Microsoft Fabric · Azure Databricks · Azure Synapse

Azure Data Platform Services That Pay for Themselves

We design, build and run data platforms on Microsoft Fabric, Azure Databricks and Azure Synapse Analytics, from the first data load to trusted Power BI reports and AI. Every engagement is led by a senior data architect with 18 years in data. Every platform we hand over is secure, tested, documented and ready for production, and every project starts with an agreed, measurable return.

years building data platforms
18years building data platforms
of rows tuned in production
Billionsof rows tuned in production
Microsoft Azure data stack
100%Microsoft Azure data stack
agreed before we build
ROIagreed before we build

Sound Familiar?

The Data Problems We Solve Every Week

These are the questions data teams post in the Microsoft Fabric Community forums week after week. If any of them sound like your team, we have already solved them for someone else.

“Our Fabric capacity keeps throttling, and the bill keeps climbing.”

How we fix it: We find the queries, notebooks and refresh schedules that burn capacity units, remove the habits carried over from older SQL platforms (row-by-row loops, SELECT *, full reloads), and right-size your capacity. You get the same results for less.

“The full load works, but the incremental load fails, so we reload everything every night.”

How we fix it: We build incremental loads with watermarks, change data capture and Delta MERGE that can be rerun safely without duplicating data. Nightly loads that took hours can finish in minutes.

“We can't connect Fabric securely to our private Azure SQL, Key Vault, on-premises SQL Server, Oracle or SAP.”

How we fix it: We design the network path with your security team: private endpoints, managed private endpoints, VNet and on-premises data gateways, and secrets in Azure Key Vault. Your security policy stays intact and your pipelines still run on time.

“We have dozens of source systems on Azure Data Factory and Synapse. Should we rebuild on Fabric?”

How we fix it: We assess what you have and show you what to move as it is, what to redesign and what to leave alone, with the cost and the benefit of each. Nothing is switched off until the numbers in Fabric match the old system.

“Promoting from Dev to Test to Production is manual, and something always breaks.”

How we fix it: We set up Git integration, deployment pipelines and Variable Libraries so connections and settings change per environment automatically, and we script what the tools don't carry over, such as SQL permissions.

“Our data arrives as report-style Excel files, CSVs and hundreds of files with different layouts.”

How we fix it: We design Bronze, Silver and Gold layers that absorb messy, changing sources from SharePoint, APIs and file drops, standardise them, and turn them into clean tables your analysts can use.

“Every department has its own numbers, and nobody trusts the dashboard.”

How we fix it: We build one dimensional model and a shared Power BI semantic model with agreed business definitions, and we publish the data's source and quality next to each report, so the debate moves from 'whose number is right' to 'what do we do about it'.

“We need real-time data, but we don't know whether it belongs in an Eventhouse or a lakehouse.”

How we fix it: We design Real-Time Intelligence solutions with Eventstream, Eventhouse and KQL, handle changing event schemas, replace polling with event-driven pipelines, and use Activator to send Teams or email alerts the moment a threshold is crossed.

“We have spent a lot on data, and the board is asking what we got for it.”

How we fix it: We tie every initiative to a business outcome, measure the return, and report it to your finance team in rand, not in dashboards delivered. See how we do it below.

Before: four reports show four different October revenue figures. After: one certified figure with an agreed definition, visible lineage and quality checks.
Zoom
Illustrative example. The figures are not from a real client.

Azure Data Platform Services

Microsoft Fabric, Azure Databricks & Synapse Consulting

From architecture and migration to engineering, modelling, governance and support: everything you need to run a modern data platform on Microsoft Azure, delivered by people who build on it every day.

01

Strategy & Architecture

Get the design right before you spend on build. We plan Azure data platforms that fit your workloads, your budget and your team.

Microsoft Fabric & Azure Data Platform Architecture

We design your target platform on Microsoft Fabric, Azure Databricks or Azure Synapse Analytics, choosing the right service for each workload. Data is organised into Bronze, Silver and Gold layers (the medallion architecture) that scale as your business grows, with security, monitoring and cost controls designed in from day one.

Azure servicesMicrosoft Fabric, OneLake, Azure Databricks, Azure Synapse Analytics, Azure Data Lake Storage Gen2

Data Platform Health Check & Fabric Readiness Assessment

An independent review of your Azure data platform covering architecture, pipelines, security, data quality, performance and cost. If you are considering Microsoft Fabric, we assess your readiness and your expected capacity size. You receive a prioritised list of findings, the business impact of each, and a practical plan to fix them.

Azure servicesMicrosoft Fabric, Azure Databricks, Azure Synapse Analytics, Azure Data Factory, Azure Cost Management

Migration to Azure & Microsoft Fabric

Data warehouse modernisation without the risk. We move on-premises SQL Server and Oracle data warehouses, SSIS packages, SSAS models and Power BI Report Server to Azure, and we migrate Azure Synapse and Power BI Premium workloads to Microsoft Fabric. Stored procedures and reports are converted, and the numbers are reconciled against the old system before go-live.

Azure servicesMicrosoft Fabric, Azure Synapse Analytics, Azure Data Factory, Azure Databricks, Power BI Service

02

Data Engineering

Reliable pipelines that load the right data, on time, every time, and cost less to run.

Data Warehouse & Lakehouse Implementation

End-to-end build of your Fabric lakehouse or data warehouse, from the first data load to production reports. Loads can be rerun safely without duplicating data, and your team gets handover documentation it can maintain.

Azure servicesFabric Lakehouse and Warehouse, Synapse dedicated and serverless SQL pools, Azure Databricks, Delta Lake

Metadata-Driven Ingestion Pipelines

One framework, driven by configuration tables, loads every source system and table. Adding a new source means adding a row, not writing a new pipeline. It includes incremental loads, change detection and deletion detection, and connects databases, business applications and older systems through ODBC and JDBC.

Azure servicesAzure Data Factory, Fabric Data Factory, Synapse pipelines, Azure SQL Database, Cosmos DB

Azure Databricks Engineering

PySpark, Spark SQL and Scala pipelines on Azure Databricks, with Delta Lake for reliable, ACID-compliant data. We implement Delta MERGE, full change history, Databricks Workflows orchestration and right-sized clusters that keep compute costs under control.

Azure servicesAzure Databricks, Delta Lake, Databricks Workflows, Azure Data Lake Storage Gen2

Real-Time & Streaming Analytics

Capture transactions, device readings and application events as they happen, and make them available for reporting and alerting in seconds. We build event-driven pipelines that react to new data instead of polling for it.

Azure servicesFabric Real-Time Intelligence (Eventstream, Eventhouse, Activator), Azure Event Hubs, Azure Stream Analytics, Databricks Structured Streaming, Azure Functions, Azure Service Bus, Logic Apps

03

Data Modelling & Analytics

One version of the truth, modelled for the questions your business actually asks.

Dimensional Data Modelling

Star schemas with clear facts and dimensions, surrogate keys, history tracking (SCD Type 1 and Type 2) and business definitions agreed with your stakeholders, so every report shows the same numbers.

Azure servicesFabric Data Warehouse, Azure Synapse Analytics, Azure Databricks SQL

Power BI Semantic Models & Reporting

Power BI semantic models, DAX measures, row-level security and dashboards built on your Gold layer. Includes Direct Lake models on Microsoft Fabric, shared semantic models across workspaces, paginated reports and gateway setup for on-premises sources.

Azure servicesPower BI, Microsoft Fabric, On-premises data gateway

04

Data Quality, Security & Governance

Numbers people trust, data that stays protected, and an audit trail for every load.

Data Quality Framework

Automated checks for completeness, validity and consistency run on every load. Failing records are flagged and the team is alerted before bad data reaches a report.

Azure servicesMicrosoft Fabric, Azure Databricks, Delta constraints, Azure Data Factory

Audit, Logging & Monitoring

A central log of every pipeline run, row count and failure, with dashboards and alerts so issues are found before your users notice them.

Azure servicesAzure Monitor, Log Analytics, Fabric Data Warehouse, Azure SQL Database

Data Security & Governance

Access control through Microsoft Entra ID, role-based permissions, row and column level security, data masking and secrets kept in Azure Key Vault. Microsoft Purview catalogues your data and shows its lineage, so you know where every number comes from, and supports your POPIA and GDPR obligations.

Azure servicesMicrosoft Entra ID, Azure Key Vault, Microsoft Purview

05

Operate & Optimise

Keep the platform fast, affordable and in your team's hands after go-live.

Performance Tuning & Cost Optimisation

We speed up slow queries and pipelines and reduce your Azure bill with partitioning, Z-ordering, table distribution and Spark tuning, and we right-size your Fabric capacity, Databricks clusters and Synapse resources. Proven on tables with billions of rows.

Azure servicesMicrosoft Fabric, Azure Databricks, Azure Synapse Analytics, Azure Cost Management

CI/CD & DataOps

Source control and automated deployment for Data Factory, Synapse, Databricks and Fabric, with separate Development, Test and Production environments, Variable Libraries for environment settings, and pull request reviews.

Azure servicesAzure DevOps (Repos, Pipelines, Boards), GitHub, Fabric deployment pipelines, Fabric Git integration

Production Support & Troubleshooting

Expert help when pipelines fail, loads slow down or numbers stop matching. We find the root cause, fix it and add safeguards so it doesn't happen again.

Azure servicesAzure Monitor, Log Analytics, Microsoft Fabric, Azure Databricks

Training & Team Enablement

Hands-on training and mentoring for your engineers and analysts on Microsoft Fabric, Azure Databricks, Azure Synapse Analytics, Data Factory and Power BI, so your team can run and extend the platform with confidence.

Azure servicesMicrosoft Fabric, Azure Databricks, Azure Synapse Analytics, Power BI

Book a call about your data platform →

AI on Your Data

Practical AI, Built on Data You Can Trust

AI is only as good as the data underneath it. Because we build the platform, we can put AI to work on it safely: inside your Azure tenant, under your security rules, and measured against a business outcome like everything else we deliver.

AI-Ready Data Foundations

Clean, governed Gold-layer tables and well-described semantic models are what make Copilot, data agents and machine learning give correct answers. We design them with AI in mind from the start.

Azure servicesMicrosoft Fabric, OneLake, Microsoft Purview, Power BI

Copilot & Fabric Data Agents

Let managers ask questions about your warehouse and semantic models in plain English and get answers that respect row-level security. We configure, ground and test Copilot in Fabric and Power BI and Fabric data agents so the answers can be trusted.

Azure servicesCopilot in Fabric and Power BI, Fabric data agents, Microsoft Fabric

Generative AI Assistants (RAG)

Secure assistants that answer questions from your own policies, contracts, product manuals and support tickets, with sources cited. Your data stays in your tenant and is not used to train public models.

Azure servicesAzure OpenAI in Azure AI Foundry, Azure AI Search, Azure Functions

Text, Sentiment & Document AI

Score the sentiment of customer feedback, classify and summarise call notes and emails at scale, and turn invoices, statements and forms into structured tables, all inside your data pipelines.

Azure servicesAzure AI Language, Azure AI Document Intelligence, Fabric AI functions

Machine Learning & Predictive Analytics

Churn prediction, demand forecasting, anomaly detection and fraud scoring. Models are tracked with MLflow and their scores are written back into the CRM, call-centre screen or app where people act on them.

Azure servicesFabric Data Science, Azure Databricks, MLflow, Azure Machine Learning

AI-Assisted Delivery

We use AI coding assistants under senior review to generate pipelines, tests and documentation faster. You get delivery sooner and at lower cost, and every line is still reviewed, tested and documented by an experienced engineer.

Azure servicesGitHub Copilot, Azure DevOps, Microsoft Fabric

Book a call about an AI use case →
A manager asks which customers are likely to leave this month and why, and gets an answer with its sources and row-level security applied, plus a stock alert.
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Illustrative example of Copilot and Fabric data agents on a governed Gold layer.

Delivery You Can Bank On

How We Make Sure Your Data Project Doesn't Fail

Industry research has found for years that most analytics projects never deliver measurable business value. The reasons are rarely technical. Nobody agreed what success was worth. Nobody owned the outcome. The insights stayed in a dashboard that the people doing the work never opened. And nobody measured the return, so the next budget request was harder than the last. Our delivery method is built to remove every one of those causes.

Eight delivery steps shown as a rising value line, next to a value scorecard comparing cost, forecast and actual value per initiative.
Zoom
Illustrative example. The scorecard figures are not from a real client.
  1. 1

    Agree the outcome in rand before we write any code

    We start with a decision or a process, not a tool: the revenue to protect, the cost to remove, the risk to reduce. Together we record today's baseline, how the improvement will be measured, and the business owner responsible for it.

  2. 2

    Build an honest business case

    We forecast the value from your own numbers and set it against the full cost, including the people, process changes, application integration and training that a dashboard-only quote leaves out. Your finance team checks the assumptions before anything is approved.

  3. 3

    Prove it on real data first

    A short proof of value, typically four to six weeks, runs on a narrow slice of your real data. You make the go or no-go decision on evidence, not on a demo, and you only scale what has shown it works.

  4. 4

    Put insights where the work happens

    Value appears when someone acts differently. We deliver results into the CRM, the call-centre screen, the ERP or a Teams alert, at the moment a decision is made. A dashboard is a starting point, not the finish line.

  5. 5

    Deliver in short phases, with the foundations included

    You get something useful every two to four weeks, and no phase runs longer than three months without delivering value. Each phase also adds a piece of the foundation (security, monitoring, CI/CD, data quality), so success never forces an expensive rebuild later.

  6. 6

    Make every number trustworthy

    Business definitions, data lineage and quality scores are published alongside each report. Leaders stop asking where a number came from and start acting on it.

  7. 7

    Measure and report the return

    A value scorecard lists each initiative with its cost, forecast value, actual value and the teams that contributed. We review it with you and your finance team every month, fix what is falling short, and use the results to justify the next investment.

  8. 8

    One senior owner, and a team that can run without us

    A senior architect is accountable for the outcome and works across business, IT and data to clear blockers. At handover you get documentation, coding standards and training, so the platform never depends on a single person, including us.

What you receive

  • ✔Business case and ROI model your finance team has checked
  • ✔Roadmap that balances quick wins with foundations
  • ✔Proof of value on your own data
  • ✔Monthly value scorecard: cost versus forecast versus actual
  • ✔Data trust pack: definitions, lineage and quality scores
  • ✔Runbooks, documentation and trained people at handover

Engagement Models

Flexible Engagement Models

Three ways to work with us, depending on the scale of your needs.

Fixed Scope

Project-Based

Defined scope, timeline and deliverables. Ideal for health checks, readiness assessments, migrations and lakehouse or warehouse implementations.

Ongoing

Managed Service

Ongoing ownership of your Azure data platform. We run it, monitor it, keep costs down and keep improving it.

Flexible

Team Augmentation

A senior Fabric or Databricks engineer embedded in your team, for capability gaps, peak periods and knowledge transfer.

Learn Microsoft Fabric with Us

Free Microsoft Fabric Video Tutorials

We publish step-by-step Microsoft Fabric tutorials on our YouTube channel, Data Engineering with Lunak. Each video is recorded in a real Fabric workspace and comes with a PowerPoint guide, so you can follow along and get the same result in your own environment. It is also the best way to see how we work before you speak to us.

Watch on YouTube →

FAQ

Questions We Hear Most Often

The conversations that usually come up before a Microsoft Fabric or Azure data project starts.

Which data platforms do you work with?

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We specialise in the Microsoft Azure data stack: Microsoft Fabric, Azure Databricks, Azure Synapse Analytics, Azure Data Factory, Azure SQL Database, Power BI, Microsoft Purview and Azure AI services. Focusing on one ecosystem means we know it deeply, including its limits and workarounds.

Do you offer Microsoft Fabric consulting in South Africa?

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Yes. Lunak Solutions is a Microsoft Fabric consultant and implementation partner based in Pretoria. We are a data engineering consultancy for organisations in Johannesburg, Cape Town, Durban and across South Africa, on-site or remotely, and we also deliver remotely for clients outside South Africa.

Should we choose Microsoft Fabric or Azure Databricks?

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It depends on your workloads, your team's skills and how much you already use Power BI. Fabric suits organisations that want one SaaS platform with Power BI built in; Azure Databricks suits heavy Spark engineering and advanced machine learning. Many clients use both. Our readiness assessment gives you a recommendation backed by your own workloads and costs.

How long does a migration from Synapse or SQL Server to Microsoft Fabric take?

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A focused migration of one warehouse and its pipelines usually takes a few months; an enterprise estate with many sources takes longer. We start with an assessment, then migrate in phases so you see value early, and we reconcile the numbers against the old system before anything is switched off.

How do you keep Microsoft Fabric capacity costs under control?

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We monitor capacity unit usage per item, fix the queries and notebooks that waste it, schedule heavy work outside business hours, and size the capacity to your real workload. The goal is the same results on a smaller capacity, or more workloads on the one you already pay for.

How do you make sure our data project delivers a return on investment?

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We agree the business outcome and how it will be measured before any build, prove value on your real data first, put insights into the processes where decisions are made, and report cost against forecast and actual value every month in a value scorecard your finance team can audit.

Our existing data platform is slow and unreliable. Can you help?

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Yes. Many engagements start with a Data Platform Health Check. We find the root causes (design, pipelines, data quality, security or cost), prioritise the fixes by business impact and then fix them with your team.

Can we use AI on our data safely?

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Yes. We build AI solutions inside your Azure tenant with Azure OpenAI, Copilot in Fabric and Fabric data agents. Your existing security rules, including row-level security, still apply, and your data is not used to train public models.

Do you provide training and ongoing support after go-live?

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Yes. Every project includes documentation and knowledge transfer, and we offer managed services, production support and hands-on training for engineers and analysts. Our free Microsoft Fabric video tutorials on YouTube are a good place to start.

How does an engagement start?

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With a short discovery call about your goals, your current platform and the outcome you need. We then recommend a starting point, usually a health check, a readiness assessment or a proof of value, with a clear scope and cost.

Tell Us the Outcome You Need

Every engagement begins with a conversation about results, not tools. Tell us about your data challenge and we'll show you the fastest route to a measurable return.

Book a discovery call →