Introduction to Quotient Quble: A Financial Analysis Tool
The technological world is fast evolving, that’s why financial analysts need knowledge in Python, SQL, and Quble. Data records form the backbone of all financial analysis. Quble takes financial modeling to the next level by using a revolutionary approach to data science and finance.
Quble can be used for modification and analysis as well as for automation in the field of finance.
Quble is a data object developed by Scientific Financial to facilitate data science and financial modeling applications.
A proven and scalable enterprise technology to conduct real-world financial analysis.
Quble as a high-high-level gauge saves you that time and energy – allowing you to commit time to your tasks.
It has an easy-to-write and simple syntax which makes it highly flexible. Quble has a rich collection of built-in data types.
Quble helps data scientists create financial solutions that give accurate insight on top of market situations.
Comparison Of Quble With Python And SQL
Qubles present a revolutionary approach to data science and financial modeling. They combine the complementary power of the SQL & Python technologies to address the challenging and practical issues facing today’s data scientists.
SQL is the go-to tool for data management. It is a proven technology that can scale to support the ever-growing data needs of large enterprises. However, the SQL language is not conducive to analytic modeling.
Python offers an intuitive, modern object-oriented language. Python embodies a robust ecosystem of advanced analysis options including machine learning and cutting-edge AI techniques. However, Python does not address the extensive practical scalability, production issues, and data persistence issues present in modern, large-scale complex data environments.
With Qubles, algorithmic logic is implemented within Python code through the application of nearly 800 Python-based manipulation and operation class methods. Qubles form Python-based wrappers around underlying SQL data tables. Qubles distinguish between index keyspaces and value spaces roles for the columns of the underlying SQL data table.
Modern object-oriented language
Complex data-join operations
Large developer community
Proven, scalable enterprise technology
Auto-persistence of results
Robust, advanced ecosystem
Data integrity, production & backups
The defacto environment for modern data-science
A revolutionary approach to data-science and finance
Combine the complementary power of SQL & Python
Simplify complex data operations
Seamlessly handle distracting details for users
Automate data management and productionalization
Quble for financial analysis could be a massive advantage because of how it collaborates with SQL and Python to overcome the challenges. Other advantages include scalability, security, stability, and sustainability.
For more information on Qubles, please watch the 7-minute Introduction to Qubles video.
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