What is Data Blending in Tableau?

What is Data Blending in Tableau?

by Manish Singh

Every day, around 2.5 quintillion data bytes are generated with an estimated maximum efficiency of about 1.7 MB by 2020. Given this, companies must find innovative processing methods to combine data to reach optimum efficiency. Tableau data blending is one such way of merging data.

Tableau data blending is currently highly demanded by companies. Tableau is growing its product portfolio, including Tableau Creator, Viewer, and Explorer, to better leverage this need. Explore the training course and the Tableau certification to get a detailed understanding and live up to practical knowledge.

Let’s cover an intriguing and practical function from Tableau: data blending. In this tutorial, learn about the fundamentals of Tableau data blending.

What is Data blending?

Data blending in Tableau combines multiple data sources in the same view by locating standard variables. Unlike an ordinary join connecting the lowest granularity data sources before any aggregation is made, you can make a data blend between data sources after aggregating the individual resources. In the end, a combined number of records is limited and computational efficiency is maximized.

The mechanics of blending data can be challenging to understand even for an experienced SQL query writer when initially displayed.

Types of Tableau data blending

As stated below, there are two forms of tableau data blending:

  1. Automatically defined relationship: The automatic data blending is defined in Tableau as a relation that works best only if the field we work on consists of the same field name for both data sources.
  2. Manual Tableau data blending: The method of data blending is employed when a more complex combination of comparative budgetary data from the tablets and data from a database is necessary.

The goals of Data blending

  • Data from numerous sources show “deeper intelligence” within your information.
  • To provide reliable, practical information in corporate analysts’ hands on a timely basis.
  • To improve decision-making skills by senior management within the firm.

Essential data blending situations are sales/marketing, financial operations, and site/marketing operations. In today’s environment, the adoption and integration of technologies that allow users to visually mash their data to have a competitive edge in responding to Big Data flows are crucial for such business sectors.

Why data blending in Tableau?

Suppose you are a Tableau Developer who has Salesforce’s transaction data and Access’s quotas stored. The Data you would like to integrate is held in many databases. The granularity of the data collected in each table varies between the two source data.

How to create Data Blending?

First, you need to understand primary and secondary data sources for data blending in Tableau. Data blending involves a primary source of data and at least one secondary source of data. It acts as the main table or source of data if you choose a primary data source.

Subsequent data sources you use on the sheet can be viewed as a secondary source of data. In this view, the primary data source only shows the columns of the secondary data source that have equivalent matches. In the same case, transactional data can be identified as the significant data source and the quota data as the secondary data source.

Primary and secondary Tableau data sources

You first need to know primary and secondary data sources to understand and carry out data blending in Tableau. The primary data source for data blending is the main table, while the secondary source is the supplementary table.

At least one secondary data source should be available to perform blending in a tableau sheet. The primary Data on which Tableau defines graphs and charts are the data from its primary data source. Nevertheless, only those data values related to or match primary data source values are obtained from the secondary data source, leaving everything else in the source.

The final graph after data blending cannot reveal data relating to the south and the east since it is not in the primary data source. So, based on the fields and data you want to display, always select your primary and secondary data sources properly. By using its fields in a diagram, you can designate a data source as the primary.

When to blend your data in Tableau?

In the following scenarios, data blending in Tableau is especially effective;

  1. If you cannot utilize cross-databases, you can join a database that does not support them, such as Oracle Essbase and Google Analytics. In such circumstances, in Tableau, you can import or connect to distinct sources and then integrate them with blending data. It allows you to use a mixture of data on a single tableau worksheet of several data sources.
  2. When employing larger data sets, data blending is the best option to go with. Instead of using joins, you can blend the data, as Joins first combine the data and then add it to the view that affects the performance when the database is vast. Instead, when we blend data, the Data is first aggregated, then when required combined. In the case of big data sets, it saves much computational power.

Final words

Are you enthusiastic about data blending? Are you excited by a career in data blending? As a data scientist or ETL developer, you can start your career and take a further jump towards data visualization with a few years’ expertise.

As one of the best business intelligence tools, data blending in Tableau is quite popular with companies. Tableau has expanded its product portfolio to further capitalize on this demand. These are designed to make it easier to measure and customize Tableau’s capabilities and receive favorable customer feedback. The success of BI initiatives depends on an organization’s selection of BI solutions and the supplier capability, together with supporting maintenance and follow-up, to offer reasonably speedy and effective installations.

Most data analysts are switching to business analytics tools today for more flexible solutions. However, it is the prime condition to ensure improved company insight to choose the proper BI tool for your firm. Select the one that best suits your firm, whether big or small, with a quick selection of BI tools.

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