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Solutions → Talend → Databricks

Talend to Databricks Migration Consulting

Retire Talend licensing and job-server overhead by re-platforming your ETL pipelines onto native Databricks jobs and Delta Live Tables — with the same data contracts and SLAs.

Talend
→
Databricks
Book a Migration AssessmentTalk to a Databricks Architect

Built for these industries

InsuranceHealthcareBanking & Financial ServicesRetailManufacturingAgricultureLogistics

Why migrate

Why companies move from Talend to Databricks

We don't tell clients Talend 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

Jobs

  • Talend ETL jobs
  • Job orchestration & scheduling
  • Reusable job components

Data

  • Source & target connections
  • Staging tables
  • Historical loads

Transformations

  • tMap & transformation logic
  • Data quality routines
  • Custom Java routines

Operations

  • Monitoring & alerting
  • Error handling & retries
  • Logging & audit trails

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: Talend → Databricks

Talend
Talend
Tables
Views
Pipelines
Job Re-engineering into Delta Live Tables
DatabricksDatabricks Lakehouse
Bronze
Silver
Gold
Unity Catalog · Data Quality · Observability
SQL · BI · AI · GenAI
Applications
Talend
Apache Kafka
Delta Lake
Python
Unity Catalog

What to expect

Migration challenges we plan for

1

Translating tMap logic into Spark-native transformations

2

Re-implementing custom Java routines

3

Job orchestration & scheduling parity

4

Data quality rule migration

5

Error handling & retry logic differences

6

Connector & source-system compatibility

7

Performance re-tuning for distributed compute

8

Parallel-run validation against Talend outputs

Why Karsient

Why Karsient for Talend → 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 Talend → 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

No — many jobs map cleanly to Databricks notebooks or Delta Live Tables pipelines; we prioritise by complexity and business criticality rather than a blanket rewrite.

Related

Other Databricks solutions

Snowflake

Snowflake → Databricks

Structured migration of Snowflake workloads to the Databricks Lakehouse.

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 →
AWS

AWS Databricks

Deploy and operate Databricks natively on Amazon Web Services.

Learn more →

Ready when you are

Planning a Talend → Databricks migration?

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

Schedule a Migration Assessment