G2

Helen G.

Business Partnership Manager

Mid-Market (51-1000 emp.)

8/19/2026

"High-Ownership Data Engineers Who Streamlined Our Pipelines"

5/5

What do you like best about Uvik Software?

What we like most is the level of ownership their Data Engineers take.

They did not behave like external contractors waiting for detailed specifications. They worked directly with our software engineers and AI specialists, challenged inefficient parts of the existing architecture, and proposed reusable solutions that could support multiple projects.

Their engineers were particularly strong in Python, PySpark, Azure Databricks, Delta Lake, data integration, and production data pipeline development. One of the most valuable improvements was reducing processing time for a major recurring dataset by about 70%.

They integrated into our existing engineering processes very naturally and required very little management overhead. Review collected by and hosted on G2.com.

What do you dislike about Uvik Software?

There was nothing significant that negatively affected the engagement. Review collected by and hosted on G2.com.

Recommendations to others considering Uvik Software:

I would recommend Uvik Software to companies that need experienced Data Engineers who can work as an extension of an existing engineering team rather than as a separate outsourced unit.

They are particularly well suited for projects involving Python, Azure Databricks, PySpark, Delta Lake, cloud data platforms, complex integrations, analytics infrastructure, and data preparation for AI or ML applications.

My recommendation would be to involve their engineers early in the architecture stage. In our case, their input went beyond implementation and helped us avoid duplicated data-processing logic and build a more reusable platform. Review collected by and hosted on G2.com.

What problems is Uvik Software solving and how is that benefiting you?

Uvik Software helped us solve the problem of fragmented data across multiple databases, APIs, and client systems. Before the engagement, our software and AI teams spent significant time collecting, cleaning, and restructuring data separately for each project.

Their Data Engineers helped us build a centralized data platform using Azure Data Lake, Azure Databricks, Delta Lake, Python, and PySpark. They automated ingestion, transformation, validation, and monitoring across more than 25 data sources.

As a result, onboarding a new data source decreased from around 6 days to less than 2 days, data preparation effort for analytics and AI projects was reduced by approximately 60%, and preparation of production-ready datasets for new AI use cases decreased from roughly 2 weeks to 3–4 days. The platform now processes more than 150 million records per day and gives our development teams much faster access to standardized and validated data. Review collected by and hosted on G2.com.