Data Lakehouse Implementation Services for AI-Ready, Scalable Architectures
As data grows and AI expectations rise, legacy platforms struggle. A modern data lakehouse combines warehouse reliability with lake flexibility. STX Next builds platforms that turn complexity into actionable intelligence for reliable decisions and safe AI adoption.
Data Platform that
shifts the paradigm
Focusing on metrics alone is like addressing symptoms, not causes. Instead of asking “What metrics do you want?”, we ask: “What problems are we solving?”
Our solutions connect dashboards and reports to real challenges, ensuring analytics are practical. Moving beyond vanity metrics helps teams gain actionable insights for prioritization and interventions.
Explainable, Reliable Data
A well-built lakehouse embeds lineage, data quality, and a clear semantic model directly into your architecture, ensuring business teams understand where numbers come from and why they change.
We treat validation, quality gates, and governance as core components, not afterthoughts. This removes guesswork, cuts down internal debates, and builds trust in every report and dashboard, while keeping the experience something people actually want to use.
Data-Driven, Targeted Decision Guidance
Modern analytics should drive action, not just observation. With unified data and consistent metrics, teams can move from guesswork to evidence-based decisions. Our solutions go beyond static reporting by actively signaling where attention is needed, whether that's an emerging risk or a new opportunity. The data lakehouse becomes the single source of clear, targeted guidance.
For example, instead of tracking a dozen generic KPIs, teams get a precise notification that a specific product line is underperforming and a recommendation for action that will fix it.
Cost Efficiency & Scalability
Using Snowflake and Databricks, our team can scale compute and storage to match your actual workload, whether that means handling traffic spikes, onboarding new data sources, or expanding analytics coverage, without infrastructure rebuilds.
Both platforms also ship with a broad set of ready-to-use capabilities that cut implementation time and reduce cost, getting you to production faster.
AI Readiness & Future Proofing
AI readiness starts with trusted, well-organized data: consistent definitions, clear business context, and no gaps that force workarounds. A modern lakehouse removes most common adoption blockers by design.
Built-in support for AI-driven analysis on dashboards, vector storage for RAG applications, and real-time data flows for agentic workloads means your platform can handle whatever comes next without requiring a separate infrastructure track.
That lets you introduce AI gradually, tied to actual business needs and existing processes, governed through a semantic layer, and without rebuilding your data architecture from scratch. The path to more advanced capabilities stays practical and cost-controlled.
Expertise Built On +100 Data Engineering Projects
Partnering with us, our clients have cut incident response times from days to minutes, consolidated thousands of redundant dashboards into focused reporting, and built systems that could never have run on their previous infrastructure.
USA
Real-time IoT data platform replacing legacy ETL for high-volume factory telemetry
A global chemical company needed to process roughly 100 million telemetry records per day across 11 factories, but their existing ETL tooling couldn't handle the scale or deliver timely insights. We built a streaming data pipeline on Azure Event Hub feeding directly into Azure Data Explorer, where in-stream aggregation and transformation happen at the source. Python-based microservices handle targeted data access and custom analytics, with results exposed to Power BI for live factory KPIs. The result: real-time visibility into production metrics, eliminated third-party ETL costs, and a pipeline architecture built to scale with new data sources.
Chemical Industry
Automotive Industry
Germany
14,000+ Hours Saved on Reconciliation
Following an acquisition, the client needed a single data platform to unify systems and automate financial reconciliation.
We built the platform and automated reconciliation of more than 320,000 transactions annually, with reporting handled end-to-end. The result: thousands of staff hours saved every year.
United Kingdom
Unified EdTech platform modernizing content delivery across global learning products
Macmillan needed to consolidate multiple digital learning tools into a single, maintainable platform that could scale across regions and improve user experience. STX Next provided the backend services, data pipelines, and CI/CD infrastructure underpinning the Macmillan Education Everywhere platform, alongside 30+ interactive tools. Deep integrations with Google Classroom, AWS, and Elasticsearch keep content delivery fast and consistent, while Pendo and product analytics provide ongoing visibility into platform performance.
EdTech
Data Lakehouse Implementation, Built Around Your Stack
Microsoft Fabric
Natural for Microsoft-first organizations seeking a unified, SaaS-style platform. Fast deployment, reuse of existing licenses, built-in governance via Purview, and growing AI capabilities (e.g., Copilot, OneLake integration).
Best for
Microsoft-first organizations
Key advantage
SaaS ease & Copilot
The “Catch”
Limited flexibility for high-volume streaming workloads
AWS Open Lakehouse
Best for AWS teams that want no proprietary lock-in. Apache Iceberg format, time-travel queries, schema evolution, AWS Bedrock for AI.
Best for
Avoiding lock-in
Key advantage
Apache Iceberg & Bedrock
The “Catch”
More components required
On-Premises
Best for organizations with sovereignty or regulatory constraints. Full data control, no cloud dependency, compatible with existing infrastructure.
Best for
Sovereignty or regulatory constraints
Key advantage
Full data control
The “Catch”
High complexity
Azure Databricks
Best for teams with complex data, analytics, and AI/ML workloads. Open standards, code-first data quality, full CI/CD support, built-in ML experimentation.
Best for
Complex AI/ML
Key advantage
Open standards & CI/CD
The “Catch”
Requires high engineering skills
Snowflake
Best for SQL-first organizations that want minimal operational overhead. Fully managed, cross-cloud (AWS/Azure/GCP), workload isolation, zero-copy cloning.
Best for
SQL-first teams
Key advantage
Zero-copy cloning
The “Catch”
Higher storage costs than raw cloud
Not sure which fits?
We offer a structured assessment before any implementation begins.
Tomasz Jędrośka
Head of Data Engineering
How We Work
Pragmatic, Iterative, ROI-Focused
We begin with your most important data sources and business goals, delivering a reporting-ready platform in a few months, not years.
Our delivery approach, based on Prince2 Agile, reduces complexity while ensuring every sprint produces visible, business-aligned outcomes such as curated datasets, validated models, and usable dashboards. This mitigates risk, accelerates adoption, and keeps stakeholders engaged throughout the journey.
Battle-Tested Templates, Tailored to Your Stack
STX Next’s teams accelerate delivery with proven, pre-templated lakehouse setups for AWS and Azure. Built from patterns validated across real-world scenarios, these templates make the kick-off smoother, and faster.
At the same time, our philosophy remains pragmatic and technology-agnostic: we use these templates only when they align with your ecosystem and goals.
Tech Stack
Data Platforms & Cloud Environments
Snowflake, Databricks, Microsoft Fabric (OneLake), AWS-Native Open Lakehouse
Open Table Formats
Apache Iceberg, Delta Lake
Data Modeling & Transformation
dbt, Apache Spark
Data Pipelines & Orchestration
Apache Airflow, Azure Data Factory (ADF), dltHub
Real-time & Streaming Data
Snowpipe, Amazon Kinesis, Azure EventHub, GCP Pub/Sub, Apache Kafka, OTel Collector
Data Governance, Quality and Observability
Microsoft Purview, Unity Catalog, DataHub, dbt tests, Great Expectations, Monte Carlo
Visualization & Analytics
Power BI, AWS QuickSight, Apache Superset, Grafana
ML & AI / Advanced Analytics
HuggingFace, OpenAI, vector databases
Infrastructure & Automation
Terraform, Kubernetes, n8n automations
PoCs & Micro-Offerings: Your First Step Toward Data Lakehouse
Start small enough to be wrong safely.
Start small enough to be wrong safely. Our 4–12 week micro-engagements are designed for organizations that want to validate both the solution and the way of working with STX Next before committing to a larger initiative.
Each engagement delivers practical recommendations and tangible artifacts your team can use immediately – giving you a solid foundation for long-term data decisions.
Data Lakehouse PoC
An end-to-end implementation of a lakehouse environment in your cloud, including ingestion of up to 15 entities, medallion architecture, pipelines, a semantic model, basic data validation, and up to 5 sample reports.
You receive a functional, reporting-ready foundation that can be evaluated, extended, or scaled into production.
Evaluating Data Needs & Target Lakehouse Architecture
A business-aligned blueprint of your future data platform.
Ideal for clarifying direction, reducing architectural uncertainty, and aligning stakeholders around a shared data vision.
Cloud Data Infrastructure & Warehouse Assessment
A structured review of your current setup, including a maturity score, high-level design (HLD), and recommended roadmap.
Best suited for organizations dealing with rising costs, performance challenges, or increasing architectural complexity.
Data Quality Assessment & Monitoring Implementation
Implementation of automated quality gates using dbt tests and/or Great Expectations, plus quick fixes for the most critical datasets.
This ensures your pipelines are trustworthy and reduces operational incidents caused by unreliable data.
Data Pipeline Health Check & Optimization
Identification and remediation of issues impacting pipeline performance, reliability, or maintainability.
Helpful when teams depend on manual processes, experience recurring failures, or want to streamline data delivery.
Data Governance, Lineage & Explainability Review
An assessment of your governance maturity and implementation of a lightweight governance layer covering lineage, metadata, and definitions.
Ideal for organizations facing duplicated reports, inconsistent definitions, or compliance gaps.
Every Micro-Offering Includes:
Stakeholder interviews
Documentation review
Code and infrastructure analysis
A clear HLD outlining gaps, benefits, timelines, and next steps
Optional code samples in Python and/or Terraform
Customer testimonial
We gave them a very high-level brief and left the rest in their hands. The app works perfectly, and they came in on time, on budget, with no outstanding issues. They obviously love what they do and like taking on projects that are a bit different. We definitely want to work with them on more projects going forward.
Natalie Dowling
Head of Tax Platform
at Hartford Consulting, UK
Why STX Next?
20 Years of Engineering Heritage
STX Next merges software delivery with a strategic data practice. We blend experts, governance processes, and tools. Our solutions are technically sound, maintainable, scalable, and aligned with your business.
Prime Integrator for Modern Lakehouses
We implement lakehouse architectures on Snowflake and Databricks using technologies like Apache Iceberg. The priority is selecting the right fit for your ecosystem.
Multi-source data ingestion, cleaning & wrangling
Our data ingestion connects all parts of your organization into a clean, analysis-ready foundation. We build resilient, scalable ingestion flows using cloud-native tools.
Standardized Data Modeling & Assurance Practices
A standard development framework ensures data products have semantic modeling, quality checks, documentation, and consistent metrics. This creates a trustworthy data layer for all teams.
Business-Ready AI-Powered Analytics
Combining data lakehouses with analytics creates dashboards focused on real decisions, not vanity metrics. Narrative layouts and storytelling guide action and interpretation, grounding decisions in data insight.
Business-Ready AI-Powered Analytics
Combining data lakehouses with analytics creates dashboards focused on real decisions. Narrative layouts and storytelling guide action and enhance interpretation, grounding decisions in usable data.
Reliable Processes
Our mature, battle-tested processes ensure transparency and minimize surprises, delivering complex projects more reliably than less experienced teams.
Our Technology Partners
We build on solid ground. Our partnerships mean your project runs on properly supported, enterprise-grade infrastructure.
Snowflake
Databricks
AWS
Azure
Anthropic
CloudFerro
Squirro
n8n
Let's talk
Schedule a chat with Head of Data Engineering and one of our senior engineers to discuss your data lakehouse needs.
Tomasz Jędrośka
Head of Data Engineering
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.
Data Lakehouse consulting services, built
around your stack
STX Next designs and implements data lakehouse platforms for mid-market and enterprise teams, on Snowflake, Databricks, Microsoft Fabric, and open lakehouse architectures. From a scoped PoC to a full production rollout, we build governed, AI-ready platforms without a full infrastructure rebuild. Let's talk to assess what fits your stack.
