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Solvios is the leading IT company in the business to give you growth, revenue, and a global platform.
We're a US-based AI-integrated data warehouse services company helping startups, scale-ups and enterprises design, build and modernise their data infrastructure cloud data warehousing, data engineering pipelines, lakehouse architecture, business intelligence and real-time analytics delivered by senior data engineers and analytics architects who understand that a data warehouse isn't a destination, it's the foundation every AI model, every BI dashboard and every operational decision your business makes will run on for the next decade.
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Whether you're building your first cloud data warehouse, migrating off a legacy on-premise data platform, designing a lakehouse architecture that serves both your BI dashboards and your ML pipelines or operationalising real-time analytics across a distributed data estate, our data warehouse services cover every layer of the modern data stack. Data warehousing and data analytics delivered by engineers who treat your data infrastructure as the strategic asset it actually is not a reporting afterthought bolted onto the operational systems that run your business.
A cloud data warehouse is only as valuable as the data model, the pipeline architecture and the governance framework built on top of it. Standing up a Snowflake account or a BigQuery dataset is the easy part. Building the warehouse that your analytics team can actually query at speed, your data engineers can maintain without a three-day on-call rotation and your compliance team can audit without reconstructing six months of data lineage. That's the engineering work. We implement cloud data warehouses across Snowflake, Google BigQuery, Amazon Redshift and Azure Synapse Analytics with dimensional data models designed for the query patterns your business actually runs, ingestion pipelines built for the data volumes and latency requirements your use cases demand and the governance, access control and cost management discipline that keeps your data warehouse from becoming the most expensive spreadsheet your company ever built.
The on-premise data warehouse that made sense in 2012 Oracle, Teradata, SQL Server SSAS, legacy Netezza is now the thing costing your business in licensing, infrastructure maintenance, query performance limitations and the inability to feed the ML pipelines and real-time analytics systems your product and operations teams are waiting for. We modernise and migrate legacy data warehouses to modern cloud platforms with the schema conversion discipline that preserves the business logic buried in ten years of transformation code, the data validation rigour that confirms row counts, aggregation accuracy and referential integrity at every migration milestone and the parallel-run strategy that lets the business keep reporting from the old platform while the new one earns its trust. No big-bang cutovers that leave the analytics team unable to produce the Monday morning board report.
A data lake without a governance layer is a data swamp. A data warehouse without the flexibility to serve unstructured data, ML feature stores and exploratory analytics is an expensive constraint. The lakehouse architecture Delta Lake, Apache Iceberg, Apache Hudi on top of cloud object storage gives you the schema flexibility and raw data retention of a lake with the ACID transaction guarantees, query performance and governance controls of a warehouse. We design and build lakehouse architectures on Databricks, AWS Glue with S3, Azure Data Lake Storage and Google Cloud Storage that serve structured reporting, ML training pipelines and ad-hoc data science workloads from a single, well-governed data platform without the data duplication and consistency problems that plague organisations running a separate warehouse and lake in parallel.
A BI dashboard that nobody trusts is worse than no dashboard at all it just moves the spreadsheet argument to a different meeting room. We build business intelligence and reporting solutions that earn trust: semantic layers designed so business users ask questions against business concepts rather than database table names, Tableau, Power BI, Looker and Qlik implementations built on well-modelled data that makes metrics consistent across every report, every team and every executive presentation and the data governance discipline that means when the CFO's revenue number and the VP of Sales's revenue number are different, the answer isn't a three-hour Slack thread. Performance-tuned reports and dashboards that load in seconds on the data volumes your business actually generates, not the sample dataset from the BI tool's demo environment.
The analytics bottleneck in most organisations isn't a shortage of data. It's that every question the business wants answered has to go through a data team backlog that's already six weeks deep. Self-serve analytics solves that but only when the underlying data model is clean enough and the tooling is governed enough that business users can actually trust what they're building. We design and implement self-serve analytics environments where business analysts and operational teams can answer their own questions without waiting for engineering: semantic layers through dbt metrics, LookML or Tableau's data model that abstract the complexity of the underlying warehouse, curated data marts with the dimensions and measures business users actually need and the training and governance that stops self-serve from becoming the source of competing metric definitions that nobody can reconcile.
Batch analytics tells you what happened yesterday. Real-time analytics tells you what's happening now and for fraud detection, operational monitoring, personalisation engines, IoT data platforms and the growing category of AI applications that need current data rather than last night's snapshot, the latency difference is the difference between a system that works and one that doesn't. We design and build real-time analytics architectures using Apache Kafka, Apache Flink, AWS Kinesis, Google Pub/Sub and Azure Event Hubs streaming ingestion pipelines that move data from source to queryable in seconds, stream processing logic that enriches, aggregates and transforms data in motion and the operational monitoring that keeps real-time pipelines running reliably when the event volume spikes and the downstream systems depend on the data being there.
Most data platforms are built for the BI dashboards of the last decade. We build data infrastructure for the AI systems of the next one. Every layer below uses AI to automate, optimise and surface intelligence from your data estate because a modern data warehouse isn't just where your reports live, it's where your AI models train, your predictions get served and your business decisions get made.
ML-driven anomaly detection in data pipelines, automated schema drift detection and handling, intelligent retry logic and self-healing data flows that keep your warehouse current even when upstream sources change without warning.
Highlights:
● Pipeline anomaly detection
● Automated schema drift handling
● Self-healing data flows
ML-driven anomaly detection in data pipelines, automated schema drift detection and handling, intelligent retry logic and self-healing data flows that keep your warehouse current even when upstream sources change without warning.
Highlights:
● Pipeline anomaly detection
● Automated schema drift handling
● Self-healing data flows
Data warehousing and analytics isn't one capability. It's a discipline that spans data modelling, pipeline engineering, governance, quality management, semantic layer design, visualisation and the AI integration layer that separates a data platform that compounds in value from one that becomes a maintenance burden. Here are the capabilities we deliver and the engineering bar we hold ourselves to on each one.
Data Modelling & Architecture Design
Dimensional modelling for analytical workloads star schema, snowflake schema and the wide table patterns that columnar engines like BigQuery and Snowflake reward. Data vault modelling for enterprise environments where auditability and historical tracking are non-negotiable. OBT and denormalised models where query performance on large datasets matters more than storage efficiency. The data modelling decisions documented with the reasoning your team will need when they're maintaining the warehouse two years from now.
ETL & ELT Pipeline Engineering
Data pipelines built for the data volumes, latency requirements and transformation complexity your use cases actually generate not the pipeline that fits the demo. dbt for transformation logic that lives in version control and gets tested like application code. Apache Airflow, Prefect and Dagster for orchestration with the observability and retry logic that production pipelines demand. Fivetran, Airbyte and custom connectors for ingestion from the source systems your data estate actually runs on. Pipelines designed for the failure modes that happen in production, not just the happy path.
Data Governance & Cataloguing
Data without governance is a liability. Column-level lineage from source to dashboard, data cataloguing through Apache Atlas, Alation or Collibra, access control policies that enforce data classification at the warehouse layer, PII detection and masking for regulated data environments and the data ownership model that makes every metric traceable to a documented, trusted source. The governance architecture that makes a data warehouse a trusted system rather than a source of competing answers.
Data Observability & Monitoring
Data pipelines fail silently more often than noisily. Row count anomalies, schema changes, distribution drift, freshness violations and referential integrity failures that only surface when a business user notices the dashboard numbers look wrong on a Thursday afternoon. We implement data observability through Monte Carlo, Great Expectations, dbt tests and custom monitoring that catches data quality issues at the pipeline layer with the alerting, on-call routing and runbook discipline that means a data incident gets resolved before it reaches the boardroom.
Cloud Cost Optimisation for Data Platforms
Snowflake credits, BigQuery slot costs and Redshift node hours accumulate fast when data platforms are built without cost engineering discipline. We implement warehouse cost controls through query optimisation, materialisation strategy, auto-suspension policies, warehouse sizing matched to actual workload profiles, partition pruning discipline and the FinOps monitoring that ties data platform spend to the business value it's generating so the data team can defend the infrastructure budget without pulling the query log manually every month.
Semantic Layer & Metrics Management
The metric definition problem where Finance's revenue number and Sales's revenue number are both technically correct but fundamentally different is a semantic layer problem, not a data quality problem. We build semantic layers through dbt metrics, Looker LookML, Tableau data models and custom metric stores that give your organisation a single, versioned, governed definition of every business metric. The layer between your warehouse and your dashboards that makes 'what does this number mean?' a question with a documented answer rather than a Slack debate.
Data Security & Compliance Engineering
HIPAA, PCI-DSS, GDPR, SOC 2 and industry-specific data compliance requirements engineered into the warehouse architecture from the first data model decision not retrofitted after the auditor asks. Column-level encryption, dynamic data masking, row-level security policies, audit logging of data access and the compliance evidence collection makes a data platform audit a reporting exercise rather than a six-week reconstruction project.
BI Tool Implementation & Optimisation
Tableau, Power BI, Looker, Qlik and Metabase implemented against well-modelled warehouse data with the performance optimisation that makes dashboards load in seconds rather than the thirty-second spin that trains executives to distrust the data. Extract optimisation, aggregation strategy, incremental refresh configuration and the BI governance that means report proliferation doesn't silently create the same metric definition problem the semantic layer was built to solve.
We're not just another data warehouse service provider. We're the AI-integrated data engineering partner you bring in when pipeline reliability, warehouse performance, dashboard trust and AI-readiness aren't acceptable to leave unsolved and when the current data platform has become the thing your data science team is working around rather than working with.
ML feature stores, AI-powered data quality, natural language analytics interfaces and predictive metric surfaces are built into how we design data platforms not added as a separate AI layer after the warehouse architecture is already locked.
You work directly with engineers and architects who've designed production data platforms at real scale petabyte-scale Snowflake implementations, real-time Kafka-to-BigQuery pipelines, enterprise semantic layers and the dbt codebase migrations that expose every undocumented transformation assumption. Not analysts learning SQL window functions on your data estate.
Ingestion, transformation, modelling, governance, quality, semantic layer and visualisation delivered by a single team that holds the full data stack rather than a warehouse vendor's professional services team that only knows their own platform.
Snowflake, BigQuery, Redshift, Azure Synapse, Databricks deep on all of them. The platform recommendation you get is based on your data volumes, your query patterns, your team's capability and your cost profile, not on which vendor we happen to be certified to resell.
HIPAA, PCI-DSS, GDPR and SOC 2 compliance controls are engineered into the data architecture from the first model design not reviewed against a compliance checklist six weeks before the audit. Your data team and your compliance team both sign off during the build.
We architect for ML feature stores, real-time streaming, lakehouse patterns, vector databases and the data infrastructure that modern AI systems actually require, not the batch ETL architecture that was best practice when your last data warehouse was built.
Most data platform projects don't fail because the engineers couldn't write the pipelines. They fail because the data model was never designed for the query patterns the business actually runs, the governance layer was never built so the warehouse accumulated untrusted metrics and the AI and ML requirements were treated as future-phase concerns rather than day-one architectural constraints. Our AI-integrated data warehouse services follow a structured delivery methodology that treats data infrastructure as the strategic platform it is.
Here's exactly how it works.
We map your current data landscape: source systems and their data quality characteristics, existing pipeline architecture and its failure modes, current warehouse or data platform structure, BI tool usage and the metric definition inconsistencies that are already causing trust problems. For greenfield implementations, we define the business questions the platform needs to answer, the data volumes and latency requirements that shape the architecture and the AI and ML use cases that need to be designed in from the start.
We design the target data architecture: warehouse platform selection, data model design, ingestion and transformation strategy, semantic layer approach, governance framework, cost management architecture and the AI integration points that make the platform AI-ready from the first pipeline run. Trade-offs documented with the reasoning your data team will need when they're operating the platform two years from now.
We validate the critical architectural assumptions on a contained data domain before committing to the full build the Snowflake cluster configuration that needs to hold under your actual query concurrency, the dbt transformation architecture that needs to survive your source system's schema volatility, the semantic layer that needs to produce the CFO's revenue number correctly before the full dashboard migration begins.
We build ingestion pipelines, transformation logic, data models and governance controls in phased delivery domain by domain, with data quality validation, lineage documentation and business sign-off at each milestone. Historical data migration executed with the row-level validation that confirms your new platform produces the same answers as your old one before the old one gets decommissioned.
We build the semantic layer, the BI dashboards and the self-serve analytics environment on top of the validated data model with the performance optimisation that makes dashboards load at the speed executives expect, the metric documentation that makes every KPI traceable to a trusted data source and the user enablement that makes self-serve analytics genuinely self-serve rather than a new route to the data engineering backlog.
We operate the data platform after go-live pipeline monitoring, data quality alerting, warehouse cost management, dbt model performance reviews, BI dashboard optimisation and the quarterly data architecture reviews that surface the next improvements before the platform starts accumulating the drift that turns a great data warehouse into the next legacy system that needs replacing.
Data platform engagements range from a focused migration sprint to a multi-year data engineering programme and the engagement model needs to fit the scope, not the other way around. The right structure depends on your data estate's complexity, your team's current capability and how defined the scope is on day one. Three models, all built for the engineering quality and delivery transparency that production data infrastructure demands.
You're running a multi-quarter data platform build or modernisation, scaling a data engineering practice across multiple business units, building the data infrastructure your AI and ML initiatives depend on or augmenting your in-house data team without the cost and lead time of permanent headcount.
Hand-picked data engineers, analytics engineers, a BI developer and a data architect working only on your platform. Sprint planning, data model reviews and pipeline delivery run on your calendar and your tooling. AI-integrated data engineering is built into how the team delivers.
Monthly retainer. No surprise invoices, no scope-creep billing. Team composition flexes as your data roadmap evolves.
2-6 engineers
6-month minimum
Scales with 30-day notice
Most organisations start with one and evolve into another as their data platform matures. Let's figure out the right starting point together.
Building AI-Integrated Data Foundations for the Businesses That Will Define the Next Decade. The organisations investing in production-grade, AI-integrated data infrastructure now won't be the ones explaining why the board report numbers don't match, why the ML models can't get clean training data or why the data warehouse migration is in its fourth year.
Data platform requirements look different across industries: the compliance posture, the data sensitivity, the latency requirements, the regulatory audit trail and the AI use cases that the data platform needs to support all change the architecture. These are the verticals where we've built production data platforms and understand what real data operations actually demand.
Honest answers to the questions every CDO, VP of Data, head of analytics and CTO asks before they engage a data warehouse service provider. If something isn't covered here, our data architects will walk you through it on a discovery call, no sales pitch, no fluff.
Look beyond the platform certifications and the dashboard screenshots. The right data warehouse service provider asks more questions than it answers in the first conversation about your current data quality, your source system landscape, your business's analytical requirements and what decisions your organisation is failing to make well because the data isn't trustworthy, fast enough or accessible enough. Evaluate on data modelling depth in the discovery phase, transparency about data quality problems they find, platform-agnostic thinking and whether they push back when the brief implies an architecture that won't scale to the AI use cases you'll have in eighteen months. Anyone who recommends Snowflake before they've understood your query patterns isn't the right partner.
End-to-end data warehouse services cover data strategy and architecture design, cloud data warehouse platform selection and implementation, data modelling, ETL and ELT pipeline development, data lake and lakehouse architecture, data governance and cataloguing, data quality management, semantic layer design, BI tool implementation and optimisation, real-time streaming analytics, ML feature store design, data security and compliance engineering and ongoing platform operations and optimisation. The best engagements start with a data estate audit before any architecture is proposed because the data quality and source system characteristics discovered in that audit determine what architecture is actually appropriate.
A focused data engagement single domain warehouse implementation, BI tool migration or pipeline build for a defined source set: $20,000-$80,000. A mid-complexity data platform build with multiple source systems, custom data modelling and BI implementation: $80,000-$300,000. An enterprise data platform programme with real-time streaming, ML feature store, governance framework and multi-domain coverage: $300,000-$1M+. Ongoing managed data platform services typically run $8,000-$40,000 per month. We provide detailed estimates after a data estate discovery. We won't quote before we understand what we're actually building.
A focused implementation of single domain warehouse build or BI migration for a defined data model: 6-12 weeks. A mid-complexity data platform with multiple source systems, data modelling and semantic layer: 3-6 months. A full enterprise data platform programme with real-time streaming, ML integration and governance framework: 6-18 months, delivered in phases. We provide milestone-based timelines before any implementation begins.
Snowflake for organisations that need separation of compute and storage with per-second billing, excellent multi-cloud portability and the ecosystem maturity that enterprise data teams value. BigQuery for Google Cloud-native organisations and product teams with large-scale analytics workloads where serverless, usage-based pricing and the tight Vertex AI integration make GCP the natural data platform home. Redshift for AWS-native organisations with existing investment in the AWS ecosystem where Redshift Serverless and the Redshift ML integration with SageMaker align with the broader infrastructure strategy. Azure Synapse for Microsoft-centric enterprises where tight Power BI integration, Azure AD governance and Fabric's unified analytics platform are the deciding factors. We make the recommendation after understanding your query patterns, your team's capability and your cloud infrastructure context not before.
A data warehouse stores structured, processed data optimized for analytical queries fast, governed and trusted, but limited to structured data and requires transformation before ingestion. A data lake stores raw data in any format at low cost, flexible and comprehensive, but without governance and query performance it becomes an ungovernable data swamp. A lakehouse combines both: raw data retained in cloud object storage with a metadata and transaction layer (Delta Lake, Iceberg, Hudi) that adds ACID transactions, schema enforcement and query performance on top serving BI, ML and exploratory analytics from a single platform without duplicating data across a separate warehouse and lake. Most modern data architectures are moving toward lakehouse patterns. The right approach for your organisation depends on your data variety, your ML requirements and your existing investment.
dbt (data build tool) is the transformation layer that brings software engineering discipline to SQL-based data transformation version control, testing, documentation and modular, reusable transformation logic that replaces the stored procedures and undocumented SQL scripts that make most legacy data warehouses unmaintainable. With dbt, every transformation is documented, tested, version-controlled and traceable to the business logic it implements. Data quality tests run automatically on every pipeline execution. Lineage from source to dashboard is generated automatically. For any organisation serious about building a trustworthy, maintainable data platform, dbt is the standard for transformation engineering. We implement dbt on every warehouse engagement where transformation complexity justifies it which is most of them.
Data governance is engineered into the architecture from the first data model decision not retrofitted after the compliance team asks. Column-level lineage tracked from source to dashboard, data cataloguing through Apache Atlas, Alation or the platform-native metadata service, access control policies enforced at the warehouse layer with row-level and column-level security, PII detection and dynamic masking for regulated data environments, audit logging of data access and the data classification framework that determines which data requires which controls. For HIPAA, PCI-DSS, GDPR and SOC 2 requirements, we produce the compliance evidence documentation your auditor will request as a byproduct of the governance implementation, not a separate sprint before the audit.
Three models: Dedicated Data Engineering Team (for multi-quarter data platform builds, ongoing data engineering programmes and organisations scaling their data capability), Time & Material (for discovery phases, iterative pipeline development and engagements where the data complexity is still being understood) and Fixed Cost (for well-scoped data projects with stable requirements specific warehouse migrations, BI implementations, defined pipeline builds). We recommend the right model based on your data estate's complexity and your scope clarity not based on which model is most convenient for us to staff.
AI and ML integration starts at the data architecture level not as an afterthought after the warehouse is built. Feature store design that makes ML feature engineering reusable and consistently defined across teams. Training dataset versioning and point-in-time correct feature retrieval that makes ML models reproducible. ML model output integration into the warehouse and BI layers so predictions are served where business decisions get made. Natural language query interfaces that let business users ask analytical questions in plain English. Automated data quality monitoring that uses ML to detect pipeline anomalies before they propagate into model training data. The data infrastructure that makes AI a production capability rather than a data science experiment.
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Curated insights, comparisons and best practices on cloud data warehousing, data engineering, dbt, lakehouse architecture, BI tooling, data governance and the AI-integrated data patterns reshaping how modern organisations build and scale their data platforms.
We'd love to understand what you want to build. The more context you share, the faster we can give you a useful response not a sales pitch, but a genuine assessment of how we can help and what working together would look like.
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