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SQL Query: 3 Methods for Calculating Cumulative SUM

SQL provides various constructs for calculating cumulative sums, offering flexibility and efficiency in data analysis. In this article, we explore three distinct SQL queries that facilitate the computation of cumulative sums. Each query leverages different SQL constructs to achieve the desired outcome, catering to diverse analytical needs and preferences. Using Window Functions (e.g., PostgreSQL, SQL Server, Oracle) SELECT id, value, SUM(value) OVER (ORDER BY id) AS cumulative_sum  FROM your_table; This query uses the SUM() window function with the OVER clause to calculate the cumulative sum of the value column ordered by the id column. Using Subqueries (e.g., MySQL, SQLite): SELECT t1.id, t1.value, SUM(t2.value) AS cumulative_sum FROM your_table t1 JOIN your_table t2 ON t1.id >= t2.id GROUP BY t1.id, t1.value ORDER BY t1.id; This query uses a self-join to calculate the cumulative sum. It joins the table with itself, matching rows where the id in the first table is greater than or

5 Top R Vs SAS Differences

Statistical analysis should know by every software engineer. R is an open source statistical programming language. SAS is licensed analysis suite for statistics. The two are very much popular in Machine learning and data analytics projects.

SAS is an Analysis-suite software and R is a programming language.

1. R Language

  1. R supports both statistical analysis and Graphics
  2. R is an open source project.
  3. R is 18th most popular Language
  4. R packages are written in C, C++, Java, Python and.Net
  5. R is popular in Machine learning, data mining and Statistical analysis projects.

a). R Advantages

  • R is flexible since a lot of packages are available.
  • R is best suited for data related projects and Machine learning.
  • Less cost since it is open source language.
  • R Studio is the best tool to develop R programming modules.
Ref: imartcus.org (read more advantages)

R vs SAS Read Today

b). R Disadvantages

  • R language architecture model is out of date. So may not use it for critical applications.
  • R is not suitable for Server programming, due to lack of security.
  • R code you cannot use in web browsers.


SAS is a statistical analysis suite. Developed to process data sets in mainframe computers. Later developed to support multi-platforms. Like Mainframe, Windows, and Linux, SAS has multiple products. SAS/ Base is very basic level. SAS is popular in data related projects.

a). SAS Advantages

  1. The data integration from any data source is faster in SAS.
  2. The licensed software suite, so you will get support from SAS organization for any issues.
  3. SAS has multiple products. Most popular in creating reports and statistical analysis.
  4. Best suited for data-oriented projects.

b). SAS Disadvantages

  1. Mining of text is hard in SAS.
  2. Graphical visualization is not present in SAS.
  3. SAS is not suitable for Machine learning projects.
  4. The SAS software is expensive.
  5. SAS studio is a useful tool to work on it.



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