Databricks Partnership
AverIQ × Databricks.
Select Tier. Certified Deep.
AverIQ is a certified Databricks Select Partner — meaning our practitioners are credentialed, our methodologies are validated, and your implementation comes with access to Databricks engineering escalation paths and roadmap previews.
Select Partner
What We Deliver on Databricks
Medallion Architecture
Bronze → Silver → Gold. Structured data quality layers from raw ingestion to analytics-ready Gold tables.
Delta Live Tables
Declarative ETL pipelines with built-in data quality expectations and automatic dependency resolution.
Unity Catalog
Unified governance across data and AI assets. RBAC, lineage, and audit logs built into every engagement.
MLflow & AI Gateway
End-to-end ML lifecycle management. Model tracking, versioning, and governed AI deployment from day one.
The Old World
Data lake
Stores everything raw
Hard to query / analyze
Hard to query / analyze
vs
Data warehouse
Fast queries, clean data
Expensive, rigid schema
Expensive, rigid schema
Companies needed BOTH — so they paid for both. Fragile. Expensive. Slow.
The Lakehouse — Best of Both
Lakehouse
Open storage
All data types kept
Raw, structured,
unstructured, images
Apache Iceberg tables
Raw, structured,
unstructured, images
Apache Iceberg tables
Smart layer
ACID transactions
Schema enforcement
Governance & catalog
Time travel / versioning
Schema enforcement
Governance & catalog
Time travel / versioning
BI + SQL speed
BI dashboards
Ad-hoc SQL queries
Low latency reads
Serverless compute
Ad-hoc SQL queries
Low latency reads
Serverless compute
One platform. No copies. No sync jobs. No silos.
What Databricks Adds on Top
Unity Catalog
Single governance layer across all data & AI assets
MLflow
Track experiments
Version models
Deploy & monitor
Version models
Deploy & monitor
Delta Live Tables
Declarative pipelines
Streaming + batch
Auto quality checks
Streaming + batch
Auto quality checks
Model Serving & DBRX
Host OSS LLMs
Inference at scale
On your own data
Inference at scale
On your own data
Why This Is Critical for Enterprise AI
Clean data
One truth
→
Feature store
→
Train model
→
Serve inference
→
Monitor & retrain
All steps governed, versioned, and audited — inside one platform, on YOUR data
No lock-in
Apache Iceberg + open APIs. Swap vendors anytime.
AI needs data gravity
Models train best when data + compute co-locate.
Compliance-ready
Lineage, access control, audit trail — out of box.