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Data Quality

Testing is key to check whether the project matches expected requirements and to ensure that there are no defects. The purpose of testing is to pinpoint errors or any missing requirements.

Proper testing ensures reliability, security and high performance which further results in time saving, cost effectiveness and customer satisfaction. Testing any IT project in the early stages helps you save time and money: if issues are identified early, they cost less to fix.

Performance testing

Performance testing is the practice of determining how a particular set-up will perform under a particular workload. The main goal is to improve speed, scalability, stability and reliability.

An automated testing approach is much more time-efficient and cost-effective while allowing for a timely detection of poor quality of the data warehouse. The Robot Framework also facilitates automatically repeated tests and ensures that data delivery defects are avoided in future output. As a result, companies can rely on trustworthy reports for their decision-making processes.

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Analytics Engineering
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Check whether the project matches expected requirements and ensure that there are no defects. Pinpoint errors or any missing requirements.
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MLOps
Manage the entire ML lifecycle, including data preparation, model training, deployment, monitoring, and maintenance with MLOps.
Academy
Intensive training for both our and your employees to ensure they master the necessary skills and methodologies.