Unlock the power of data insights
We will help you transform data from numbers into a strategic asset
THE TECH WE USE
OUR COMMITMENT
We Take Care of Your Data Baseline
Our engineers are experts in the fields of data architecture, data engineering, cloud migration, machine learning, and predictive analytics. We design optimal architecture for data-intensive applications with a strong focus on data governance in a distributed data mesh environment.
- Designing and reviewing data models, policies, rules, and standards to define the most optimal architecture of modern data-driven organizations
- Focusing on governance on how data is collected, stored and integrated, and eventually put to use in data-intensive applications
- Building modern data platforms and driving migrations from monolithic data warehouses and data lakes to a distributed data mesh environment
- Analyzing and addressing non-functional requirements like data quality, security and scalability
- Building reliable data pipelines with a strong focus on scalability, security, maintainability and automation
- Collecting and transforming data with ETL/ELT processes in batch and streaming fashion
- Providing the required infrastructure components in an automated way by applying best DevOps practices with CI/CD and IaC
- Optimizing and cleaning datasets to let data analysts and data scientists purely focus on insights to business stakeholders
- Defining cloud migration strategy and evaluating migration costs and needs to select the most suitable cloud environment
- Executing migration process with minimal disruption to normal operation, at the lowest cost, and over the shortest period of time
- Monitoring changes in the critical infrastructure to predict and reduce possible workload contentions
- Reviewing applications, databases and required components in terms of performance, availability and security
- Identifying and building an appropriate ML model to make predictions faster and more accurate
- Evaluating and tuning the model to improve the performance and select the best model for the specific problem
- Deploying the model to production reliably and efficiently with MLOps practices
- Maintaining and monitoring the model to take corrective actions in case of performance degradation
OUR PROJECTS
