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RASMIN BHALLA
RASMIN BHALLA

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Migrating Legacy ETL to Modern Data Stack: Matillion dbt on Databricks

#matillion #dbt #databricks #dataenginee

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Modern data engineering is shifting from tool-driven ETL to code-first, modular pipelines. In this post, I’ll walk through how I migrated legacy Matillion workflows to a scalable architecture using dbt and Databricks.

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Problem Statement

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We had multiple Matillion mappings handling core business entities like:

  • Company
  • Department
  • Group
  • Class / Sub-Class
  • Supplier / Supplier Site
  • Barcode

Challenges:

  • Tight coupling between jobs
  • Limited reusability
  • Difficult debugging and lineage tracking
  • Inconsistent data quality validation

🏗️ Target Architecture

We redesigned the system using a medallion architecture, where data flows through multiple refinement layers:

  • Bronze → Raw ingestion
  • Silver → Cleaned & validated data
  • Gold → Business-ready datasets

This layered approach improves data quality progressively as it moves downstream ([Databricks Documentation][1]).

🔄 Migration Strategy

1. Decomposing Matillion Mappings

Each Matillion job was broken down into:

  • Source extraction
  • Joins & filters
  • Aggregations

Then rewritten as modular dbt models.

🧱 Layered Modeling Approach

  • Staging (stg_*) → Raw cleanup
  • Intermediate (int_*) → Business logic reuse
  • Marts (dim_*, fct_*) → Analytics-ready tables

Example:

stg_supplier → int_supplier_enriched → dim_supplier
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⚡ Incremental Processing

Instead of full refresh pipelines:

  • Used updated_at based filtering
  • Applied incremental models

👉 Result: Reduced compute cost and faster execution

🧪 Data Validation Strategy (Critical Step)

Ensuring parity with production was the most critical step.

✔️ Validation Techniques

  • Row count validation
  • Aggregation checks (SUM, COUNT)
  • Sample-level validation
  • Hash-based comparison

✅ Data Quality Framework in dbt

Implemented both standard and custom tests:

  • Not Null
  • Unique
  • Relationships (FK integrity)
  • Accepted Values
  • Freshness checks

⚡ Performance Optimization

  • Incremental models for large tables
  • Partitioning (Delta tables)
  • Optimized joins

🔍 Key Challenges

1. Hidden Dependencies

Solved using dbt DAG (ref())

2. Data Mismatch

Resolved via structured reconciliation

3. Job Variables

Converted into dbt macros

📊 Outcome

✔ Improved maintainability
✔ Standardized SQL transformations
✔ Strong data quality enforcement
✔ Reduced runtime and cost
✔ Clear lineage and traceability

💡 Key Takeaway

This migration wasn’t just tool replacement—it was a shift to:

👉 Modular data engineering
👉 Version-controlled transformations
👉 Reliable, testable pipelines

👋 Final Thoughts

If you're still using legacy ETL tools, moving to dbt can drastically improve:

  • Development speed
  • Debugging
  • Data trust Happy to discuss dbt + Databricks architectures or migration strategies!

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