Year: 2024
Type: article
Abstract: Businesses need to get accurate, up-to-date information from more and more transactional, market, and consumer data sources in today's fast-paced financial environment. Dynamic financial analytics data solutions need to be able to alter, grow, and work quickly. Some of the old data systems that are on-site can't do these things. Cloud-based data hubs let everyone on a team and all of their tools access the same data at the same time. This makes it easier for people to get to, share, and use information. These cloud-based apps can move and change data almost instantly with SQL pipelines. This helps banks and other financial firms get rid of data silos, make decisions faster, and process information faster. When you use SQL pipelines, it's easy to automate the steps of acquiring data, cleaning it, and adding to it. This makes sure that analytical workloads can handle the greatest traffic without getting too slow or missing out on growth opportunities. This article speaks about a good way to set up a cloud-first data analytics system for apps that handle money. Instead of one big data lakehouse, you should employ smaller SQL pipelines. This case study of a medium-sized fintech company shows that this architecture made it feasible to always keep an eye on compliance indicators, portfolio performance, and risk exposure, with query times of less than a second. What will happen next? Building infrastructure is cheaper, you can report crimes to the authorities more quickly, and you can modify your company model quickly. This paper talks about essential design ideas, operational benefits, and lessons gained from using SQL-driven pipelines and cloud data platforms in the real world for firms who want to improve their financial analytics infrastructure
Author: Karthik Allam
Institution: JPMorgan Chase & Co (United States)
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4413543576