Mirka: Qlik to Power BI migration for manufacturing
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.
MVP cloud data warehouse in 1.5 months for a manufacturer
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.
Turning device data into action at an energy supplier
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.
Modernizing a Snowflake DWH for a multinational fintech
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
Transformation
Orchestration & DataOps
Ingestion & streaming
Governance & quality
BI & AI/ML
FAQ
Questions data & business leaders ask us
Common questions we get from data and business leaders considering a platform build or renewal.
We already have BI and a warehouse. Why would we need a “platform”?
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.
Snowflake, Databricks, Microsoft Fabric – which should we pick?
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.
How long until we see value?
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.
Can you work alongside our existing data team?
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.
What about AI? Do we need a data platform before doing AI?
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.
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.
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Urmas Kobin
Proekspert AS
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