Comparative Study of QlikView, Spotfire & Tableau – Detailed Exam Notes

Big Data and Business Intelligence: Context

  • Big Data
    • Refers to very large, highly complex collections of datasets that traditional data-processing software cannot handle efficiently.
    • Necessitates specialised tools and technologies for storage, processing, and analysis.
  • Business Intelligence (BI)
    • Broad category of technologies, applications, practices, and activities devoted to collecting, integrating, analysing, and presenting data.
    • Goal: support organisations in making better, faster, and more accurate business decisions.
    • Output usually delivered as interactive dashboards and visualisations that can be accessed by both technical and non-technical users.

Essential Features Required in BI Tools

  • Must comfortably process large volumes of data.
  • Should accept and display dynamic, real-time data streams.
  • Need rich data-visualisation options (charts, graphs, maps, etc.).
  • Require basic programming/query capabilities—e.g., SQL functions—for custom report generation.
  • Should create and maintain reliable data backups in case a live data-source connection fails.

Challenges in Adopting BI

  • Lack of a clear business strategy during BI project planning.
  • Poor or incomplete data collection methods.
  • High costs of acquisition, licensing, and operation of some BI platforms.
  • Ongoing maintenance and user-training burdens.

Advantages Delivered by BI Tools

  • Improved organisational efficiency and productivity.
  • More accurate planning, analysis, and decision-making.
  • Ability to measure key performance indicators (KPIs) effectively.
  • Simplified tracking of day-to-day business activities.
  • Faster, easier access to mission-critical information for all stakeholders.

Overview of Three Prominent BI Platforms

  • QlikView (by Qlik)
    • Integrates disparate data rapidly into a single, in-memory analytics application.
    • One consolidated dashboard provides a holistic view of operations.
  • Tableau
    • Interactive data-visualisation suite using worksheets and dashboards.
    • Non-technical users can build custom visuals with drag-and-drop ease.
  • TIBCO Spotfire
    • Combines robust visual analytics with predictive modelling.
    • Employs AI-driven data-discovery features for deeper trend insight.

Comparative Analysis of QlikView, Spotfire, and Tableau

1. Data-Source Connectivity

  • QlikView
    • Broad range of built-in connectors: Amazon Redshift/Vectorwise/EC2, Cloudera & Hortonworks Hadoop, HP Vertica, IBM Netezza, MicroStrategy, MS SQL Server, MySQL, ODBC, ParAccel, Salesforce, SAP (incl. SAP HANA), Teradata, etc.
    • Supports R integration through APIs and can connect to other Big-Data platforms.
    • MDX (Multidimensional Expressions) connectivity can pose challenges.
  • Spotfire
    • Connectivity quality rated “Excellent” with extensive native connectors.
    • Comparable breadth to Tableau (see below) and can integrate with R for advanced analytics.
  • Tableau
    • Also “Excellent” with native links to spreadsheets, CSV, SQL databases, Cloudera Hadoop, Firebird, Google Analytics, Google BigQuery, Hortonworks Hadoop, HP Vertica, MS SQL Server, MySQL, Salesforce, Teradata, etc.
    • R integration available, bolstering statistical and predictive functions.

2. Deployment Complexity

  • QlikView – Medium complexity.
  • Spotfire – Easy.
  • Tableau – Easy.

3. Usability & Interactivity

  • QlikView – Rated Excellent.
  • Spotfire – Very Good.
  • Tableau – Excellent.
  • All three provide:
    • Drill-up / drill-down navigation.
    • Multi-range filters, bookmarks, mouse-hover tooltips.
    • Strong collaboration options (sharing, commenting, web embedding).

4. Device Independence (Mobile, Tablet, Desktop)

  • QlikView – Yes (full support).
  • Spotfire – Yes to some extent (partial / device-specific limitations).
  • Tableau – Yes (responsive designs, native mobile apps).

5. Data-Transformation & ETL (Extract–Transform–Load)

  • QlikView
    • Script-based modelling and ETL built in; supports complex transformation logic.
  • Spotfire
    • Limited data-transformation functions (blending, pivoting, metadata layer) but no deep ETL for highly complex use cases.
  • Tableau
    • Similar limitations: blending, pivoting, minor cleansing; no advanced ETL like Alteryx, Datameer, or Palantir.

6. Visualisation & Reporting Flexibility

  • QlikView, Spotfire, and Tableau alike provide numerous user-configurable charts, can change visual type “on the fly,” and support parameter-driven reporting.

7. Ad-Hoc Reporting

  • All three tools allow end-users to create self-service (ad-hoc) reports through both desktop and web interfaces.

Numerical & Reference Information

  • Online ISSN of publishing journal: 2278−88082278-8808.
  • SJIF 2018 impact factor of the journal: 6.3716.371.
  • Publication issue: January–February 2019, Volume 6/496/49, pages 11645−1164811645{-}11648.

Conclusion Drawn by the Authors

  • The open-source and commercial BI landscape is rapidly evolving, especially regarding visual-appeal and data-connectivity features.
  • After reviewing QlikView, Spotfire, and Tableau along multiple dimensions (connectivity, deployment, usability, transformation, interactivity), organisations should match tool strengths to their specific data strategies, infrastructure, skillsets, and budget constraints.

Connections & Broader Implications

  • Aligns with industry shift toward self-service analytics empowering non-technical decision-makers.
  • Highlights the growing importance of real-time dashboards for competitive advantage.
  • Underlines that BI adoption is not purely technological; it requires strategic planning and data-governance frameworks to avoid common pitfalls like irrelevant data capture.

Key References (for Further Reading)

  • Dr. Sailesh S. Iyer & Dr. Kamaljit Lakhtaria – Practical evaluation of Big Data analytical tools.
  • Harshil T. Kanakia – Comparative study on BI report generation.
  • S. Vidhya et al. – Diverse Big Data analytics tools.
  • Emmanuel Ahishakiye et al. – Open-source BI in crime analytics.
  • Divyani Shrivastava et al. – Comparative study of BI tools.
  • Victor M. Parra et al. – Pentaho vs. Jaspersoft for Big Data.
  • Dr. Venkatesh Naganathan – Big Data analytics challenges and trends.
  • Capterra BI features list (web resource).