Success Story – Real-Time Finance Data Feed
Case Informations
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Business Challenge
Client:
A financial research company gesturing investment options needed of real-time financial data feeds.
Challenge:
The client needed to mix social and market data to create a financial endorsement engine, which could deliver exclusive signals for organized equity investing. This had a contributed audience depending on which would send trade signals on an everyday basis. To get the solution working, the company required all possible data from the finance, which was trending on blogs, articles, news, as well a social media. This data would assist the intelligence, which a customer was creating internally to produce the final outputs on a personalized basis. Scaling was the main character in the picture as the number of resources associated was huge- having a range of 30,000+ websites. Data availability, as well as its coverage, were very important due to vitality in the financial market.
X-byte Solution
DaaS platform of X-Byte Enterprise Crawling together with its huge scale as well as the low-latency crawling offering was utilized to address the problem. We have made the system fitted into the scale and adaptively scrape sources depending on ones, which were active versus the ones, which were hidden. Alerts were given to notify regarding dead resources so that crawling results were precise and the entire system became more efficient. To address the lower latency requirements of 5 to 10 minutes, some modules were added, which might live up to this computation power. All the scraped data was indexed with hosted indexing components and search APIs were given that a client might ask every few minutes for getting the results. The final results were provided in JSON format.
X-byte Enterprise Crawling Advantages
- Our team has provided 100% API availability with constant data feeds
- X-Byte provides a dynamic listing of keywords as well as sources
- The client doesn’t need to make any data processing efforts from his end.
- Our scalable infrastructure has reduced client’s costs considerably.
- Client’s experts only concentrated on asking for final datasets as well as running analyses and this mistake was corrected by us.
