Tuesday, September 9, 2014

Data Visualization and Data Cubes - by Andrei Pandre

Data Visualization stands on the shoulders of the giants  – previously tried and true technologies like Columnar Databases, in-memory Data Engines and multi-dimensional Data Cubes (known also as OLAP Cubes).

OLAP (online analytical processing) cube on one hand extends a 2-dimensional array (spreadsheet table or array of facts/measures and keys/pointers to dictionaries) to a multidimensional DataCube, and on other hand DataCube is using datawarehouse schemas like Star Schema or Snowflake Schema.

The OLAP cube consists of facts, also called measures, categorized by dimensions (it can be much more than 3 Dimensions; dimensions referred from Fact Table by “foreign keys”). Measures are derived from the records in the Fact Table and Dimensions are derived from the dimension tables, where each column represents one attribute (also called dictionary; dimension can have many attributes). Such multidimensional DataCube organization is close to a Columnar DB data structures. One of the most popular usage of datacubes is a visualization of them in form of Pivot tables, where attributes used as rows, columns and filters while values in cells are appropriate aggregates (SUM, AVG, MAX, MIN, etc.) of  measures.

OLAP operations are foundation for most UI and functionality used by Data Visualization tools. The DV user (sometimes called analyst) navigates through the DataCube and its DataViews for a particular subset of the data, changing the data’s orientations and defining analytical calculations. The user-initiated process of navigating by calling for page displays interactively, through the specification of slices via rotations and drill down/up is sometimes called “slice and dice”. Common operations include slice and dice, drill down, roll up, and pivot:


A slice is a subset of a multi-dimensional array corresponding to a single value for one or more members of the dimensions not in the subset.


The dice operation is a slice on more than two dimensions of a data cube (or more than two consecutive slices).

Drill Down/Up:

Drilling down or up is a specific analytical technique whereby the user navigates among levels of data ranging from the most summarized (up) to the most detailed (down).


(Aggregate, Consolidate) A roll-up involves computing all of the data relationships for one or more dimensions. To do this, a computational relationship or formula might be defined.


This operation is also called rotate operation. It rotates the data in order to provide an alternative presentation of data – the report or page display takes a different dimensional orientation.

OLAP Servers with most marketshare are: SSAS (Microsoft SQL Server Analytical Services), Intelligence Server (Microstrategy), Essbase (Oracle also has so called Oracle Database OLAP Option), SAS OLAP Server, NetWeaver Business Warehouse (SAP BW), TM1 (IBM Cognos), Jedox-Palo (I cannot recommend it) etc.

Microsoft had (and still has) the best IDE to create OLAP Cubes (it is a slightly redressed version of Visual Studio 2008, known as BIDS – Business Intelligence Development Studio usually delivered as part of SQL Server 2008) but Microsoft failed (for more than 2  years) to update it for Visual Studio 2010 (update is coming together with SQL Server 2012). So people forced to keep using BIDS 2008 or use some tricks with Visual Studio 2010.

Original Source : Apandre Wordpress


  1. How about we get the gathering of a couple of information driven investigation that standard: data science course in pune

  2. Such a very useful Blog. Very interesting to read this article. I have learn some new information.thanks for sharing. know more about

  3. Awesome blog. I enjoyed reading your articles. This is truly a great read for me. I have bookmarked it and I am looking forward to reading new articles. Keep up the good work!

    Invisalign specialist

  4. The information provided on the site is informative. Looking forward more such blogs. Thanks for sharing .
    Artificial Inteligence course in Patna
    AI Course in Patna

  5. I have to search sites with relevant information ,This is a
    wonderful blog,These type of blog keeps the users interest in
    the website, i am impressed. thank you.
    machine learning course in hyderabad


  6. Great information!! Thanks for sharing nice blog.
    Data Science Course in Hyderabad

  7. PMP Certification Pune
    Great tips and very easy to understand. This will definitely be very useful for me when I get a chance to start my blog.Great post i must say and thanks for the information. Education is definitely a sticky subject. However, is still among the leading topics of our time. I appreciate

  8. Attend The Data Analytics Courses From ExcelR. Practical Data Analytics Courses Sessions With Assured Placement Support From Experienced Faculty. ExcelR Offers The Data Analytics Courses.
    Data Analytics Courses

  9. I wanted to leave a little comment to support you and wish you a good continuation. Wishing you the best of luck for all your blogging efforts.
    a href="https://www.excelr.com/data-analytics-certification-training-course-in-pune/"> Data Analytics Course in Pune/">It is perfect time to make some plans for the future and it is time to be happy. I've read this post and if I could I desire to suggest you some interesting things or suggestions. Perhaps you could write next articles referring to this article. I want to read more things about it!

  10. Awesome blog. I enjoyed reading your articles. This is truly a great read for me. I have bookmarked it and I am looking forward to reading new articles. Keep up the good work! data scientist courses

  11. This is my first visit to your web journal! We are a group of volunteers and new activities in the same specialty. Website gave us helpful data to work. On Premise CRM

  12. Are you still unsuccessful in your search for the top online data science courses? Several platforms provide data science courses, but it's crucial to focus on those that meet your requirements and allow for domain specialisation. A few training opportunities in data science are included below for those who are just entering the profession.data science course training in faridabad

  13. Python is a programming language that enables quicker work and more efficient system integration. Because of its simplicity, ease of use, and accessibility to libraries like NumPy, Pandas, and Matplotlib, it is one of the best languages used by data scientists for a variety of data science applications. You will gain a multidisciplinary skill set and be prepared to work with massive volumes of data to find insights and solutions to business challenges after completing this data science certification course. Having this certification will provide you a technical understanding of computer science and statistics in addition to a postgraduate degree. It covers the spring framework, hibernate, and advanced java.data science course training in faridabad

  14. Interesting read, Andrei! Your observations on the relationship between Data Visualization and multi-dimensional Data Cubes are fascinating. Indeed, the "slice and dice" characteristics of OLAP enable analysts to gain deeper insights.

    Data Analytics Courses in India

  15. Good reading! Your discoveries regarding the connection between multi-dimensional data cubes and data visualisation are remarkable. Indeed, OLAP's "slice and dice" features let analysts get more in-depth insights.
    Data Analytics Courses in Agra

  16. This post delves into the intricate world of data visualization and data cubes. It provides valuable insights into the foundation of data analytics and the tools that drive it. A must-read for data enthusiasts!
    Is iim skills fake?

  17. A good read! It's amazing what you've dicovered about the relationship between multi-dimensional data cubes and data visualisation.
    Data Analytics Courses in Agra

  18. thanku for sharing this blog. I found this blog very interesting and helpful
    Data Analytics Courses in Trivandrum


Share Your Inspiration...