. I provide a worked example demonstrating how to develop a solution which answers these emerging challenges. In the worked example, I show how to compute hypercubes from raw data and use those hypercubes as the basis for n-dimensional drill through dashboards. I explore various transformations and aggregation techniques across hypercubes to demonstrate how to summarize data across multiple dimensions. I show the power of Flow's multidimensional visualization and pivot engine by creating visualizations which allow for 5+ levels of drill-down. I finish the example by designing an interactive dashboard and showing how to distribute the completed report across an organization. Finally, I cover how to deploy the developed workflow to Flow's agent framework to continuously and autonomously execute our reporting tasks on a schedule.