Databricks & Lakehouse Architecture
We design and implement Databricks Lakehouse solutions that combine the best of data lakes and data warehouses, providing a unified platform for all your data engineering and ML workloads.
Lakehouse Solutions for Data Engineering & ML
Databricks
We deploy Databricks and Lakehouse architectures for data engineering and ML workloads, enabling advanced analytics, machine learning, and unified governance at enterprise scale.
Lakehouse · Spark · Unity Catalog
What Does Databricks on Azure Look Like?
Databricks is where we build when the engineering is serious: large-scale Spark pipelines, streaming at volume, machine learning that needs a proper lifecycle, and data teams who live in notebooks and Git. It pioneered the lakehouse pattern that the whole industry now follows, and for engineering-led estates it remains the deepest implementation of that idea on the market.
Our Databricks practice is unusual because of what sits next to it. Synapx holds all three Microsoft Fabric Featured designations, so when we recommend Databricks it is not because it is the only thing we sell. It is because, for your workloads, it is the better tool, and we can defend that call platform by platform, workload by workload.
On Azure, Databricks is a first-party service, which surprises people who assume Microsoft-centric means Fabric-only. Entra ID handles identity, private networking keeps traffic off the internet, and OneLake mirroring means Power BI can serve analytics straight from the Delta tables your engineers maintain. The two platforms cooperate more than their marketing suggests.
The disciplines we bring are the same ones behind every Synapx data build: medallion architecture with data contracts, Unity Catalog governance designed in from the start, infrastructure as code, CI/CD for notebooks and pipelines, and cluster policies that keep DBU spend honest. A lakehouse without those disciplines is a data swamp with better branding.
And when the workload profile says Fabric, Synapse or plain Azure SQL would serve you better, we will say exactly that. Read our Fabric vs Databricks comparison for how we make the call.
Databricks, delivered on Microsoft Azure
What We Deliver
Key capabilities
Data Engineering at Scale
We build scalable data pipelines on Databricks using Delta Lake, Apache Spark, and Delta Live Tables for production-grade data ingestion, transformation, and orchestration.
- Medallion architecture with data contracts between layers
- Batch and Structured Streaming from one codebase
- CI/CD for notebooks, pipelines and jobs via Git integration
- Automated data quality expectations in Delta Live Tables
Machine Learning & MLOps
We implement end-to-end ML workflows using Databricks ML Runtime and MLflow, streamlining experimentation, model training, governance, and production deployment.
Unity Catalogue & Governance
We establish unified data governance across multi-cloud and multi-workspace environments using Unity Catalogue, with metadata management, access controls, and lineage tracking at scale.
- One permission model across every workspace and cloud
- Column-level lineage and audit trails for regulators
- Data discovery your analysts will actually use
- Designed in from the start, not retrofitted after the first audit
Performance Optimisation
We tune Apache Spark jobs and optimise Databricks clusters for maximum performance and cost efficiency through partitioning strategies, caching, and compute right-sizing.
10 wks
To a governed Lakehouse with first workload live
100%
Unity Catalogue coverage across workspaces
IaC
Every environment defined as code with CI/CD from day one
1
Copy of data serving both Databricks and Fabric via OneLake
Common Use Cases
Where Databricks earns its keep
Enterprise lakehouse platform
Build a Delta Lake-based medallion architecture that consolidates raw, curated and consumption layers for analytics, BI and ML.
Production ML and MLOps
Operationalise models with MLflow, Model Serving and Feature Store so data science stops being experimental and starts driving decisions.
GenAI and RAG on enterprise data
Use Databricks Mosaic AI and Vector Search to build retrieval-augmented assistants grounded in your governed lakehouse.
Streaming analytics at scale
Deliver sub-minute operational insights using Spark Structured Streaming and Delta Live Tables over IoT, clickstream or transactional feeds.
Migrating off Hadoop or legacy Spark
Retire Cloudera, HDInsight or self-managed Spark estates onto Databricks with predictable cost and far less operational overhead.
Unified governance across clouds
Introduce Unity Catalogue to give a single, auditable view of data and AI assets across Azure, AWS and GCP workspaces.
How We Work
A proven delivery approach
- 01 Step
Assess
Review workloads, data volumes, existing Spark or warehouse estate, and commercial model to set a Databricks target state.
- 02 Step
Design
Architect workspace, Unity Catalogue, networking, cluster policies and ALM patterns aligned to your security and cost guardrails.
- 03 Step
Build
Deploy infrastructure-as-code, migrate or engineer the first domain, and establish CI/CD for notebooks, pipelines and models.
- 04 Step
Optimise
Tune clusters, partitioning, caching and workflows; monitor DBU spend; transition to managed operations.
Architecture Choice
Lakehouse or traditional warehouse?
Plenty of organisations arrive at Databricks from a warehouse world: star schemas, nightly loads, SQL everywhere. The lakehouse is not a rebrand of that model, and knowing where the two genuinely differ is what stops a migration becoming an expensive lateral move.
| Criteria | Lakehouse on Databricks | Traditional data warehouse |
|---|---|---|
| Data it handles | Structured, semi-structured and unstructured in one open store | Structured data; everything else lives somewhere else |
| Workloads | BI, engineering, streaming, ML and GenAI on one platform | BI and reporting; ML means copying data out |
| Storage format | Open Delta and Parquet you can take anywhere | Proprietary formats tied to the vendor |
| Cost behaviour | Compute and storage scale separately; pay for what runs | Capacity sized for the worst night of the year |
| Governance | Unity Catalog: one model across data, ML and AI assets | Mature for tables, absent for everything beyond them |
| When it wins | Engineering-led estates with ML, streaming or scale ambitions | Stable, purely relational reporting estates with no ML roadmap |
If your five-year picture includes machine learning, streaming or AI over your own data, the lakehouse is the defensible foundation, and the warehouse patterns you value survive inside it as gold-layer SQL. If your estate is genuinely stable relational reporting, a warehouse (including Fabric Warehouse) may be all you need, and we will tell you so before you spend a pound on migration.
FAQ
Frequently asked questions
Is Synapx a Databricks specialist?
Yes. We hold Microsoft Solutions Partner designations for Data & AI and Azure Infrastructure, and our engineers are certified on Databricks (Data Engineer, ML, Platform Admin). We deliver Databricks primarily on Azure, with experience on AWS where required.
Should we pick Databricks or Microsoft Fabric?
Databricks is our typical recommendation for heavy data engineering, large Spark workloads and production ML. Fabric is stronger for Power BI-centric estates and consolidated licensing. We are one of the few partners that delivers both, often side-by-side in the same estate.
How do you control Databricks costs?
We use cluster policies, job compute, spot instances, autoscaling, photon where it pays back, and Unity Catalogue usage analytics to understand spend by workload. Clients moving off legacy platforms typically see 30–50% total cost reduction.
Can Databricks handle our governance and compliance needs?
Yes. Unity Catalogue provides fine-grained access controls, row / column security, lineage and audit logging. We pair it with Azure policies, private networking and customer-managed keys for financial services, healthcare and public sector clients.
How long does a Databricks implementation take?
A governed Lakehouse foundation with the first workload live typically takes 8–12 weeks. Full migrations from Hadoop, Synapse or legacy warehouses usually run 4–9 months depending on pipeline count and complexity.
Can you run Databricks for us after delivery?
Yes. Synapx-as-a-Service covers platform operations, cluster and cost optimisation, Unity Catalogue administration and ongoing enhancement, with UK-based engineers who already know your estate.
Our Clients
Trusted by
Client Voices
Hear from our clients
Video Stories
David, Skanska
Project/Programme Manager
Mike, Mount Anvil
Head of Technology Applications
Testimonials
From the very first conversation, Synapx stood out. They didn't offer a one-size-fits-all solution; they listened, understood our workflows, and constructively challenged our assumptions. Since launch, the difference has been night and day: engagement is up, performance is dramatically better, and our teams now have a single, connected space for learning and communication. I'd recommend Synapx to any organisation looking to get more out of Microsoft technology.
Skanska
Project/Programme Manager
Working with Synapx has been transformative for Mount Anvil. They didn't feel like an external supplier; they became an extension of our team. Their technical expertise in implementing Microsoft Fabric solved our immediate challenges and positioned us for future growth, including AI and machine learning. What truly set Synapx apart was their transparency, commitment, and cost-optimisation through Xscale, making Fabric financially sustainable.
Mount Anvil
Head of Technology Applications
Synapx automated complex, manual processes, modernised legacy systems, and delivered scalable Power Platform solutions that streamline workflows and provide real-time insights. Their standout achievement was integrating SiteOps for accurate, real-time management of materials and compliance across multiple sites. Thanks to their future-proof architecture, we now have a robust digital ecosystem that empowers our teams.
Seras Energy Ltd
Head of Projects & Service Delivery
Exceptional communication. The final product exceeded our expectations.
The tool has streamlined our trade management process, reduced manual workloads and significantly improved efficiency and accuracy across our operations. What truly set Synapx apart was their exceptional communication, project management, and dedication to delivering excellence. They kept us informed at every stage, addressed challenges proactively, and ensured the final product exceeded our expectations.
Nuevo Partners UK LLP
Head of Operations
Their team delivered exactly what we needed, taking time to understand the purpose and specific requirements. By linking our Typeform online surveys with our Raisers Edge database, using Power Automate, they have enabled us to automate our incoming data processes. From our initial enquiry, scoping and delivery, we found them incredibly responsive and very good to communicate with.
The Ocean Conservation Trust
Head of Fundraising
Our go-to data partner. We recommend them to every one of our clients.
We work with Synapx as our go-to and trusted partner for anything Power BI, or data in general, so much so we recommend them to all our clients! The team at Synapx work with us in a way that makes it feel like they are part of our business, our 'data team'; projects and on-going support are executed easily and effortlessly.
Lanware
Chief Technology Officer
Talk Through Your Databricks Workloads
Bring your heaviest pipeline or the model that never made it to production. We will tell you honestly whether Databricks is the right home for it, and how we would run the build.
Speak to a Databricks Specialist