Data Lakehouse Consulting & Development Services
Data Lakehouse Consulting Services for AI-Ready, Scalable Architectures
STX Next builds data lakehouse platforms for mid-market and enterprise teams modernizing legacy data infrastructure, combining warehouse-grade reliability with data lake flexibility in one architecture.
Through end-to-end data lakehouse consulting services, STX Next designs and delivers pragmatic platforms that turn complex data into trusted, actionable intelligence. Tailored to your strategy and built with strong modeling, governance, and patterns that ensure reliable decision-making today and safe AI adoption tomorrow.
Data Lakehouse consulting services that shift the paradigm
Focusing on metrics alone treats symptoms, not causes. Instead of asking “What metrics do you want to see?”, we ask: “What problems are we trying to solve?”
Through our data lakehouse implementation services, we ground analytics in real-world business outcomes rather than vanity metrics. Your teams gain actionable insights that drive sharper prioritization, faster interventions, and clear business growth.
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.
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.
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 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 consulting 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.
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.
US
Research data warehouse replacing legacy analytics for global market intelligence
One of the biggest global automotive enterprises struggled to consolidate and analyze years of market research data because of a costly and inflexible legacy system. Our team built a custom data platform on Azure that automates ingestion and normalization from SPSS files and online forms, ensuring consistency across markets. At the core sits a research-oriented data platform designed for multidimensional, longitudinal analysis. Tableau and Power BI integrations deliver flexible, interactive dashboards to end users, while the underlying architecture is built to absorb future changes in source systems without a full rewrite.
Germany
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.
UK
Data Lakehouse implementation services, built around your stack
The right architecture depends on your cloud environment, team, and goals. We offer a handful of predefined approaches – and help you choose the right one.
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). Watch for: limited flexibility for high-volume streaming workloads.
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. Watch for: higher engineering skill and cost control requirements.
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. Watch for: more components to assemble and maintain.
Snowflake
Best for SQL-first organizations that want minimal operational overhead. Fully managed, cross-cloud (AWS/Azure/GCP), workload isolation, zero-copy cloning. Watch for: storage costs higher than raw cloud; advanced workflows may need external tools.
On-Premises
Best for organizations with sovereignty or regulatory constraints. Full data control, no cloud dependency, compatible with existing infrastructure. Watch for: highest implementation and maintenance complexity especially for scalable analytics and AI.
Not sure which fits?
We offer a structured assessment before any implementation begins.
How we work
Pragmatic, iterative, ROI-focused
STX Next provides data lakehouse consulting services and data lakehouse implementation services for enterprise teams on Snowflake, Databricks, and Azure. 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 business-aligned outcomes such as curated datasets, validated models, and usable dashboards. This mitigates risk, accelerates adoption, and keeps stakeholders engaged.
Tech stack
Snowflake, Databricks, Microsoft Fabric (OneLake), AWS-Native Open Lakehouse
Apache Iceberg, Delta Lake
dbt, Apache Spark
Apache Airflow, Azure Data Factory (ADF), dltHub
Snowpipe, Amazon Kinesis, Azure EventHub, GCP Pub/Sub, Apache Kafka, OTel Collector
Microsoft Purview, Unity Catalog, DataHub, dbt tests, Great Expectations, Monte Carlo
Power BI, AWS QuickSight, Apache Superset, Grafana
HuggingFace, OpenAI, vector databases
Terraform, Kubernetes, n8n automations
STX Next’s teams accelerate delivery with 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.
PoCs & Micro-Offerings: 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
Actionable HLD & roadmap
Optional code samples
Every engagement delivers practical value right out of the gate. It is a low-risk way to evaluate STX Next as a long-term partner before taking us on a full-scale project.
Let's talk
Schedule a chat with Head of Data Engineering and one of our senior engineers to discuss your data lakehouse needs.
Why STX Next
20 Years of Engineering Heritage
STX Next combines production-grade software delivery with a mature, strategic data practice. We work with mid-market and enterprise teams across data-heavy industries, including finance, manufacturing, and energy, modernizing legacy platforms into cloud-native lakehouses.
Prime Integrator for Modern Lakehouses
We design and implement lakehouse architectures on Snowflake and Databricks using open technologies like Apache Iceberg. The priority is always selecting the right fit for your specific ecosystem rather than pushing a default stack.
Multi-source data ingestion, cleaning & wrangling
Our data ingestion practice connects data from all corners of your organization, from legacy systems to event streams, into a clean, analysis-ready foundation built around your business logic. We engineer ingestion flows that are resilient, scalable, and cost-controlled, using cloud-native tooling that fits your existing stack.
Standardized Data Modeling & Assurance Practices
Using a standard development framework ensures every data product ships with semantic modeling, built-in quality checks, clear documentation, and consistent metric definitions. The result is a data layer that both technical and non-technical teams can trust and act on.
Business-Ready AI-Powered Analytics
By combining data lakehouses with intelligent analytics, from RAG extraction to predictive modeling, dashboards focus on real decisions, not vanity metrics. Story-driven, problem-focused layouts speed up interpretation and guide clear action, grounding every choice in actionable insight.
Embedded Data Catalog & Governance
Every lakehouse we deliver comes with governance built in, from data lineage and access controls to metadata and shared definitions. This foundation helps our clients make faster decisions and scale AI efficiently.
Training & Bootcamps
We offer targeted bootcamps for engineering, analytics, and business teams to accelerate adoption and build confidence. By sharing practical knowledge and driving early ownership, we shorten time-to-value and scale your internal capabilities with the platform.
What our clients say about us
Even though we believe that our work speaks for itself, we are always grateful for words of appreciation from our clients.
Client
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.
Hartford Consulting, UK
Client
testimonial
When I came to my current company, we went through an evaluation process and considered offshore providers in various countries. Ultimately, we found STX Next. The quality they offered and the fact that I had access to the teammates I had previously worked with on a different project gave me the confidence that they could execute our complex project as well. I didn’t want to risk working with unknown people, so I chose to bet on STX Next because I knew they could provide quality resources.
B Generous, US
Who we partner with
We work with leading technology providers to equip you with the most reliable solutions and ongoing support.
AWS
snowflake
databricks
dbt
Azure
cloudferro
n8n
squirro
stackit
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.
