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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

4 Top Data Mining Tools

Many data mining tools present out of those listed here top free tools useful for development.

data mining tools

4 Top Data Mining Tools

1. Rapid Miner (erstwhile YALE)

This is very popular since it is a ready-made, open-source, no-coding-required software, which gives advanced analytics. 

Written in Java, it incorporates multifaceted data mining functions such as data preprocessing, visualization, predictive analysis, and can be easily integrated with WEKA and R-tool to directly give models from scripts written in the former two.


This is a JAVA based customization tool, which is free to use. It includes visualization and predictive analysis and modeling techniques, clustering, association, regression, and classification.

3. R-Programming Tool

This is written in C and FORTRAN and allows the data miners to write scripts just like a programming language/platform. Hence, it is used to make statistical and analytical software for data mining. It supports graphical analysis, both linear and nonlinear modeling, classification, clustering, and time-based data analysis.

4. Python-based Orange and NTLK

Python is very popular due to its ease of use and its powerful features. There is an option available New fresh best Daily Python tips to your Inbox to learn more. 

Orange is an open-source tool that is written in Python with useful data analytics, text analysis, and machine-learning features embedded in a visual programming interface. NTLK, also composed in 

Python is a powerful language processing data mining tool, which consists of data mining, machine learning, and data scraping features that can easily be built up for customized needs.

5. Knime

Primarily used for data preprocessing – i.e. data extraction, transformation, and loading. This is also a part of data science and The 4 Most Asked Skills for Data Science Engineers really help to take the next step to learn more about data science. Knime is a powerful tool with a GUI that shows the network of data nodes. 

Popular amongst financial data analysts, it has modular data pipelining, leveraging machine learning, and data mining concepts liberally for building business intelligence reports.


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