Databricks

Start your Databricks journey with us and set up infrastructure, development and data governance practices. Create solutions for BI reporting, ad hoc analysis and AI use cases.

We design and implement Databricks Lakehouse architectures for different use cases. We have experience in building unified data platforms and implementing distributed data mesh architectures. Would you like to discuss how you can build a data ecosystem with data products using Databricks?

Functional and well-defined data modelling practices are crucial for successful data solutions. We are experts in helping you define the approach for modelling your data solutions. Databricks recommends using the medallion architecture for layering the platform, but how should you structure and model the data inside those layers? There is no one correct solution that should be applied everywhere. Let’s design the approach together to suit your use cases.

Our MLOps approach helps you create production-grade AI solutions. We help you to identify ML/AI opportunities, prove value through production pilots and scale AI practices with our MLOps framework. Our framework helps you standardize processes for ML workflows and get the most out of your Databricks ML and AI capabilities.

• Create reusable, automated solutions with fewer errors
• Avoid manual deployments and varied development practices
• Increase time used on creating value through AI solutions and reduce technical debt

Good data governance helps you improve efficiency and data quality and reduce risks. Our experts help you define data governance practices for Databricks.

• How to govern several Databricks workspaces and use Unity Catalog for data governance
• Define development practices and standardise ways of working
• How to govern data mesh architectures with federated governance and multi-plane data platform.
• Enhance solution security and data privacy
• How to monitor and improve data quality