Migrating ETL Workflows to Azure Databricks: A Case Study

In this post, I’ll share my experience leading the migration of ETL workflows from legacy systems to Azure Databricks
at Zürich Insurance. This project presented unique challenges and opportunities for modernizing our data infrastructure.

Project Overview

The goal was to migrate existing ETL workflows from legacy systems to Azure Databricks, improving scalability,
maintainability, and performance. The migration involved multiple data sources and complex transformations.

Key Challenges

1. Legacy System Complexity

   • Complex SQL-based transformations
   • Multiple data source integrations
   • Custom scheduling mechanisms

2. Data Quality Assurance

   • Ensuring data consistency during migration
   • Validating transformation logic
   • Maintaining data lineage

3. Performance Optimization

   • Optimizing cluster configurations
   • Implementing efficient data processing patterns
   • Managing resource utilization

Solution Approach

1. Assessment and Planning

We began with a thorough assessment of the existing system:

• Documenting current workflows
• Identifying critical paths
• Mapping data dependencies

2. Architecture Design

The new architecture leveraged Azure Databricks’ capabilities:

• Delta Lake for reliable data storage
• Structured Streaming for real-time processing
• Unity Catalog for data governance

3. Migration Strategy

We adopted a phased approach:

1. Parallel development of new workflows
2. Incremental migration of existing processes
3. Validation and testing at each step
4. Gradual transition to production

Technical Implementation

Cluster Configuration

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# Example cluster configuration
{
    "name": "etl-cluster",
    "spark_version": "13.3.x-scala2.12",
    "node_type_id": "Standard_D4s_v5",
    "num_workers": 4,
    "autoscale": {
        "min_workers": 2,
        "max_workers": 8
    }
}

Delta Lake Implementation

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# Example Delta table creation
from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("ETL Pipeline") \
    .config("spark.jars.packages", "io.delta:delta-core_2.12:2.4.0") \
    .getOrCreate()

# Create Delta table
spark.sql("""
    CREATE TABLE IF NOT EXISTS silver.transactions
    USING DELTA
    PARTITIONED BY (date)
    AS SELECT * FROM bronze.transactions
""")

Results and Benefits

1. Performance Improvements

   • 40% reduction in processing time
   • 60% improvement in resource utilization
   • Better scalability for growing data volumes

2. Operational Benefits

   • Simplified maintenance
   • Improved monitoring capabilities
   • Better error handling and recovery

3. Cost Optimization

   • Reduced infrastructure costs
   • Better resource allocation
   • Pay-per-use model benefits

Best Practices Learned

1. Data Quality

   • Implement comprehensive testing
   • Use Delta Lake for ACID transactions
   • Maintain data lineage

2. Performance

   • Optimize cluster configurations
   • Use appropriate caching strategies
   • Implement efficient partitioning

3. Monitoring

   • Set up comprehensive logging
   • Implement alerting mechanisms
   • Track key performance metrics

Conclusion

The migration to Azure Databricks significantly improved our data processing capabilities while reducing operational
complexity. The project demonstrated the importance of careful planning, phased implementation, and continuous
validation.

Next Steps

Looking forward, we’re exploring:

• Advanced analytics capabilities
• Machine learning integration
• Real-time processing scenarios

Would you like to learn more about specific aspects of the migration or discuss your own experiences with Azure
Databricks?