Services > Data platforms
Data platforms that connect machine and business data
We bring together data from industrial equipment, business applications, databases and files, so your reporting, analytics and AI run on data you can trust.
- From machine data to insight
- Governed and traceable
- Value in every phase
Why a data platform
Dashboards tell you what happened. A platform lets you act on it.
Point-solution BI gets your team through the quarter. A real data platform compounds: every new dataset, model, and product lands on the same trustworthy foundation – and gets cheaper, faster, and safer to deliver.
Decisions in hours, not weeks
One governed source of truth across finance, operations, and product – so executives, analysts, and ops teams stop arguing about whose number is right.
AI that actually ships
Clean, lineage-aware data with the access controls your legal team signs off on. The difference between a demo and production AI is almost always the platform underneath it.
Scales with the business
Built on modern cloud stacks – Snowflake, Databricks, Microsoft Fabric – with DataOps from day one. New markets, products, and acquisitions plug in; they don’t require a rebuild.
What we build
End-to-end data platform capabilities
We design and build every layer of the modern data stack – and, just as importantly, the operating model around it.
Cloud data warehouse & lakehouse
Greenfield builds or migrations on Snowflake, Databricks, and Microsoft Fabric (Warehouse & OneLake). Dimensional, Data Vault, or medallion – modeled for how your business actually asks questions.
Ingestion & streaming
Batch, CDC, and real-time pipelines from operational systems, IoT fleets, SaaS APIs, and partner feeds. Kafka, Event Hubs, Fivetran, Airbyte, Fabric Eventstream & Dataflows Gen2, custom connectors.
Transformation & modeling
dbt-based transformations with tests, docs, and lineage – plus Fabric notebooks and Spark where it fits. A semantic layer your business units can trust (dbt metrics, Fabric semantic models) and your data team can actually maintain.
DataOps & orchestration
Airflow, Dagster, Fabric Data Factory, CI/CD, environments, monitoring, cost controls. We treat the platform like production software – because it is.
Governance, quality & lineage
Catalogs (Unity Catalog, Microsoft Purview, Fabric OneLake catalog), access policies, PII handling, data contracts, observability. GDPR-ready and audit-ready, not bolted on after year two.
AI & ML enablement
Feature stores, vector stores, RAG foundations, MLOps, and Fabric Data Science / Copilot where it accelerates delivery. The platform decisions that determine whether your AI initiatives scale – or stall.
Reference architecture
Building blocks, assembled around your stack
We don’t ship a fixed stack. We design top-down – starting from the outcome you need to deliver, then tracing upstream through serve, transform, store, and ingest, back to the source. Every layer is a deliberate choice, made in service of the value above it.
Consume
BI & dashboards
Power BI (Fabric), Tableau, SAP BusinessObjects, SAP Analytics Cloud
Embedded analytics
Power BI Embedded, in-product data
AI & ML apps
RAG, forecasting, Fabric Copilot
Reverse ETL
Operational sync
Serve
Semantic layer
dbt metrics, Cube, Fabric semantic models
Feature / vector store
Feast, pgvector
APIs & data products
GraphQL, REST, Fabric SQL endpoints
Access & masking
Row/column policies
Transform
dbt transformations
Tested, versioned
Orchestration
Airflow, Dagster, Fabric Data Factory
Notebooks & Spark
Databricks, Fabric notebooks
Quality & observability
Great Expectations, OpenLineage, Monte Carlo
Store
Cloud DWH / Lakehouse
Snowflake, Databricks, Fabric (Warehouse & Lakehouse)
Catalog & lineage
Unity Catalog, Microsoft Purview, Collibra
Storage tiers
OneLake, S3, ADLS, GCS
Security & governance
IAM, encryption, audit
Ingest
Batch & CDC
Fivetran, Airbyte, Fabric Dataflows Gen2
Streaming
Kafka, Event Hubs, Fabric Eventstream
IoT & edge
MQTT, IoT Hub, Real-Time Intelligence
SaaS & APIs
Custom connectors, Fabric shortcuts
AI-ready by design
Every platform we build is AI-ready from day one
The difference between an AI demo and AI in production is almost always the platform underneath it. We build that foundation in from the start – clean, lineage-aware data, governed access, and the serving infrastructure your models and Copilots need.
Governed, lineage-aware data
Every model input is traceable to source – the bar enterprise AI actually has to clear.
Vector & feature stores
RAG foundations, embeddings, and feature pipelines built into the platform, not bolted on later.
Copilot-ready
Semantic layer and access policies that make Fabric Copilot and similar tools safe to switch on.
How we work
From platform assessment to a production data ecosystem
Four phases, delivered incrementally. We don’t disappear for nine months and come back with a big-bang reveal – each phase ends with something useful in your hands.
How we deliver results
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Step 1
Assess & align
We map your current state – systems, data, reporting, pain points – and translate it into clear platform outcomes.
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Step 2
Architect
We design a target architecture fit for your regulatory, cost, and organizational context – with an unbiased tech selection.
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Step 3
Build the MVP platform
We stand up the core platform around a high-value use case, end to end – ingestion, storage, modeling, serving, governance.
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Step 4
Scale & operate
We extend the platform across new domains, embed governance, and – if helpful – operate it with you.
Case studies
Work we've done
Technologies
Databricks, SQL, Power BI, Qlik, Kimball star schema, medallion architecture.
Client challenge/business need
Mirka’s BI had grown across multiple platforms over the years, with Qlik anchoring production test reporting. As new product lines, test systems, and data formats were added, the analytics landscape kept expanding. To support future analytical needs and improve operational efficiency, Mirka decided to standardize its BI platform around Power BI and Databricks.
Solution at a glance
Proekspert led the design and build of a Databricks-based data platform and a Power BI semantic model for Mirka’s production quality analytics. We translated Qlik scripts and QVD logic into a medallion architecture and Kimball-style star schema with conformed dimensions, optimized for incremental refresh and tuned for 500M+ rows in Power BI.
Results
- Faster, more confident quality decisions. The quality team can catch anomalies sooner across products and test parameters, acting with greater confidence.
- A leaner cost base for analytics. Consolidating on Power BI streamlines the environment and enables more efficient use of development resources.
- A platform ready for the next decade. Databricks and Power BI scale to new product lines, test systems, and AI use cases without another rebuild.
Technologies
Azure, Azure Data Factory, dbt, Snowflake, Power BI, Data Vault 2.0
Client challenge/business need
After a period of rapid growth, the company recognized that its CRM and data warehouse foundations were due for renewal. The team wanted a modern, cloud-native platform – ready for full integration across CRM, billing, and reporting, with the headroom to take on AI-readiness as the business kept scaling.
Solution at a glance
Proekspert helped validate the target stack and build a cloud-native data warehouse MVP on Snowflake within 1.5 months – with Azure Data Factory for ingestion, dbt for transformations, Power BI for reporting, and Data Vault 2.0 modeling. The platform integrates fully with CRM and billing, with “Talk To Your Data” planned next.
Results
- Unified, simplified reporting. Power BI on a Data Vault 2.0 foundation gives the business one consistent view across functions, with historical data modeled for change.
- Cloud-native, lower maintenance. A fully cloud-native architecture minimizes platform overhead and supports the team as the company continues to grow into new markets and product lines.
- AI-ready foundation. Data Vault 2.0 and a Power BI semantic layer set up the platform for governed AI – “Talk To Your Data” is next on the roadmap.
Technologies
Azure Databricks, Medallion architecture, Great Expectations
Client challenge/business need
The client wanted to give its quality department a consolidated view of company devices – a runtime environment for analytics on device data. The goal was to respond to customer requests faster, build sharper operational insight, and support better decision-making across the network as data volumes grew.
Solution at a glance
Proekspert proposed and built an Azure Databricks environment with medallion architecture – streamlined ingestion, processing, and analytical capabilities within a single platform. Comprehensive dataflows run nightly, with Great Expectations quality checks and pipeline error notifications wired in, so the analytics team has the operational visibility a quality-critical platform needs to keep delivering.
Results
- Actionable device-level insight. Reports built on diverse device analytics let the quality team respond to user requests quickly, supporting better decision-making across the network.
- Streamlined, governed dataflow. Nightly pipelines, quality gates, and error notifications give the team a consistent dataflow with the visibility a critical analytics platform needs.
- Built to grow. Medallion architecture on Databricks scales to new device types, new feeds, and new analytics use cases as the quality department’s needs evolve.
Technologies
Snowflake, Airflow, dbt, Power BI
Client challenge/business need
Three years into their Snowflake data warehouse journey, the client wanted to take the foundation further. They were looking for a more flexible architecture, a clearer dimensional model, and modern transformation and orchestration tools – so the platform could keep pace with business growth across markets.
Solution at a glance
Proekspert helped the team build a flexible foundation and a dimensional model on Snowflake, administrable from a single place. Airflow and dbt joined the stack for orchestration and transformations, Power BI reporting was simplified for clarity, and the resulting platform now serves as the client’s roadmap for keeping pace with business growth.
Results
- Clarity and reusability. A consistent dimensional model gives the team reusable building blocks for reports and analytics, with cleaner relationships and easier validation across markets.
- Flexibility to business change. Airflow, dbt, and a single point of administration mean changes can be made in one place and flow through the platform consistently.
- A roadmap for growth. Snowflake, Airflow, and dbt give the team a foundation they can keep extending as the business grows into new markets and analytics use cases.
Tech we build on
Modern stacks, picked for your context
We’re deeply hands-on across the leading cloud data stacks. We recommend what fits your context, not what we sell most of.
Cloud DWH & Lakehouse
Snowflake, Databricks, OneLake, Microsoft Fabric · Warehouse, Microsoft Fabric · Lakehouse
Transformation
dbt, Spark, SQL, PySpark, Data Vault 2.0, Fabric notebooks, Fabric Dataflows Gen2
Orchestration & DataOps
Airflow, Dagster, Azure Data Factory, Fabric Data Factory, Fabric pipelines, GitHub Actions, Terraform
Ingestion & streaming
Kafka, Event Hubs, Fivetran, Airbyte, Debezium (CDC), OneLake shortcuts, Fabric Real-Time Intelligence
Governance & quality
Unity Catalog, Microsoft Purview, Fabric OneLake catalog, Collibra, OpenLineage, Great Expectations
BI & AI/ML
Power BI, Power BI in Fabric, Fabric Copilot, Tableau, SAP Analytics Cloud, PyTorch, TensorFlow, MLflow, Fabric Data Science
FAQ
Questions data & business leaders ask us
If every new use case costs disproportionately more than the last, if your AI initiatives keep stalling on “we don’t trust the data,” or if you can’t trace a number in a dashboard back to its source, you’ve outgrown your BI setup and you need a platform. A platform is what turns data work from a cost center into something that compounds.
It depends on your workloads, your existing stack, your team’s skills, and your regulatory context. We’ve delivered production platforms on all three and we’re not incentivized to pick one. Our Assess & Architect phases are explicitly designed to make this call on evidence, not vendor marketing.
Our first measurable outcome typically lands in 8–12 weeks: a working slice of the platform delivering one high-value data product end-to-end, on the target stack. From there we extend iteratively — you never wait a year for the big reveal.
Yes — that’s our default. We embed with your team, transfer knowledge continuously, and leave you with a platform your people can extend and operate. We can also run an operate-with-you model for DataOps if that’s useful.
You can pilot AI without one. You can’t reliably ship AI in production without one. The vast majority of stalled enterprise AI programs we see fail at the data layer — stale, ungoverned, unlineaged data — not at the model layer. A platform is the fastest path to AI that actually survives legal review and scale.
Contact us
Tell us where your data is today – we’ll map a way forward
Share your current stack, your headaches, or a use case you’re trying to unlock. We’ll come back with a candid read on what’s feasible, what it’d take, and whether we’re the right partner for it.
Urmas Kobin
Partner and BI & Data Analytics Lead
Languages: EST, ENG