Data Engineering

Data Lakehouse Architecture - The Best of Data Lake and Data Warehouse in One

Numlytics designs expert data lakehouse architectures for enterprises across the US, UK, Australia & UAE. We build unified data platforms on Microsoft Fabric, Databricks, and Delta Lake, using medallion architecture (Bronze → Silver → Gold) to deliver governed, cost-efficient storage with a BI-ready serving layer. One platform. All your workloads.

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Medallion architecture (Bronze → Silver → Gold) as standard
Microsoft Fabric, Databricks & Delta Lake certified engineers
Unified storage for BI, ML, and streaming workloads
Up to 50% lower cost vs US/UK data engineering firms
Delivery Facts
60%
Avg. storage cost reduction
vs traditional DW + data lake
1
Unified platform for BI,
ML & streaming
4wk
First Gold layer live
within 4 weeks
50%
Lower cost vs US/UK
engineering firms
We build on
Microsoft Fabric
Databricks
Delta Lake
Apache Iceberg
Azure Data Lake Gen2
Apache Spark
dbt
Power BI
What We Build

One Platform for Storage,
Analytics, and AI Workloads

Traditional data architectures force a choice: a data lake for raw storage and ML workloads, or a data warehouse for governed BI queries. Most organisations end up with both - duplicating data, doubling infrastructure costs, and creating synchronisation problems between two systems that should be one.

The data lakehouse removes that trade-off. By combining open table formats like Delta Lake or Apache Iceberg with ACID transactions, schema enforcement, and SQL query engines, a lakehouse gives you the cost efficiency and flexibility of a data lake with the performance, governance, and reliability of a data warehouse - on a single platform.

Our data lakehouse architecture service designs and implements this platform on Microsoft Fabric, Databricks, or Azure - using the medallion architecture pattern to deliver clean, governed, BI-ready data from a single unified storage layer.

Design Your Lakehouse →
Why Clients Come to Us
"We're paying for both a data lake and a data warehouse"
Data duplicated across S3 and Snowflake, or ADLS and Synapse. Two sets of pipelines. Two sets of bills. A lakehouse consolidates them into one - with the capabilities of both.
"Our ML team can't access the data the BI team uses"
Data scientists working from a raw data lake on different data than what the dashboards show. No shared semantic layer, no consistent feature store, no unified governance.
"We're adopting Microsoft Fabric and don't know where to start"
Fabric's OneLake, Lakehouse, and data engineering capabilities are powerful but complex to architect correctly. Most organisations need a design blueprint before they start building.
"Our data lake has become a data swamp"
Years of raw data accumulated with no schema, no governance, no cataloguing, and no reliable way to query it. The lake was built for storage, not for analytics.
What We Deliver

Six Components of Your Data Lakehouse Architecture

Every lakehouse we design is built across these six components - from the storage layer through to the BI-ready serving tier.

Platform Design & Architecture Blueprint

We design your end-to-end lakehouse architecture - platform selection (Microsoft Fabric vs Databricks), storage account structure, compute layer, security zones, and workspace design, before any infrastructure is provisioned.

Platform selection & architecture design
Storage zone & workspace structure
Compute & cost architecture
Medallion Architecture Implementation

Full medallion architecture design and implementation, Bronze (raw ingestion), Silver (cleansed and conformed), and Gold (business-ready aggregates). Each layer defined with clear data contracts, quality rules, and access controls.

Bronze → Silver → Gold layer design
Data contracts per layer boundary
Quality rules & validation at each tier
Data Ingestion & Pipeline Layer

The ingestion pipelines that land raw data into your Bronze layer - batch, incremental, and streaming, built in Azure Data Factory, Databricks, or Fabric Data Factory with full monitoring, error handling, and schema evolution support.

Batch & incremental ingestion patterns
Schema evolution & schema-on-read handling
CDC & streaming ingestion
Transformation & Gold Layer (dbt / Spark)

Silver-to-Gold transformations using dbt or Spark - building the dimensional models, aggregates, and business-logic layers that power your BI tools and ML feature pipelines from the Gold tier of the lakehouse.

dbt models for Gold layer
Spark transformations for large-scale jobs
ML feature store design
BI Serving Layer & Semantic Model

The serving layer that connects your Gold tier to Power BI, a semantic model with business metric definitions, DAX calculations, row-level security, and query optimisation so your dashboards perform at sub-second speed on top of the lakehouse.

Power BI semantic model on Gold layer
DirectLake mode configuration (Fabric)
Row-level security & certified datasets
Governance, Cataloguing & Unity Catalog

Lakehouse governance implemented via Databricks Unity Catalog or Microsoft Purview - data cataloguing, lineage tracking, access control at table/column level, and audit logging across every layer of the lakehouse.

Unity Catalog or Purview implementation
Column-level access controls
Data lineage & audit logging
Medallion Architecture — How Data Flows
Bronze Layer
Raw Ingestion

Raw data landed as-is from source systems, no transformation, no business logic. Full historical append. Schema preserved from source.

Parquet / Delta Schema-on-read Full history
Silver Layer
Cleansed & Conformed

Validated, deduplicated, and conformed data. Business rules applied. Data types enforced. Ready for cross-domain joins and ML feature engineering.

Delta Lake ACID transactions Quality validated
Gold Layer
Business-Ready Aggregates

Dimensional models, KPI aggregates, and business-domain tables optimised for Power BI DirectLake, SQL analytics, and ML feature consumption.

Star schema DirectLake ready Governed
How We Deliver It

From Architecture Blueprint to BI-Ready Lakehouse in 4 Phases

First Gold layer live in 4 weeks. Sprint-based delivery with weekly demos - every sprint adds a tested, documented lakehouse increment.

Discovery & Architecture Design

Data landscape audit, workload requirements (BI, ML, streaming), and platform selection. Full lakehouse architecture blueprint - storage zones, compute, medallion layer design, governance model, documented before build begins.

⏱ Weeks 1–2
Foundation Build

Platform provisioning, Bronze layer pipelines, Delta Lake / OneLake configuration, Unity Catalog or Purview setup, and workspace structure. The foundation all subsequent sprints build upon - secure, governed, monitored.

⏱ Weeks 2–4
Silver & Gold Layer Sprints

Weekly sprints building Silver cleansing layer and Gold dimensional models - each sprint delivering a tested data domain. dbt models, quality validation, and Power BI semantic model built and validated each sprint.

⏱ Weeks 4 onwards
Handover & Knowledge Transfer

Full documentation - architecture diagrams, data flow docs, dbt model documentation, runbooks. Team training on the lakehouse platform, medallion pattern, and governance tooling. Your team owns and extends it from day one.

⏱ Final sprint
Why Numlytics

Why Choose Numlytics for Data Lakehouse Architecture

We've designed and built lakehouse architectures on Microsoft Fabric and Databricks for enterprises across the US, UK, and Australia.

Certified on Fabric, Databricks & Delta Lake
Every architect is certified on the platforms they design - DP-600 Microsoft Fabric, Databricks Certified Data Engineer, and dbt Developer. Not generalists reading documentation on your project.
Medallion Architecture Applied Correctly
Medallion is a pattern, not a template. We design each layer with the right data contracts, quality rules, and access controls for your specific workloads, not a generic Bronze/Silver/Gold that becomes a mess in production.
Platform-Agnostic Recommendation
We work across Microsoft Fabric, Databricks, Azure Data Lake, and Snowflake. Our recommendation is based on your workloads, stack, and team, not vendor relationships. If Fabric is right, we say Fabric. If Databricks is right, we say Databricks.
BI-Ready From the Gold Layer Up
We design the Gold layer and Power BI semantic model together, so your dashboards run on DirectLake mode (Fabric) or optimised Databricks SQL, not slow on-demand queries that frustrate business users.
ML & BI on the Same Platform
We design lakehouses that serve both BI and ML workloads from the same Silver and Gold layers - with feature store patterns built into the architecture so your data scientists and BI teams work from the same governed data.
Up to 50% Lower Cost
Certified offshore data engineers from India, same lakehouse design depth as US or UK engineering firms at up to 50% lower cost. Full timezone overlap, daily standups, and Slack access throughout the engagement.
★★★★★

"We had a data lake in ADLS with three years of raw data that nobody could reliably query. The data science team was working from different data than the BI team, and reconciling them was a weekly argument. Numlytics designed a medallion architecture on Microsoft Fabric, migrated our existing pipelines, built the Silver cleansing layer, and delivered a Gold dimensional model with DirectLake Power BI on top. For the first time, our ML engineers and BI team are working from the same governed data. Query times on our main dashboards dropped from 4 minutes to under 10 seconds."

AM
Andrew M.
Head of Data Platform · Technology Company, Australia

The lakehouse is your platform. These services build on top of it.

ETL Pipeline Development
The ingestion pipelines that populate your Bronze layer
→
Data Warehouse Consulting
When a structured DW is the right fit over a lakehouse
→
Real-Time Data Streaming
Stream data into your Bronze layer in near real-time
→
Data Quality Management
Quality validation at every medallion layer boundary
→
All Data Engineering Services ↗
Full data engineering service hub
→
FAQ

Data Lakehouse Architecture FAQs

Common questions before starting a lakehouse architecture engagement with Numlytics.

Ask Us Anything →
A data lakehouse combines the cost-efficient, scalable storage of a data lake with the ACID transactions, schema enforcement, and SQL performance of a data warehouse - on a single platform. Using open table formats like Delta Lake or Apache Iceberg, a lakehouse brings data warehouse reliability directly to object storage - eliminating the need to maintain separate lake and warehouse systems.
Medallion architecture organises lakehouse data into three layers:Bronze (raw data ingested as-is), Silver (cleansed, validated, and conformed with business rules applied), and Gold (aggregated, business-ready data optimised for BI and ML). Each layer has clear data quality standards, access controls, and data contracts - making the lakehouse governable and maintainable at scale.
On the Microsoft stack, >Microsoft Fabric is usually the best fit — OneLake unified storage, native Power BI DirectLake integration, and tight Azure AD security. Databricks with Delta Lake and Unity Catalog suits organisations with heavy ML/AI workloads, multi-cloud requirements, or existing Spark expertise. We evaluate your requirements and recommend accordingly - with no vendor incentives. See our data engineering services for the full picture.
A data warehouse is optimised for SQL queries and BI — great performance but expensive for large raw data volumes and limited for ML. A data lake stores all data cheaply but historically lacked ACID transactions and reliable SQL performance. A data lakehouse combines both: low-cost object storage with ACID transactions, schema enforcement, and SQL performance comparable to a warehouse - plus ML and streaming workload support on the same platform.
Yes — data lake to lakehouse migration is one of our most common engagements. We assess your existing lake structure, design the target medallion architecture, implement the governance layer (Unity Catalog or Purview), and migrate your pipelines incrementally - with zero downtime and full data validation at each stage.
Ready to Start?

One Platform. All Your Data. BI, ML & Streaming Unified.

Get expert data lakehouse architecture - medallion design, Microsoft Fabric or Databricks implementation, Gold layer, and BI-ready semantic model. Certified engineers. Proposal in 24 hours. US, UK, Australia & UAE.

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Other Data Engineering Services
ETL Pipeline Development Data Warehouse Consulting Real-Time Data Streaming Data Quality Management Business Intelligence