How do you manage the accuracy of data migrations with a code base of 200k + lines? We made a robust set of automatic tests on the migration itself for our client that alerts us to any issues immediately and automatically. Our test suite reduced the manual testing workload and increased the accuracy of data in our databases.
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In some shape or form nearly every development project we undertake revolves around data. We depend on PostgreSQL for our database storage for most projects, and SQLAlchemy for interfacing with our Python applications.
A large client in the transportation industry needed a Business Intelligence tool that allowed them to generate reports and dive deep into the data so they could evaluate Key Performance Indicators (KPIs) and pull reports for organizational level activities. We helped theme valuate their options, ultimately settling on Pentaho, which we then tied into their existing authentication system and the data warehouse.
The trust and confidence they have in our development skills has continued to grow in the 10+ years that we have been working together.
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We worked with a large client in the transportation and logistics industry to help them design and build a data warehouse backed by a highly tested ETL framework. This prevents errors caused by out of sync data, while the automated testing helps us catch errors early in the development process and gives our client confidence that the data provided to them is accurate and up to date.
Learn MoreWith many years invested in a legacy analytics tool that was no longer supported, our client needed to find a replacement that would allow their business users to continue to analyze and evaluate Key Performance Indicators (KPIs) and to pull reports for organizational activities and data. We designed and built a data warehouse and developed numerous (ETL) processes to organize and move data from their operations database into the data warehouse. We built an ETL framework that shared libraries with the operations database to cut down on development time and prevent errors caused by the ETL data models and operations data models getting out of sync.
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