There are many threads over the internet expressing different views and experience supporting each of the tools (Tableau vs QlikView) and criticizing the other one.
After doing several Qlikview and Tableau project, I have listed down some of the key difference between these two tools which I discovered under my understanding. Below are the listed differences/comparison. It would be nice of you to share your thought on same to enrich comparison sheet. This can help us in making choice between the two under different requirements and budgets for proposal and expressing our point of view for client prospects.
Special thanks to Sushil353 who has helped and provided expert comments in making this documents.
Those who desire strong visualization capabilities with BI Reporting.
Those who want to build real time visualizations on the fly, with little technical expertise required.
More commonly used as departmental vs. enterprise wide BI Solution
Commonly used for dash boarding purpose and rapid visualization.
Strong in financial services, insurance OEM version available for embedding in third party apps.
Server and online editions allow visualizations to be shared.
Combines visualization with in-memory BI tool and ETL engine
Dashboard simple to build, even for novice.
Easy to manipulate data from multiple sources on the fly.
Best for rapidly visualizing a single data set.
Single product for complete BI Solution
Dependent on ETL and DWH
Basic charts and visualization objects.
Charts are easy to build and has more visualization options
Gauge charts are available
No Gauge chart but bullet chart is easy to build
Powerful Straight and pivot table available
Not good for Tabular visualization. Seeks workaround for meeting complex requirement.
Longer learning curve
Easy to learn this tool
Maps needs workaround
Excellent in Maps
In-memory BI platform provides much faster analysis than traditional OLAP
Partial In-memory, Data extract and live connection fetch query result from hard disk/server.
Built-in ETL engine to transform disparate data sets into a common data structure.
In case data is residing in DB then view's has to be created by IT people.
Associative inference engine for uncovering associations in data sets.
Blending data from different sources can be difficult as no ETL to normalize data
Trigger and Actions are very powerful
Actions are available but has limited scope.
Easy to maintain objects and dashboard components.
Everything has to be developed as a sheet. For bigger application and more no of charts. Handling multiple sheets are not easy.
Global filters and global search is very powerful
Difficult to Implement global filter and no search option available
Extension objects are available for extra ordinary visualization
No support for such objects or charts.
Integrates with a very broad range of data sources including Amazon Vectorewise, EC2, and Redshift, Cloudera Hadoop and Impala, CSV, DatStax, Epicor Scala, EMC Green Plum, Hortonworks Hadoop, HP Vertica, IBM DB2, IBM Netezza, InforLawson, Informatica Powercenter, MicroStrategy, MS SQL Server, My SQL, ODBC, PAr Accel, Sage500, Salesforce, SAP, SAP Hana, Teradata and many more.
Integrates with a broad range of data sources including spreadsheets, CSV, SQL databases, Salesforce, Cloudera Hadoop, Firebird, Google Analytics, Google BigQuery, Hortonworks Hadoop, HP Vertica, MS SQL Server, MySQL, OData, Oracle, Pivotal Greenplum, PostgreSQL, Salesforce, Teradata and Windows Azure Marketplacea and many more.
Visualization speed depends on server RAM only.
Visualization speed depends on Source/DB speed and RAM of Machine