Skip to content

Solutions → Vertica → Databricks

Vertica to Databricks Migration Consulting

Migrate Vertica's analytical database workloads — schemas, projections, and heavy analytical SQL — onto a Databricks Lakehouse architecture built for both BI and AI.

→
Databricks
Book a Migration AssessmentTalk to a Databricks Architect

Built for these industries

InsuranceHealthcareBanking & Financial ServicesRetailManufacturingAgricultureLogistics

Why migrate

Why companies move from Vertica to Databricks

We don't tell clients Vertica is a bad platform — most migrations are driven by a specific need Databricks fits better.

Unifying data engineering, analytics and AI on one platform

Building a Lakehouse architecture on open formats

Advanced ML/AI workloads that need direct data access

Open data formats such as Delta and Parquet

Centralized governance with Unity Catalog

Streaming and batch workloads on one engine

Existing Databricks investment elsewhere in the business

Consolidating multiple data platforms into one

Scope

What Karsient migrates

Data

  • Tables & schemas
  • Projections & sort orders
  • Historical partitions

SQL

  • Analytical SQL & window functions
  • Stored procedures
  • User-defined functions

Pipelines

  • Batch load jobs
  • COPY-based ingestion
  • Scheduled ELT

Access

  • Roles & grants
  • Resource pools
  • Workload management policies

Methodology

Our migration methodology

01

Discovery

Understand business goals, current systems, and constraints before proposing anything.

02

Assessment

Inventory databases, tables, pipelines, workloads, dependencies and usage across the estate.

03

Architecture

Design the target Databricks Lakehouse architecture, Unity Catalog, and governance model.

04

Design

Define detailed technical design — data models, pipeline patterns, and platform standards.

05

Migration

Move data, SQL, transformations and pipelines in planned, testable phases.

06

Engineering

Build and re-engineer pipelines, jobs, and integrations on the target platform.

07

Validation

Compare record counts, aggregations, business rules, data quality and performance against source.

08

Optimization

Tune file sizes, partitioning/clustering, SQL, compute, and Delta table configuration.

09

Deployment

Move production traffic with a rollback plan and minimal disruption to downstream consumers.

10

Operate

Hand over with documentation and training, or continue under a managed-services partnership.

Architecture

Target architecture: Vertica → Databricks

Vertica
Tables
Views
Pipelines
Projection Redesign & Analytical Re-platforming
DatabricksDatabricks Lakehouse
Bronze
Silver
Gold
Unity Catalog · Data Quality · Observability
SQL · BI · AI · GenAI
Applications
SQL
Delta Lake
Apache Spark
Unity Catalog

What to expect

Migration challenges we plan for

1

Re-designing Vertica projections as Delta clustering/partitioning

2

Vertica-specific SQL syntax and function conversion

3

Resource-pool & workload management equivalents

4

COPY-based ingestion re-platforming

5

Query performance parity on distributed compute

6

Validating analytical query outputs at scale

Why Karsient

Why Karsient for Vertica → Databricks

We don't simply move workloads — we redesign the platform for Databricks, combining architecture expertise, data engineering delivery, migration engineering, and AI/MLOps integration under one team.

Architecture expertise

Data engineering

Migration engineering

AI/ML integration

Migration assessment

Is Vertica → Databricks right for your organization?

Get a technical assessment covering architecture, workloads, migration complexity, dependencies, risks and optimization opportunities.

Get a Free Migration Assessment

FAQ

Common questions

Delta Lake's Z-ordering, liquid clustering, and file-layout optimisation serve a similar performance role, tuned per table based on query patterns.

Related

Other Databricks solutions

Snowflake

Snowflake → Databricks

Structured migration of Snowflake workloads to the Databricks Lakehouse.

Learn more →
Talend

Talend → Databricks

Re-platform Talend ETL jobs onto native Databricks pipelines.

Learn more →
Microsoft Azure

Azure Databricks

Deploy and operate Databricks natively on Microsoft Azure.

Learn more →
AWS

AWS Databricks

Deploy and operate Databricks natively on Amazon Web Services.

Learn more →

Ready when you are

Planning a Vertica → Databricks migration?

Let's assess your current platform, identify migration risks and design your target Databricks architecture.

Schedule a Migration Assessment