Industry: Investment Research, Investment Management, Fintech.
Location: Headquartered in Chicago, IL, the U.S.
Partnership period: 2 years and 6 months.
Team size: 15 experts.
Software product: Web Crawler, Data Collection Tool, Website Redesign.
Expertise delivered: Custom Software Development, Product Development, Designing and Implementing New Features.
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Our client is one of the premier U.S. asset management companies that employs more than 10 000 people, operates in 29 countries, and manages an investment portfolio in excess of $200 billion. In 2018, the company’s revenue exceeded $1 billion.
As one of their core activities, our client provides investors with company information, forecasts, and related proprietary analytics to enable them to evaluate the investment potential of various businesses. In order to do this, they collect financial updates en masse by using Web crawling for a wide variety of sources. This, along with Data Collection, constitutes one of the company’s multiple business lines.
At the time they approached SPD Technology for assistance, the client was looking to fulfill three mission-critical business needs:
- Perform data integration related to the Web Crawling and Data Collection business lines that the client introduced through the acquisition of other companies.
- Enhance the company’s Web Crawling and functional capabilities by developing efficient software applications that enable this.
- Redesign the company’s website to make it more user-friendly and visually appealing.
The client engaged our company to help them across the board. Therefore, we needed to deliver:
- A Web crawler application – an investment research application, capable of collecting relevant information from websites for its further processing by business analysts.
- A Data Collection application – a financial application that is capable of quickly collecting IPO-related information and analyzing this information.
- Website Redesign – a complete redesign of the client’s website, including the addition of new features to it.
We started with the data integration effort that took us nearly 1 year to complete. To proceed with the project’s implementation, we expanded our project team from the initial 6 to 15 experts.
- Web Crawler Application
To develop the Web crawling functionality, we initially employed Regular Expression (Regex) scanning for keywords based on an open-source framework and leveraging Java and Spring Boot.
However, the Regex approach proved to be inefficient as keyword patterns were found out of context. This resulted in a large amount of unusable information. Due to this, we decided to switch to Machine Learning technology, and, more specifically, to its Natural Language Processing capabilities. Doc2Vec and Word2Vec models have allowed us to identify the context for a piece of text, eliminate unwanted pieces and valueless processing, and make the resulting data more usable.
Our project team completed all the front-end tasks by using the Vue JS framework. We didn’t have any QA Engineers, so our back-end development team learned how to conduct tests, including the building of a testing strategy.
We trained the Web Crawler’s Machine Learning model on more than 1,500 entries. We also connected the solution to the AWS Cloud Computing Platform.
- Data Collection
This part of the project was handled by 10 of our project team’s members. The main technologies we used were Spring Boot and Docker. Working with AWS was a requirement, and we managed to make the best of it. We built an infrastructure, adjusted monitoring, and implemented best practices into the processes.
Implementing the project was made more challenging by a strict deadline. To meet this deadline, our experts had to put in more hours and work without days-off. We’ve managed to live up to all the highest expectations in relation to the Data Collection functionality.
Our team implemented a CI/CD with Spinnaker for deployment purposes and made it a standard across the Data Collection teams.
- Website Redesign
This part of the project was different from the other two in that it used the Staff Augmentation engagement model: 5 of our experts joined the client’s U.S-based in-house development team.
As mentioned previously, we hired additionally highly-skilled front-end developers to have a full-blown development team on our side.
The client requirement that only Vue JS and Angular be used made implementing this part of the project more difficult. Therefore, we couldn’t use our React expertise. Despite this, we completed all the tasks assigned to us in full accordance with the client’s expectations.
Our project team has used the following technology stack to implement the project:
|Data Collection Tool
|Java, Spring Boot, AWS, Crawler4J, and Vue JS
|Kanban, Lean, CI/CD, Trunk base development, Feature Toggling, Champagne Brunch, Automated Testing, DevOps, Immutable Infrastructure, and Infrastructure as a code
|AWS, Serverless, Vue JS, Nuxt, Java, Spring Boot, Vavr, Node JS, and Express
By implementing the project, we have increased the volume of the data collected by the client and improved its value. We’ve also been able to reduce the time the client’s business analysts spend on data processing.
Our Data Collection development effort wasn’t the only IPO-related project the client had. Their other development teams worked with IPOs too. However, after the solution’s launch, it became obvious that we had built the best one of all the client’s IPO-related solutions. As a result, the client decided to use our Data Collection Tool as a template for any new similar solutions that are going to be developed inside their organization.
Overall, we have built a set of unified tools that have allowed the client to optimize the company’s internal business functions, speed, and productivity.
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