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Solutions → AWS Databricks

AWS Databricks Consulting

Design, deploy, and operate Databricks on AWS — integrated with S3, IAM, VPC networking, and the wider AWS data and ML ecosystem.

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Databricks
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Built for these industries

InsuranceHealthcareBanking & Financial ServicesRetailManufacturingAgricultureLogistics

Platform setup

What we set up and integrate

Platform setup

  • Workspace deployment
  • VPC & PrivateLink networking
  • Unity Catalog on AWS

Integration

  • Amazon S3 & Delta Lake
  • IAM roles & instance profiles
  • Glue, Redshift & EMR interoperability

Operations

  • Cost & usage monitoring
  • CloudWatch integration
  • CI/CD with GitHub Actions/CodePipeline

Methodology

Our delivery approach

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: AWS Databricks

AWS
AWS Sources
Tables
Views
Pipelines
AWS-Native Landing Zone & Governance
DatabricksDatabricks Lakehouse
Bronze
Silver
Gold
Unity Catalog · Data Quality · Observability
SQL · BI · AI · GenAI
Applications
AWS
Delta Lake
Unity Catalog
Terraform

What to expect

Migration challenges we plan for

1

VPC design & PrivateLink configuration

2

IAM role and instance-profile permission modelling

3

S3 bucket policy & cross-account access design

4

Interoperability with existing Glue/Redshift/EMR workloads

5

Reserved capacity & cost optimisation across accounts

Why Karsient

Why Karsient for AWS 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 AWS 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

Yes — we configure Unity Catalog and Delta Lake directly against your existing S3 buckets, preserving existing data without a forced copy.

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 →

Vertica → Databricks

Move Vertica analytical workloads to the Databricks Lakehouse.

Learn more →
Microsoft Azure

Azure Databricks

Deploy and operate Databricks natively on Microsoft Azure.

Learn more →

Ready when you are

Planning a AWS Databricks migration?

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

Schedule a Migration Assessment