// boutique data engineering consultancy
We build data platforms that actually work.
SerialNode is a boutique consultancy specialising in enterprise data engineering on Microsoft Azure, Databricks, and Microsoft Fabric. We design and deliver production-grade data platforms. From architecture to production pipelines.
We work with a small number of clients at a time. That means your engagement gets senior-level attention on every sprint, not a junior team handed a ticket queue. Our work is architecture-led, documented, and built to be owned by your team when we leave.
Whether you are migrating a legacy warehouse, standing up a lakehouse from scratch, rolling out Microsoft Fabric, or scaling ML workloads on Databricks — we have done it at enterprise scale across manufacturing, energy, automotive, and materials industries. We also have a track record of cutting Azure spend significantly — often 30–50% — without touching SLAs or data freshness.
Azure DatabricksMicrosoft FabricAzure Data FactoryDelta LakedbtPySparkMedallion ArchitectureMLflowPower BICI/CDUnity CatalogData GovernanceCloud Cost Optimisation
// why a boutique over a big consultancy
Senior delivery, always
The engineer who scopes your project is the engineer who builds it. No bait-and-switch with juniors once the contract is signed.
Deep Azure specialisation
We don't do AWS, GCP, Snowflake, or generic "data strategy." We are narrow by design — Azure, Fabric, Databricks, and the surrounding stack. Depth beats breadth.
We build to hand over
Every engagement ends with documented architecture, tested pipelines, and your team capable of running what we built. We are not interested in creating dependency.
Honest scoping
We tell you what can be done in your timeline and budget before we start. If a Fabric migration is premature for your maturity level, we'll say so.
Cost is a first-class concern
Cloud costs are part of the design, not an afterthought. We audit compute, storage, and pipeline patterns as part of every engagement. On active projects we have reduced Azure spend by 30–50% without affecting performance or data freshness — through cluster policy, job architecture, and storage tier optimisation.
// services — full delivery lifecycle
01Strategy & Platform Architecture
Before any code is written, we align on the target state. We deliver a written architecture document — technology choices with rationale, security and governance frameworks, phased migration plan, and a cost model with TCO projection.
02Data Engineering & Lakehouse Build
End-to-end pipeline development on Azure Databricks or Microsoft Fabric. Medallion architecture (Bronze → Silver → Gold), Delta Lake, dbt transformation layers, automated testing, and observability baked in from the start.
03Legacy Warehouse Migration
Structured migration from on-premise SQL, SSIS, or siloed databases to a unified cloud lakehouse. We handle assessment, code conversion, data validation, reconciliation, and production cutover — with zero surprises.
04BI, Analytics & Reporting
Semantic layer design and Power BI deployment on top of your curated lakehouse data. We build models that are fast, governed, and self-service ready — with real-time analytics where the workload demands it.
05ML & AI Workloads
Feature engineering, model training, and MLflow lifecycle management on Databricks. We set up reproducible, observable ML pipelines — from raw feature tables through to deployed model endpoints.
06Governance, Security & Cost Control
Unity Catalog setup, RBAC, data masking, row-level security, and lineage tracking. Proactive cost monitoring, cluster policy governance, and storage tier management to keep your Azure bill predictable — and shrinking.
// cutting your azure bill — without cutting corners
Databricks and Fabric are powerful — and expensive when misconfigured. Most organisations overspend on cloud data infrastructure by 30–50% within the first year, usually due to oversized clusters, always-on compute, unpartitioned Delta tables, and pipelines that re-process data they don't need to. We fix this systematically.
Cluster right-sizing
Most Databricks clusters are 2–3× larger than the workload requires. We profile actual job resource usage and define cluster policies that auto-terminate and right-size — reducing idle compute to near zero.
Pipeline architecture review
Full scans on large Delta tables, missing Z-ORDER, no data skipping, excessive shuffle — these patterns silently multiply your compute bill. We audit and re-architect pipelines to process only what needs processing.
Storage tier & retention
Delta table VACUUM, lifecycle policies on ADLS, and hot/cool/archive tier assignment can cut storage costs by 20–40% with zero impact on query performance for active workloads.
Fabric capacity management
Fabric F-SKU sizing and pause/resume scheduling are frequently misconfigured. We set up autoscale policies and workload isolation so you pay for what you actually use, not a flat always-on capacity.
Ongoing cost monitoring
We instrument your environment with Azure Cost Management alerts, Databricks cluster usage dashboards, and budget guardrails — so regressions are caught before the invoice arrives.
Real results: Spark job optimisation cut pipeline runtime from hours to <30 minutes and reduced compute spend by 50%. Pipeline re-architecture improved data freshness by 600% while eliminating redundant daily batch re-runs.
// microsoft fabric + databricks — stronger together
Most organisations on Azure end up running both. Databricks handles the heavy lifting — large-scale ingestion, complex transformations, and ML workloads. Fabric handles the business layer — semantic models, governed reporting, and self-service analytics. We design architectures where the two work as one system, not two silos.
Databricks Lakehouse → OneLake (Delta / Parquet) → Microsoft Fabric semantic layer → Power BI
[Databricks handles]
Large-scale ingestion and ELT, Spark transformations, ML model training, streaming workloads, Unity Catalog governance, and Delta Lake as the storage foundation.
[Fabric handles]
Semantic modelling, Power BI reporting, real-time analytics with KQL, self-service BI for business users, and cost-efficient compute for analytical queries.
[Single source of truth]
Databricks' lakehouse feeds Fabric's OneLake via Delta sharing or direct mount — every team reports from the same governed, versioned dataset. No reconciliation overhead.
[AI to business, fast]
ML outputs from Databricks surface directly in Fabric Power BI reports and Copilot experiences — no manual export pipeline between your data science and BI teams.
// how OUR engagement works
01
Scoping call
30 min, no NDA required
We learn your stack, pain points, and timelines. You get a straight answer on fit and a rough scope estimate before any paperwork.
02
Architecture proposal
Written document covering target state, migration path, technology choices with rationale, risks, and a phased delivery plan.
03
Iterative delivery in sprints
Two-week cycles with defined deliverables. Working pipelines early, not a 3-month black box. Weekly async updates; no status-meeting overhead unless you want it.
04
Handover & enablement
Full documentation, a runbook, and a knowledge-transfer session with your team. The platform belongs to you, not to us.
// clients
Enterprise clients across the manufacturing, energy, automotive, and materials industries across Europe.
// certifications
Databricks Certified Data Engineer ProfessionalDatabricks
Databricks Certified Associate Developer for Apache SparkDatabricks
DP-700 — Microsoft Fabric Data Engineer AssociateMicrosoft
DP-203 — Azure Data Engineer AssociateMicrosoft
AZ-400 — DevOps Engineer ExpertMicrosoft
DP-500 — Azure Enterprise Data Analyst AssociateMicrosoft
AZ-104 — Azure Administrator AssociateMicrosoft
// technology stack
Cloud & Platforms
Azure Databricks, Microsoft Fabric, Azure Synapse Analytics, Azure Data Lake, Azure Data Factory
Languages
Python, PySpark, SQL, T-SQL, Spark SQL, PowerShell
Transformation
dbt, Delta Lake, Delta Live Tables, Spark structured streaming
ML & AI
MLflow, Databricks Feature Store, LLM / RAG pipelines
BI & Reporting
Power BI, Fabric semantic models, KQL (Kusto Query Language)
DevOps & Tooling
Azure DevOps, Git, CI/CD, YAML, Key Vault, Terraform
Governance
Unity Catalog, RBAC, data masking, row-level security, lineage tracking
Architecture patterns
Medallion architecture, lakehouse, ELT, data contracts, schema evolution, SCDs
