Integrating Tableau with Hadoop

Tableau, a leading Business Intelligence company, allows instant insight by transforming data into interactive visualizations called dashboards. It is a quick and easy tool for data analysis, visualization and information sharing. Tableau connects easily to nearly any data source currently available in the market, whether it be a corporate Data Warehouse, Microsoft Excel or web based data. And what’s more, Tableau can also be connected to multiple flavours of Hadoop.

In this post, I will help you understand “How we can integrate Tableau with Hadoop”. I don’t want to be technical and say that with the Apache Hive (via ODBC connection), we can integrate the Tableau software with Hadoop. To make it easier to understand, let’s first know more about the prerequisites to the integration.

Firstly, Hive, as well as one of the very well-known Hadoop vendors (Cloudera, Hortonwork, and MapR ) should be installed in the system. We can say that Tableau now supports the ability to visualize complex large data stored in Cloudera’s, Hortonworks’, and MapR’s distribution via Hive and the Cloudera, Hortonworks, and MapR Hive ODBC driver. Once Hadoop is connected with Tableau, we can bring data in-memory and do fast ad-hoc visualizations. We can even see patterns and outliers in all that data that’s stored in our Hadoop cluster. With this integration, we can’t get any value from the data unless we can see what is there inside of it.

In today’s evolving technology landscape, with the essential key being outperforming competitors, Tableau’s solution for Hadoop is one of the most elegant solutions there is. It provides the most desired performance, quickly and easily. It prevents any need for us to move huge log data into the Relational store before analysing it with Tableau and makes it more accessable. Also Tableau Software enables businesses to keep pace with the competitors through an adaptive and intuitive means of visualizing their data. Tableau lets us bring our data into its fast, in-memory analytical engine. With this approach we can query an extract of data without waiting for MapReduce queries to complete. We can just click to refresh the extract or schedule automatic refreshes.

However, we should also pay attention to technology merging effects and the fact that the Hive service does not relay information about query progress to Tableau for data accessing. It also does not provide an interface for cancelling the requested queries. Hadoop’s known drawback is its high latency. When we work with Hadoop and Tableau, we can connect live to our Hadoop cluster and then extract the data into Tableau’s fast in-memory data engine. This can be a limitation, as to get the benefit of ad hoc visualizations at interactive speeds, we need to be able to move fast.

We can conclude that Hadoop reporting is faster, easier and more efficient with Tableau. Tableau’s solution for Hadoop is elegant and performs very well. This obviates the need for us to move huge log data into a relational store before analyzing it. This makes the whole process seamless and efficient.

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Image courtesy: By David Castillo Dominici,
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