Databricks Lakehouse Consulting: Open Architecture for Data + AI
Databricks has become the default data platform for multi-cloud enterprises and ML-intensive organizations. But building a production lakehouse — with proper Unity Catalog governance, optimized Delta tables, and CI/CD pipelines — requires engineers who've done it before. That talent is exceptionally scarce.
Lakehouse Architecture
Delta Lake, medallion pattern, multi-hop pipelines, partitioning strategy
Unity Catalog Governance
Centralized access control, lineage, audit, cross-workspace policies
Spark Optimization
Cluster tuning, query performance, cost management, autoscaling
MLOps on Databricks
MLflow, feature store, model serving, experiment tracking
The platform is powerful. The engineering gap is real.
Databricks gives you the tools to build a world-class data platform: Delta Lake for reliable storage, Unity Catalog for governance, Spark for distributed processing, MLflow for experiment tracking. But tools don't build architectures. Engineers build architectures.
The gap between running a Databricks notebook and operating a production lakehouse is enormous. Production means: medallion layers with proper schema evolution, Unity Catalog policies that enforce column-level access control, Spark jobs tuned for your data volumes and cluster economics, CI/CD pipelines that promote code from development through staging to production, and monitoring that catches data quality issues before they reach dashboards.
Xylity's consulting-led matching process identifies Databricks engineers with this production depth — verified through scenario-based assessment, not just certification badges. When your lakehouse needs architects who understand Delta optimization, Z-ordering, liquid clustering, and cost-per-query economics, our network delivers.
Databricks lakehouse consulting capabilities
Every capability below is staffed by pre-qualified Databricks engineers with verified production experience — matched to your cloud, your data volumes, and your use cases.
Lakehouse Architecture & Design
Medallion architecture (bronze/silver/gold), Delta table design, partitioning and clustering strategy, workspace topology, and storage layout. The structural decisions that determine whether your lakehouse performs at scale or collapses under production workloads.
Unity Catalog Implementation
Centralized governance across workspaces: metastore setup, catalog and schema structure, table and column-level access control, data lineage tracking, audit logging, and integration with identity providers. The governance layer enterprises require.
Data Pipeline Engineering
Delta Live Tables for declarative ETL, structured streaming for real-time ingestion, batch pipelines with proper error handling and dead letter queues. Ingesting from databases, APIs, files, and event streams into clean medallion layers.
Spark Performance & Cost Optimization
Cluster right-sizing, autoscaling policies, query optimization, join strategies, caching, and photon engine tuning. The difference between a Databricks deployment that's cost-effective and one that burns through compute budget without proportional value.
MLOps & Feature Engineering
MLflow experiment tracking, model registry, feature store integration, model serving endpoints, and A/B testing infrastructure. The bridge between your lakehouse data and production AI applications.
Migration to Databricks
From legacy warehouses (Teradata, Oracle, SQL Server), cloud services (Redshift, BigQuery, Synapse), and Hadoop. Schema mapping, data validation, pipeline conversion, and parallel-run testing. The most common path to lakehouse adoption.
See data warehousing →Databricks on every major cloud — specialists for each
Azure Databricks
ADLS Gen2 integration, Azure DevOps CI/CD, Entra ID, Synapse migration
AWS Databricks
S3 storage, Glue Catalog integration, Redshift migration, IAM policies
GCP Databricks
GCS storage, BigQuery integration, Vertex AI connectivity
Multi-Cloud
Cross-cloud lakehouse patterns, Delta Sharing, Unity Catalog federation
Delta Lake
ACID transactions, time travel, schema evolution, Z-ordering, liquid clustering
SQL Warehouses
Serverless SQL, BI integration, JDBC/ODBC connectivity, query federation
MLflow
Experiment tracking, model registry, model serving, feature store
Delta Live Tables
Declarative ETL, expectations/quality rules, auto-scaling, streaming support
Databricks, Fabric, or both?
The right platform follows your architecture strategy. Xylity consults on this decision and provides specialists for both.
Choose Databricks when...
Multi-cloud: You operate on AWS, GCP, or a hybrid multi-cloud strategy
Open-source first: You value open formats (Delta, Iceberg, Hudi) and the Spark ecosystem
ML-heavy: Data science and MLOps are primary workloads, not afterthoughts
Advanced governance: Unity Catalog's cross-workspace, cross-cloud governance fits your model
Choose Fabric when...
Microsoft commitment: Your org is deep in M365, Azure, Power BI, Dynamics 365
Unified SaaS: You want one managed platform for DE, warehousing, and BI
Direct Lake: Power BI at lakehouse scale without import/refresh is a priority
Simpler governance: You prefer Microsoft-managed governance integrated with Purview
See Fabric consulting →Pre-qualified Databricks engineers, matched to your cloud and use case
Platform Discovery
We map your current data stack, cloud provider, migration targets, and Databricks adoption goals. Matching starts from your architecture — not a generic profile database.
Cloud-Specific Matching
Databricks engineers matched for your cloud: AWS, Azure, or GCP. Unity Catalog experience, Delta optimization skills, and domain knowledge verified through scenario assessment.
Production Evaluation
Candidates demonstrate lakehouse expertise through real scenarios: medallion design trade-offs, Spark job optimization, Unity Catalog policy design. 92% pass your screen on first match.
Deploy & Scale
Your Databricks engineer contributes from week one. As workloads expand — from data engineering to ML to production AI — Xylity scales the team across specializations.
Databricks expertise for enterprises and delivery partners
Building a lakehouse but can't find engineers with production Databricks depth?
Databricks engineers with real Unity Catalog, Delta optimization, and multi-cloud experience are among the hardest roles to fill. Xylity matches pre-qualified Databricks specialists who've operated production lakehouses — not just completed training courses. Companies of 500-10,000 employees trust our consulting-led process for this specialized talent.
Start a Consulting Engagement →Client chose Databricks but your bench is thin on lakehouse skills?
Databricks projects require specific expertise your generalist developers may not have. When a client needs Delta Lake architecture, Unity Catalog governance, or Spark optimization — Xylity delivers curated profiles in days. IT services companies of 20-1,000 employees use Xylity to staff Databricks engagements with confidence.
Scale Your Data Delivery →Databricks consulting — answered
Your lakehouse deserves engineers
who've built production ones before.
Tell us about your Databricks goals. We'll match pre-qualified engineers with verified lakehouse production experience — in an average of 4.3 days.