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What our clients say
We're dedicated to delivering powerful solutions that help our customers achieve their goals. As their partners and journey companions, we value their feedback. Read what they have to say about our collaboration.
Hogarth Worldwide
From the very start of the data catalog piece they built for us, they were thinking about how to track adoption metrics for this tool when it gets released into the world. We have a set of metrics for that particular tool that we can see the uptake in our user base. I can only characterize it as a success.
Man Group
GS1
They challenged the status quo, were insightful, and ultimately the best people I could have chosen to do a part of my project that I did not have the skills for. Not only this, but they pushed our project forward with their thoughtful questions, which in turn made my entire idea better and made the value for money much more than I could ever have hoped.
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Speed up work on your software projects and outpace the competition.
FAQ
Who do you build data lakehouse platforms for?
We work with mid-market and enterprise teams in data-heavy industries, technology, financial services, manufacturing, retail, and insurance among them, who are modernizing legacy data infrastructure or building a cloud-native platform from the ground up. Most of our clients come to us with fragmented systems, unreliable pipelines, or a growing need to support AI use cases without rebuilding everything from scratch.
How is a data lakehouse different from a data warehouse or data lake?
A data warehouse is built for structured reporting and BI. A data lake offers flexible storage but often lacks governance. A lakehouse merges both: warehouse-grade reliability and performance with data lake flexibility, in one platform that supports BI, analytics, ML, and near real-time processing. We design this architecture around your existing stack, on Snowflake, Databricks, or Microsoft Fabric, so you get one governed platform instead of stitching two systems together.
What does the data lakehouse implementation process look like with STX Next?
We start with your most important data sources and business goals, not a full-scope rebuild, and deliver a reporting-ready platform in months rather than years. Our delivery approach, based on Prince2 Agile, breaks the work into sprints that each produce a usable outcome: curated datasets, validated models, or working dashboards, so you see progress and can adjust course early rather than waiting for a single large delivery at the end.
How long does a data lakehouse implementation take?
It depends on scope, but most engagements start smaller than people expect. Our Data Lakehouse PoC runs 4 to 12 weeks and covers ingestion of up to 15 entities, a medallion architecture, pipelines, a semantic model, and sample reports, enough to evaluate the approach before committing to a full build. A production-scale implementation typically follows in phases after that, sized to your data sources and team capacity.
What does a data lakehouse implementation cost, and how is it scoped?
Cost depends on data volume, number of sources, and how much governance and AI-readiness work is involved. We scope it through a structured assessment first, a maturity review, high-level design, and roadmap, rather than quoting a number before understanding your environment. If you're not ready to commit to a full implementation, our micro-engagements (4 to 12 weeks) let you validate scope and cost with a working PoC first.
Can a data lakehouse support AI and machine learning?
Yes. A lakehouse is a strong foundation for AI readiness because the data is clean, modeled, and governed by design. Our implementations include vector-enabled storage for RAG applications and real-time data flows for AI-driven analytics, so you can introduce AI gradually without re-architecting the platform later.
How do you reduce risk on a data lakehouse migration?
The biggest risks in a lakehouse migration are usually scope creep, poor data quality carried over from the old system, and stakeholders losing confidence before they see results. We manage this by starting with your highest-priority data sources rather than a full-platform cutover, building automated data quality checks in from the start (dbt tests, Great Expectations), and structuring delivery in sprints so business teams see working outputs early instead of waiting months for a single go-live.
What are common use cases for a data lakehouse, and why not just keep separate systems?
Fragmented analytics, reporting, and ML systems create silos, inconsistent metrics, and duplicated work. A unified lakehouse gives you one source of truth for ERP, CRM, SaaS, and file-based data, supports real-time event monitoring, and consolidates financial, marketing, and fraud-detection analytics into one platform. We've built this for clients ranging from real-time factory telemetry (100 million records a day) to multi-market research data consolidation, see our case studies above.
Is a data lakehouse cost-effective to run?
Yes, when the architecture fits your actual workload. Snowflake and Databricks scale compute and storage independently, so you pay for what you use rather than fixed capacity, and both ship with ready-to-use capabilities that cut implementation time. The bigger cost risk isn't the platform, it's over-scoping the initial build, which is why we recommend starting with a PoC or assessment before committing to full implementation.
