Hope you have read Part 1 here

https://warehows.ai/blog/the-complete-guide-to-migrating-from-talend-to-dbt-why-2026-is-the-year-to-make-the-switch

Phase 3: Component Migration

This is where Talend expertise becomes essential. Each Talend component maps to specific dbt patterns:

tMap → SQL Joins and CTEs

Talend's visual tMap becomes explicit SQL. This is actually an improvement—the logic is visible, testable, and version-controlled.

Talend tMap with multiple lookups:

Main flow: orders
Lookup 1: customers (on customer_id)
Lookup 2: products (on product_id)
Filter: order_status = 'completed'

dbt model equivalent:

with orders as (
    select * from {{ ref('stg_orders') }}
    where order_status = 'completed'
),

customers as (
    select * from {{ ref('stg_customers') }}
),

products as (
    select * from {{ ref('stg_products') }}
)

select
    o.order_id,
    o.order_date,
    c.customer_name,
    c.customer_segment,
    p.product_name,
    p.category,
    o.quantity,
    o.amount
from orders o
left join customers c on o.customer_id = c.customer_id
left join products p on

tFlowToIterate → dbt Macros or Orchestration

Talend's looping constructs require different approaches depending on the use case.

For data-driven iteration (processing each row), use dbt macros with Jinja loops or refactor to set-based SQL operations.

For job orchestration (running jobs in sequence), use external orchestration tools like Airflow, Dagster, or dbt Cloud's built-in scheduling.

tDBInput → Source Definitions

Talend's database input components become dbt source definitions:

# models/staging/sources.yml
sources:
  - name: raw_sales
    database: analytics
    schema: raw
    tables:
      - name: orders
        description: Raw order transactions
        columns:
          - name: order_id
            tests

Then reference in your models:

select * from {{ source('raw_sales', 'orders') }}

tAggregate → SQL GROUP BY

Talend tAggregate:

Group by: customer_id, order_month
Operations: sum(amount), count(order_id)

dbt model:

select
    customer_id,
    date_trunc('month', order_date) as order_month,
    sum(amount) as total_amount,
    count(order_id) as order_count
from {{ ref('stg_orders') }}
group by 1, 2

Context Variables → dbt Variables and Environment Configs

Talend's context groups become dbt variables:

# dbt_project.yml
vars:
  start_date: '2024-01-01'
  default_currency: 'USD'

Referenced in models:

where order_date >= '{{ var("start_date") }}'

Phase 4: Testing and Validation

Implement dbt tests. At minimum, every model should have:

models:
  - name: fct_orders
    columns:
      - name: order_id
        tests:
          - unique
          - not_null
      - name: customer_id
        tests:
          - not_null
          - relationships:
              to: ref('dim_customers')
              field

Run parallel validation. During migration, run both Talend jobs and dbt models against the same source data. Compare outputs row-by-row. Any differences need investigation—they might reveal bugs in the legacy system that you can fix during migration.

Performance testing. dbt models running on modern cloud warehouses typically outperform Talend jobs significantly. The Macif case study showed pipeline runtime dropping from over two hours to under five minutes. Verify you see similar improvements.

Phase 5: Cutover and Optimization

Plan your cutover carefully. Options include:

• Big bang: Switch everything at once (higher risk, faster completion)

• Parallel running: Run both systems simultaneously (lower risk, higher cost)

• Phased migration: Migrate domain by domain (balanced approach)

Optimize post-migration. With dbt's visibility into model dependencies and run times, identify optimization opportunities:

• Consolidate redundant transformations

• Implement incremental models for large tables

• Create reusable macros for common patterns

How Warehows Analytics Can Help

Our team has deep expertise in both Talend and dbt. We've worked inside Talend implementations—we know the tMap complexity, the context variable sprawl, the scheduling dependencies. And we've built production dbt projects on Snowflake, BigQuery, and Databricks.

We offer:

• Migration assessment: Evaluate your Talend environment and provide a detailed migration roadmap with effort estimates

• Hands-on migration: Our engineers work alongside your team to execute the migration

• Training and enablement: Get your team productive with dbt quickly

• Ongoing support: Post-migration optimization and best practices

As official dbt partners, we bring both the technical expertise and the methodology to make your migration successful.