Dynamic pursuit and data visualization app
Visualization of huge volumes of full scale financial data
Our engineers have an economic and investment research tool that will be used for visualizing the data and analyzing the financial data for economic and investment purposes.
The Business Challenge
The client required to assemble a powerful investment and economic tool with easy to understand and adaptable UI, fast data search, and data visualization system of immense volumes of full scale and miniaturized scale financial pointers and other information in diagrams, maps, tables and charts. The tool was required to give access to 4 million data series amassed from 1.5K data sources and put away in one spot.
The data series needed to remember the latest and verifiable information for macroeconomics, ventures and financial markets of 128 rising, boondocks and created economies.
The client required the users of the stage to have access to trustful data as well as make their own insights (workbooks) picturing the chosen data they have to help with their work.
The created tool was to supplant the current stage, improving its client experience and guaranteeing its high performance. The client likewise mentioned the tool to empower users to work together over made insights.
Brisk Logic’s product engineers were tested to make a simple-to-utilize stage highlighted by enhanced performance, improved information revelation and perception.
To guarantee the accomplishment of the created product Brisk Logic team included not just experienced engineers grasping the most forefront advancements and QA engineers who dealt with the product quality. Gifted budgetary experts of Brisk Logic were likewise included.
They needed to team both physically and naturally every one of those tremendous volumes of data series. Furthermore, they helped the development team in search optimization, which made possible the display of applicable data and as-you-type recommendations.
To provide high up-time different Amazon web services were used such as E2 and Spot Instances, S3, LoadBalancer, CloudFront, VPC, etc. Node.js was used as the main app server. Intermediate data caching was implemented with MongoDB.
Redis was applied as the innovation to process lines of information. Python was utilized to empower download to Excel and different configurations of reports.
Solr empowered powerful search capacities of the stage on MySQL and MongoDB levels. Phantom.js was applied to create thumbnails and to download pictures of representation parts. HighCharts assisted with building outlines and charts in the application.
Gulp and Webpack were used to assemble the application and for the deployment process. SASS helped to create the beautiful frontend of the application. WebSockets were used to enable the collaborative work of multiple users. Microservices was selected as the main architecture pattern.
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