The Evolution of Precomputation Technology and its Role in Data Analytics

The Evolution of Precomputation Technology and its Role in Data Analytics

In this article, author Yang Li discusses the importance of precomputation techniques in databases, OLAP and data cubes, and some of the trends in using precomputation in big data analytics. If you are still wondering about it then this article is for you.

Key Takeaways

  • Precomputation is a common technique used in information retrieval and analysis, including index, materialized view, cube and more.
  • It’s a trade-off between time and space, query speed and update flexibility, online processing and offline processing.
  • A few megatrends that make precomputation essential to the big data era.
  • A real example of 200x acceleration of an OLAP query using different types of precomputation.
  • In the near future, how AI and automation will improve precomputation and how that impacts the TCO of big data systems.

Precomputation is a common technique used in information retrieval and analysis. The basic idea is to calculate and store the intermediate results ahead of time, and reuse them to speed up further inquiries.

The oldest form of precomputation, perhaps, is the multiplication table used by the Babylonians about 4000 years ago. Nowadays, children memorize multiplication tables like the one below in grade school. So we can do multiplication of small numbers in our head by simply memorizing the precomputed result instead of calculating them by ourselves.

big data data management olap data analytics ai ml & data engineering article

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