SPL: A Faster Solution for Data Analysis

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Querying databases to analyze data is an integral part of every data scientist's job but SQL is super time-consuming to use📊 To tackle this problem @esProc_SPL has developed an open-source scripting language - SPL which is faster and more efficient🧵👇 blog.scudata.com/sql-is-consuming-the-lives-of-data-scientists/

Data scientists mostly work with business teams and understand problems rather than the core technical part So it is important to get the results as soon as possible with existing technologies🛠

Some of the problems typical data scientists solve by querying large databases👇 • Find out the top n customers whose sales account for 50% and 80% of the total sales based on the sales data, so as to carry out precision marketing

• Find out stocks that have experienced a rise by the daily limit for three consecutive trading days (rising rate >=10%) based on the stock trading data to construct an investment portfolio

• Determine which patients are most in need of prevention and treatment based on their medical records, diagnostic results, and treatment plans So why SPL is better than SQL?🤔🤖

Some of the drawbacks of SQL: 1) Difficult to code when encountering complex tasks 2) SQL coding is not simple 3) Difficult to debug 4) Low performance (Slow and inefficient)

Advantages of SPL for querying databases: 1) Simpler in coding (Simple structured coding process) 2) Naturally supports procedural calculation 3) Provides richer data types and computing libraries 4) Easy to edit and debug 5) High performance

Let's now look at the query example in both of these languages to how simpler SPL is than SQL Query example in SQL language👇

Writing code in SPL is simple yet efficient due to its support for computing libraries. Query example in SPL language👇

Check out their GitHub repository for more information about its various functions in detail👇 github.com/SPLWare/esProc