Data engineering model for competitor analysis

Business challenges

Inefficient processes: Traditional practices often involve manual processes that can be slow, labor-intensive, and prone to errors and inconsistencies.

Lack of scalability: Traditional practices may not be scalable, making it difficult for companies to handle increasing volumes of data and expanding business operations.

Outdated technology: Traditional practices may rely on outdated technology that can limit a company's ability to process and analyze large amounts of data in a timely and cost-effective manner.

Difficulty in adapting to change: Companies that rely on traditional practices may find it challenging to adapt to changes in the market or new technologies, potentially putting them at a disadvantage in the market.

Limited insights and decision-making: Traditional practices may not provide the level of detail and insights needed to make informed decisions, potentially leading to missed opportunities and negative business outcomes.

Compliance and data security issues: Traditional practices may not be compliant with industry standards and regulations, and may not provide adequate protection for sensitive data, putting companies at risk.

Solution overview

An automated data pipeline has been built to perform incremental uploads on a weekly basis on both development and staging servers.

The exported normalized data will be used for competitor analysis.

Data pipeline has been created to extract and upload the data to the SQL server using a package for each table data to perform incremental load.

BCP queries have been generated using ID and date fields to extract the latest data from multiple tables in different databases.

The uploaded data is normalized and loaded into the staging database.

Automated SQL jobs have been configured to export the normalized data as a pipe-delimited file and upload it to Azure blob storage for Machine learning team analysis.

Business impact

Improved efficiency and accuracy: Automated data pipelines can streamline data processing and reduce the risk of human errors, leading to more efficient and accurate data management.

Scalability: Automated data pipelines can handle large amounts of data and can be easily scaled as a company's data needs grow, allowing businesses to remain competitive.

Faster decision making: Automated data pipelines can provide real-time access to data, enabling companies to make informed decisions more quickly and respond to market changes more effectively.

Better insights: Automated data pipelines can provide access to more detailed data and insights, helping companies to make better informed decisions and identify new business opportunities.

Cost savings: Automated data pipelines can reduce the cost of manual data processing and increase the speed at which data is processed, leading to significant cost savings for companies.

Compliance: Automated data pipelines can help ensure that companies are in compliance with industry standards and regulations, reducing the risk of data breaches and other security incidents.

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