![]() In Alteryx, you build a flow, usually starting with one or more Input Data tools to bring in data from a source such as a text file or database and then, using various other tools like joins, formulas, unions, and filters, you can shape the data with a lot of flexibility. Alteryx Data TipsĪlteryx offers a slightly different paradigm for bringing various data sources together for analysis. It takes the concepts that are already built into Tableau Desktop to a whole new level to make data preparation and shaping even easier and more robust. And with various other data prep tools such as pivot, merge and join calculations, you have tremendous power to shape the data in just the right way for your analysis.Īlso, be on the lookout for a new product from Tableau - code-named “Maestro” -which was demoed at Tableau Conference 2016. No problem! Tableau will let you bring together all of these and more. Do you have customer data stored in a couple of SQL Server and Oracle databases? Do you have supplemental data in Excel and a Google Sheets document that the team updates every day? Tableau allows you to join tables from various data sources together to create a single data source that will meet your analytical needs. You can get very creative and do things like change the level of aggregation on the fly via parameter or blend between different time periods for period-over-period comparisons.ģ. Since data blending happens at the same time as data visualization, it’s less of a data preparation step and more of a “real-time” experience with the data. ![]() Does one of your data sets contain a record for every customer for every day and another contain monthly goals? If you joined those two sets you might end up with a lot of duplication of monthly values, but when you use data blending, your monthly values come through perfectly. This can improve performance and give you quite a bit of flexibility with a data model. Instead of joining them together, use Tableau to blend the data at an aggregate level. This can be very useful in an enterprise data warehouse where you have large fact tables. Use data from two or more massive data sets without having to join them at a row-by-row level.This innovative approach was introduced way back in Tableau 6 and has been improved since.ĭata blending in Tableau gives you the power to do amazing visual analytics with disparate data and offers unique benefits: Unlike joining, which is done row-by-row, data blending is performed at an aggregate level. In Tableau, “data blending” is a technical term used to describe using two separate data sources in a single visualization. 2. Different data sources blended together. The users will be happy to see financial data from your general ledger system and customer data from your CRM in a single dashboard, your boss will be impressed with how fast you built it, and you’ll smile to yourself as you think about how easy it was to create each connection and bring it all together with a few clicks. Action filters and cross-database filters allow you to create seamless interactivity for the end user. Different data sources visualized in a single dashboard.Ī Tableau workbook can have as many connections to as many different data sources as you’d like and you can use all of them to create individual visualizations that can be combined seamlessly on a dashboard. ![]() Tableau has three primary ways of bringing disparate data sources together for analysis: 1. Each new release introduces new features that make life easier for a data analyst. ![]() Tableau continues to innovate in data preparation, blending and integration. Understanding these differences allows you to leverage the power of each tool. ![]() In fact, I’ve recently enjoyed some interaction with the data community around different approaches to shaping data in each tool.īoth Tableau and Alteryx are unique in their approach to data prep, blending and integration. Both tools allow you to combine data from various sources into meaningful structures for deeper analysis. I also have been growing in my knowledge and appreciation for the capabilities of Alteryx. I love Tableau and use it on a daily basis to answer data questions and tell data stories. So how do you bring this data together for analysis? How do you compare values across different sources? Occasionally, there’s a JSON or XML feed with valuable data that needs to be captured. Sometimes there are directories full of PDFs or text files. It might be various on-premise databases with some cloud data sources and an extraneous Excel document or two. Many times I work with clients that have two, three or even a dozen different sources of data. In my day-to-day role as a data consultant, I have never run across a situation where a single data source answered every question. ![]()
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