Cube Optimization Journal: Introduction

I have been working on cube optimization techniques for some time now. And honestly I even got my job for the same requirement. Its actually interesting to work with team of highly qualified MSBI professionals and learn new things on the same field, you were very confident about.

I am starting this as a journal and hoping to cover more and more on the topic gradually. In this way we can achieve a more strong foundation on this topic.

In this post we will cover the aspects where Cube optimization can be applicable/tested actually.

Following are areas we think of when we talk about cube optimization:

  1. Decreased usual size of cube: we often come in situation where cube is created and being used but it takes a lot of space on server. Cube optimization is mostly focused on decreasing the cube size which can lead to more efficiency in different other areas as well (listed below).
  2. Decreased processing time of cube: this one is a catch and we always want to achieve. Decrease in processing time can also impact in limiting space of cube, good reporting time, more frequency of data processing. Mostly due to longer time period of processing we decrease frequency of processing and users see a great latency in the report data (talking about MOLAP).
  3. Fast Reporting: Optimizing MDXs only in SSRS reports cannot give you fast report, it must come from real source “the cube”. And reporting like excel are only as fast as a cube is. We need faster reports for that we need a faster cube.
  4. “Can you think of any more valid reason anymore?”

Well we can talk about many other less important aspects like Loads on servers, better architecture blah blah . . .but what I believe is in project whats most important aspect is “Client” nothing else is significant more.

Client doesn’t care about populous server till he gets the result and damn if he care about your architecture if he doesn’t get the result he expected. Harsh but true :P.

So I hope using most important aspects for cube optimization. I will start with one of them to cover simple and effective optimization techniques.


Posted on October 21, 2016, in Begin BI, MDX and tagged , , , , , , . Bookmark the permalink. 2 Comments.

  1. here we consider the

    Horia Orasanu

  2. here we consider the


    Horia Orasanu

    . Course unit content: This course covers Lagrangian and Hamiltonian Mechanics. Topics include main principles of Newton mechanics and , Kepler Problem, Scattering Theory, motion of many body systems, variations, d’Alembert’s and Hamilton’s Principles, generalized coordinates, Lagrange’s Equation, Conservation laws associated with spatial and time properties, theory of linear oscillations, normal modes, solid body dynamics, Hamiltonian Equation, Poisson Brackets and canonical transformations

    1. Introduction.
    Generally, there are two procedures used for solving variational problems that have constraints. These are the methods of direct substitution and Lagrange multipliers. In the method of direct substitution, the constraint equation is substituted into the integrand; and the problem is converted into an unconstrained problem as was done in Chapter II. In the method of Lagrange multipliers, the Lagrangian function is formed; and the unconstrained problem is solved using the appropriate forms of the Euler or Euler-Poisson equation. However, in some cases the Lagrange multiplier is a function of the independent variables and is not a constant

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