Data to Decisions

Automation, Integration, Analytics, Reports & Dashboards

A Quick Introduction to the Five Types of Filters in Flow

In this blog post, I provide an introduction to the five filter actions in Flow. Filter actions are

Overview

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.

A Quick Introduction to the Five Types of Filters in Flow

 

 

How to Import and Analyze Common Types of File Data Sources

This blog post provides a worked example of how to import and analyze Microsoft Access Data. We lear

Overview

This blog post provides a worked example of how to import and analyze Microsoft Access Data. We learn how to use the Access Database integration interface to consume the sample Northwind database into Flow. A step-by-step walkthrough is provided which details how to denormalize the various relational tables into a consolidated flattened set for analysis. We learn how to apply generic expressions to compute new data points on the fly. Finally, we learn how to leverage Flow's multidimensional analysis engine to compute hypercubes and summarize the data.

How to Import and Analyze Common Types of File Data Sources

 

 

Import and Analyze MS Access Data Using Flow Analytics

Overview This blog post provides a worked example of how to import and analyze Microsoft Access Data

Overview

This blog post provides a worked example of how to import and analyze Microsoft Access Data. We learn how to use the Access Database integration interface to consume the sample Northwind database into Flow. A step-by-step walkthrough is provided which details how to denormalize the various relational tables into a consolidated flattened set for analysis. We learn how to apply generic expressions to compute new data points on the fly. Finally, we learn how to leverage Flow's multidimensional analysis engine to compute hypercubes and summarize the data.

How to Import and Analyze MS Access Data Using Flow Analytics

 

 

Import and Analyze JSON Data with Flow Analytics

Overview In this blog post, I provide a worked example demonstrating how to import and analyze data

Overview

In this blog post, I provide a worked example demonstrating how to import and analyze data from JSON based sources. Flow allows for the consumption of JSON data into a tabular form for analysis without requiring any knowledge of structure or schema. I demonstrate how to leverage this functionality to read and flatten JSON from a web-based resource into a dataset. I then show how to apply transformations to the data by using the expression builder to calculate new data points on the fly. I show how to compute hypercubes against the flattened data and perform a simple language analysis, highlighting the ability to wrangle and analyze the data. Finally, I demonstrate how to export the transformed data to various file formats allowing us to persist the flattened set for use elsewhere.

How to Import and Analyze JSON Data with Flow Analytics

 

 

Analyze Blank or Missing Data Values Using Flow Analytics

Overveiw In this blog post, I provide a worked example demonstrating how to perform an analysis of b

Overview

In this blog post, I provide a worked example demonstrating how to perform an analysis of blanks on a target dataset. When analyzing data a typical first step is to get an understanding of where there are missing values. Identifying where there are missing values in your data can help you make more informed decisions about your analysis approach.

How to Analyze Blank or Missing Data Values Using Flow Analytics

 

 

How to Denormalize (Join) Data Using Flow Analytics

Overview This blog post demonstrates how to configure the denormalize function in order to join disc

Overview

This blog post demonstrates how to configure the denormalize function in order to join disconnected data sets together. A worked example is provided which shows how to import and merge various delimited files. The denormalize action is used to join the data from the separate files together in order to consolidate them into a single set for analysis. Once the data is joined, we learn how to use hypercubes to aggregate and summarize the data.

How to Denormalize (Join) Data Using Flow Analytics