Cloud Strategy & Consulting
Modern Data Lakehouse That Fits Your Business Landscape
When it comes to data, compromise between security and usability is not an option. A modern data lakehouse brings together the reliability of a data warehouse with the flexibility of a data lake. It enables a single, scalable architecture that handles structured, semi-structured, and unstructured data in one, unified system. Does this sound like something you need?
STX Next: Engineering Excellence, Applied to Your Data
Two decades of building production-grade software has taught us that data lakehouses are where engineering discipline matters most. As a prime integrator for Snowflake and Databricks – with deep expertise in Apache Iceberg for maximum deployment flexibility – we identify which architectural decisions age well and which ones create technical debt. Our goal is to deploy a platform your team actually wants to use, producing numbers they trust and a system they can scale independently.
A Unified Data Platform Merging Data Lake Flexibility with Data Warehouse Performance
Limiting your thinking about data to metrics alone is like focusing on symptoms rather than causes. Therefore, instead of asking you “What metrics do you want to see?”, we ask a much more impactful question: “What problems are we trying to solve?”
A Data Lakehouse grounds dashboards and reports in real-world challenges and business outcomes, ensuring analytics remain focused and practical. By moving beyond vanity metrics, your teams gain insights that drive action – from sharper prioritization and faster interventions to a clear understanding of what moves the business forward.
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.
PoCs & Micro-Offerings: Your First Step Toward Data Lakehouse
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
Code and infrastructure analysis
A clear HLD outlining gaps, benefits, timelines, and next steps
Optional code samples in Python and/or Terraform
Documentation review
Each engagement delivers immediate, actionable value–even before a full-scale project begins – while giving you a low-risk way to validate STX Next as your long-term partner.
Expertise Built On +100 Data Engineering Projects
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
Why STX Next
20 Years of Engineering Heritage
STX Next combines production-grade software delivery with a mature, strategic data practice. Our approach blends cross-domain experts, with proven governance processes, and powerful tooling. Every solution we deliver is not only technically sound but also maintainable, scalable, and aligned with your business reality.
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 across the platform 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-based extraction to predictive modeling – dashboards are built around real decisions rather than vanity metrics. Narrative-driven layouts and problem-oriented storytelling guide action and accelerate interpretation, grounding every decision in usable data insight."
Embedded Data Catalog & Governance
Governance is built into every lakehouse we deliver, not bolted on afterward, covering lineage, metadata, access controls, and shared definitions as standard. Our clients consistently point to this as what makes both decision-making and AI adoption much more efficient.
Training & Bootcamps
To accelerate adoption and build internal confidence, we offer dedicated bootcamps for engineering, analytics, and business teams. These programs transfer practical knowledge, demystify the platform, and ensure teams feel ownership of the solution from day one. This shortens time-to-value and helps organizations grow their competencies in parallel 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 leaders across the financial ecosystem to modernize core technology and accelerate digital transformation.
AWS
snowflake
Azure
cloudferro
n8n
squirro
stackit
FAQ
How is a data lakehouse different from a data warehouse or data lake?
A traditional data warehouse is optimized for structured reporting and BI, while a data lake provides flexible storage but often lacks governance and performance optimization.
A lakehouse architecture merges both approaches:
- Warehouse-grade performance and reliability
- Data lake scalability and flexibility
- Support for BI, analytics, machine learning, and near real-time processing
- One unified data platform for business and technical users
Why should we invest in a unified data platform?
A unified data platform eliminates silos between analytics, reporting, ML, and operational data. With consistent metrics, standardized data modeling, and built-in governance, teams can move from fragmented reporting to evidence-based decision-making.
Your organization benefits from:
- A single source of truth
- Consistent semantic models
- Faster time-to-insight
- Reduced operational overhead
How does a data lakehouse improve data quality and governance?
A properly implemented lakehouse embeds data governance, lineage, and data quality monitoring directly into the platform.
This includes:
- Automated data quality tests (e.g., dbt tests, Great Expectations)
- Clear semantic modeling and documentation
- Automated lineage tracking
- Metadata management and shared business glossaries
- Access controls and explainability layers
Can a data lakehouse support AI and machine learning?
Absolutely. A lakehouse is a strong foundation for AI readiness because it ensures data is clean, well-modeled, and governed.
It enables:
- AI-driven analytics on top of trusted dashboards
- Vector-enabled storage for RAG-style applications
- Predictive modeling and advanced analytics
- Real-time data flows for intelligent automation
This allows organizations to introduce AI gradually – without re-architecting their entire data landscape.
What are common use cases for a data lakehouse?
The lakehouse becomes a central data platform for analytics, reporting, ML, and decision intelligence.
Typical use cases include:
- Unifying ERP, CRM, SaaS, and file-based data into a single reporting platform
- Real-time event ingestion and monitoring
- Sales and financial analytics
- Marketing attribution and customer journey analytics
- Fraud detection and anomaly detection
- IoT data ingestion and operational optimization
Is a data lakehouse cost-effective?
Yes. Platforms like Snowflake and Databricks allow independent scaling of compute and storage, ensuring you pay only for what you use.
This elasticity:
- Reduces infrastructure waste
- Handles traffic spikes automatically
- Avoids costly architectural rebuilds
- Accelerates time-to-value with built-in services
When properly designed, a lakehouse improves both performance and cost efficiency.
