Unifying financial data with data engineering
Business challenges
Time-consuming: Collecting and analyzing stock data from multiple sites can be a time-consuming task, hindering businesses from focusing on other critical areas.
Lack of consistency: Data from different sources can be inconsistent, making it challenging to compare and draw meaningful conclusions.
Human error: Manually collecting data increases the risk of human error, which can impact the quality and reliability of the information.
Inability to handle large amounts of data: Collecting and analyzing large amounts of data manually can be overwhelming, leading to missed opportunities and inaccurate decision making.
Lack of real-time data: Manually collecting data from multiple sources can result in a lag in receiving real-time information, affecting the ability to make timely decisions.
Dependence on manual labor: Relying on manual labor for data collection increases the risk of data loss and process disruptions.
Solution approach
We helped a finance company in gaining data-driven insights from 20 years of aggregated data from multiple sites, enhancing their decision making.
Scraped stock data from multiple sites and performed complex custom financial ETL calculations.
Merged missing data points from multiple sites and verified using a third-party website.
The extracted data is provided to the client in the form of an Excel template that contains 20 years of stock data.
Created an end-to-end solution with bot input (stock name) for crawling, ETL processing, and delivery in the customer's preferred Excel template.
Developed a framework for handling different platforms and deployed it on Airflow for automated weekly runs.
Business impact
Better trend analysis: By aggregating data over a 20-year period, data engineering models can provide a broader view of stock trends and help identify long-term trends. By aggregating data over a 20-year period, data engineering models can provide a broader view of stock trends and help identify long-term trends.
Increased competitiveness: Using a data engineering model can give stock analysts a competitive advantage by providing more accurate and actionable insights.
Increased Efficiency: Automating the data scraping process reduces manual effort and saves time, allowing stock analysts to focus on their core responsibilities.
Competitive Advantage: With a more comprehensive view of the stock market, companies with a data engineering model can gain a competitive advantage over their peers.
Better Decision Making: By having access to a large volume of accurate data, stock analysts can make more informed decisions and reduce the risk of making poor investment choices.
Better Risk Management: By being able to identify trends and patterns in stock data, the data engineering model can help finance companies manage risk more effectively.
