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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

Data mining Real life Examples

Data mining is a process to understand about unused data and to get insights from the data. You need a quick tutorial and examples to perfect with this process. The best example is the Backup data business use case to mine the data for useful information.

The backup data is simply wasted unless a restore is required. It should be leveraged for other, more important things. This method is called Data Mining Technique.

For example, can you tell me how many instances of any single file is being stored across your organization? Probably not. 

But if it’s being backed up to a single-instance repository, the repository stores a single copy of that file object, and the index in the repository has the links and metadata about where the file came from and how many redundant copies exist.
Data mining Real life Examples
By simply providing a search function into the repository, you would instantly be able to find out how many duplicate copies exist for every file you are backing up, and where they are coming from.

Knowing this information would give you a good idea of where to go to delete stale or useless data. 

The complete knowledge of Data mining is a plus point to start further on this. 

After all, the best way to solve the data sprawl issue in the first place is to delete any data that is either duplicate or not otherwise needed or valuable.

 Knowing what data is a good candidate to delete has always been the problem.

Data Mining vs Data Science

There may be an opportunity to leverage those backups for some useful information. When you combine disk-based backup with data deduplication, the result is a single instance of all the valuable data in the organization. This is the best data for data mining.
With the right tools, the backup management team could analyze all kinds of useful information for the benefit of the organization and the business value would be compelling since the data is already there, and the storage has already been purchased. 

The recent move away from tape backup to disk-based deduplication solutions for backup makes all this possible.

data mining vs data science

Being able to visualize the data from the backups would provide some unique insights. As an example, using the free WinDirStat tool.

A best use case is, I noticed I am backing up multiple copies of my archived Outlook file, which in my case is more than 14GB in size. If you have an organization of hundreds or thousands of people similar to me, that adds up fast.

Top Questions ask Yourself if the Data Mining tool is needed
  • Are you absolutely sure you are not storing and backing up anyone’s MP3 files?
  • How about system backups?
  • Do any of your backups contain unneeded swap files?
  • How about stale log dumps from the database administrator (DBA) community? 
  • What about any useless TempDB data from the Oracle guys? 
  • Are you spending money on other solutions to find this information? 
  • Are you purchasing expensive tools for email compliance or audits?

Advantages of Data mining
  1. The backup data could become a useful source for data mining, compliance and data archiving or data backup,
  2. Also, bring efficiency into data storage and data movement across the entire organization.


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