Cloud Data Platform Migration

Databricks Consulting For Lakehouse-Scale Analytics & AI

Our Databricks consulting services help enterprises design, migrate, and optimize Lakehouse architectures on Databricks - combining Apache Spark performance tuning, Delta Lake reliability, and Unity Catalog governance. We build production-grade pipelines and MLflow ML workflows that scale, at up to 50% lower delivery cost than in-house teams.

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4-6 week Lakehouse migration delivery
Databricks-certified Spark & MLflow engineers
Unity Catalog governance built-in from day one
50% lower cost than US/UK in-house hiring
Delivery Snapshot
4-6wk
Average Lakehouse migration timeline
40%
Average Spark job runtime reduction
100+
Pipelines migrated to Delta Lake
50%
Lower cost vs in-house Databricks team
Tech Stack Databricks Lakehouse Apache Spark Delta Lake Unity Catalog MLflow PySpark / Python Delta Live Tables Azure / AWS / GCP Workflows / Jobs Power BI / Tableau
What We Cover

A Lakehouse Platform That Unifies Data, Analytics & AI

Most enterprises run fragmented data warehouses, ad-hoc Spark clusters, and siloed ML notebooks - leading to duplicated pipelines and stale dashboards. Our Databricks consulting team rebuilds this foundation as a single Lakehouse architecture on Delta Lake.

We design medallion-layer pipelines (Bronze/Silver/Gold), tune Apache Spark jobs for cost and speed, and implement Unity Catalog for unified governance across workspaces, clouds, and teams.

From Delta Live Tables ingestion to MLflow-tracked model deployment, every component is built for production reliability - not just proof-of-concept notebooks.

Common Challenges We Solve
"Our Spark jobs are slow and expensive and cluster costs keep climbing with no clear way to control them.
"We have raw data in S3 and Azure but no consistent transformation layer and no single source of truth.
"Our data scientists work in isolated notebooks and nothing they build ever makes it to production reliably.
"We bought Databricks licences but the platform is barely used beyond a few ad-hoc queries.
What We Deliver

End-to-End Databricks Lakehouse Services

From platform setup to production ML - six core service areas covering the full Databricks Lakehouse lifecycle.

01

Lakehouse Architecture Design

We design a medallion-layer Lakehouse on Delta Lake - Bronze, Silver, and Gold zones - replacing fragmented warehouses with a single governed source of truth.

Includes data modeling, storage layout, and partitioning strategy tailored to your workload patterns and cloud provider.

Medallion architecture blueprint
Cloud storage & partitioning design
Schema evolution strategy
02

Spark Performance Optimization

Our engineers tune Apache Spark jobs - partitioning, caching, AQE, and cluster sizing - to cut runtime and compute spend.

We profile slow jobs, rewrite inefficient transformations, and right-size clusters for cost-efficient scaling.

Job profiling & bottleneck analysis
Cluster sizing & autoscaling tuning
Cost-per-query reduction
03

Delta Live Tables Pipelines

We build declarative ingestion and transformation pipelines using Delta Live Tables, with built-in data quality expectations and lineage tracking.

Pipelines auto-handle schema drift, retries, and dependency orchestration -reducing manual pipeline maintenance.

Declarative ETL/ELT pipelines
Data quality expectations
Automated lineage & monitoring
04

Unity Catalog Governance

We implement Unity Catalog for centralized access control, data lineage, and auditing across all workspaces and clouds.

Includes role-based access policies, PII tagging, and cross-workspace data sharing setup.

Centralized access control
PII tagging & masking policies
Cross-workspace data sharing
05

MLflow & ML Pipeline Deployment

We operationalize machine learning with MLflow - experiment tracking, model registry, and CI/CD-driven deployment to production endpoints.

Built for reproducibility: every model version is tracked, validated, and rollback-ready.

Experiment tracking setup
Model registry & versioning
CI/CD model deployment
06

Workflow Orchestration & Migration

We migrate legacy ETL jobs (Airflow, ADF, SSIS) into Databricks Workflows, consolidating orchestration into a single control plane.

Includes job scheduling, dependency mapping, and alerting integration for production pipelines.

Legacy ETL migration
Workflow scheduling & dependencies
Alerting & monitoring integration
Our Approach

A Proven 4-Step Databricks Implementation Process

Structured, milestone-driven delivery - from assessment to production rollout, typically completed in 4-6 weeks.

01
Assess & Architect
Audit existing pipelines, data volumes, and Spark workloads. Design target Lakehouse architecture and migration plan.
⏱ Week 1
02
Build the Lakehouse
Stand up Delta Lake medallion layers, configure Unity Catalog, and provision Databricks workspaces and clusters.
⏱ Weeks 2-3
03
Migrate & Optimize
Migrate pipelines into Delta Live Tables and Workflows, then tune Spark jobs for performance and cost.
⏱ Weeks 4-5
04
Deploy & Enable
Roll out to production, deploy ML pipelines with MLflow, and enable BI tools and team training.
⏱ Week 6
Tools & Platforms We Work With
Databricks Lakehouse
Apache Spark
Delta Lake
Delta Live Tables
Unity Catalog
MLflow
PySpark
Databricks Workflows
Azure Databricks
AWS Databricks
Power BI
Tableau
Why Numlytics

Why Enterprises Choose Numlytics for Databricks Consulting

Databricks-Certified Engineers
Our team holds Databricks Certified Data Engineer and ML Associate credentials, ensuring best-practice implementations.
Performance-First Engineering
We don't just migrate - we optimize. Clients see measurable Spark runtime and cost reductions post-delivery.
Governance Built In
Unity Catalog access controls, lineage, and PII policies are part of every implementation - not an afterthought.
Production ML, Not Notebooks
We deploy MLflow-tracked models into CI/CD pipelines designed for real production traffic and rollback safety.
Global Delivery Experience
We've delivered Databricks projects for clients across the US, UK, Australia, and UAE - across finance, retail, and healthcare.
50% Lower Delivery Cost
Our offshore-augmented delivery model gives enterprises senior Databricks expertise at roughly half the cost of local hires.
★★★★★

Before working with Numlytics, our Databricks environment was a mess of ad-hoc notebooks and runaway cluster costs. Their team rebuilt our entire Lakehouse on Delta Lake with proper medallion layers, and within six weeks our Spark job costs dropped noticeably while query performance improved significantly. They also set up Unity Catalog so our data governance team finally has visibility across every workspace. The MLflow pipeline they deployed for our churn model now runs in production without the manual babysitting our old setup required. Communication was clear throughout, and they worked seamlessly across our US and offshore time zones.

JM
James Mitchell
VP of Data Platform, Retail Enterprise - United States
FAQs

Databricks Consulting Questions, Answered

Everything you need to know about working with our Databricks consulting team.

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Databricks consulting involves designing, building, and optimizing a Lakehouse data platform on Databricks - covering Delta Lake architecture, Spark performance tuning, Unity Catalog governance, and MLflow-based ML pipelines. It's ideal for enterprises running large-scale data and AI workloads who want a unified, governed platform instead of fragmented warehouses and notebooks.
Most Lakehouse implementations take 4-6 weeks from assessment to production rollout, depending on the number of pipelines being migrated and the complexity of existing data sources. Larger enterprise migrations with extensive legacy ETL may extend to 8-10 weeks.
We work with the full Databricks ecosystem - Apache Spark, Delta Lake, Delta Live Tables, Unity Catalog, MLflow, and Databricks Workflows - across Azure, AWS, and GCP. We also integrate with downstream BI tools like Power BI and Tableau for reporting.
Yes. Many of our engagements start as optimization projects - tuning Spark jobs, restructuring Delta tables, fixing cluster configurations, and adding Unity Catalog governance to an existing environment without a full rebuild. We assess your current setup first and recommend the minimal-disruption path.
Yes. We offer post-implementation support packages including monitoring, performance tuning, and pipeline maintenance. Many clients also transition into our managed analytics or dedicated team engagement models for ongoing Databricks operations.
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