Case studies
Built an ETL Testing Solution Empowers Leading UX Company to Streamline AI and Cloud Development
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The client is a leading Artificial Intelligence, Cloud, and User Experience (UX) Development company that provides solutions for digital transformation, data-driven decision-making, and automation to various industries.
They focus on building innovative products that help businesses leverage the power of emerging technologies to achieve operational efficiency, enhance customer experience, and drive growth.
The client approached Codvo.ai with a challenge of handling a large volume of real-time data while ensuring data quality and no loss during the customized transformation and loading to their target system. They were facing bottlenecks due to access limitations, which made it challenging to identify errors in data transformation and loading.
The client needed a solution to handle a massive volume of real-time data and ensure data quality with zero data loss during the transformation and loading process. They required a tool that could handle customized transformation and loading, given the limitations of their system. The client also faced bottlenecks due to access limitations, which made it challenging to identify errors in data transformation and loading.
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We first understood the complexity of the client's challenge and worked within their budget constraints to identify the perfect tool to handle their requirements. We then designed a customized interface using Python to download real-time data and transform it. The interface was then verified for bulk transformation and loading using Python.
We then loaded the transformed data to MongoDB Atlas and accessed it through MongoDB Compass, testing for data accuracy and integrity. This helped us identify any errors in the data transformation and loading process, allowing us to fix them before deploying the final application.
We used a combination of MongoDB, Python, Talend, Microsoft SQL Server, and Git to build the ETL testing solution for the client. MongoDB was used to store and manage the transformed data, while Python was used to build the customized interface for data transformation and loading. Talend was used to handle the data integration process, and Microsoft SQL Server was used to perform the necessary queries for data validation. Git was used to manage the version control of the codebase.
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