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DB2 to BigQuery Migration

Replace DataStage ETL with edge-native processing that runs on or near your data center.

The Problem​

Your organization has spent years building ETL pipelines in DataStage:

  • DB2 databases with decades of transaction data
  • Complex transformation logic embedded in proprietary stages
  • Nightly batch jobs moving data to cloud analytics

The challenge: Migrating to GCP/BigQuery means rewriting everything—or paying for DataStage Cloud licenses forever.

The Solution: 6 Edge-Native Transformations​

This pipeline replaces DataStage with Expanso processors that run at the edge:

1. Add Lineage Metadata → Audit Trail​

  • DataStage: Custom annotations or external logging
  • Expanso: Automatic lineage injection with source tracking
  • Result: Complete audit trail for compliance

2. Normalize Currency → USD Conversion​

  • DataStage: Lookup Stage + Transformer
  • Expanso: branch + mapping with inline rates
  • Result: All amounts in USD with originals preserved

3. Mask Account Numbers → PCI Compliance​

  • DataStage: Transformer with custom routines
  • Expanso: mapping with slice/hash functions
  • Result: Last 4 digits visible, full number hashed for joins

4. Categorize Transactions → MCC Mapping​

  • DataStage: Switch/Case or lookup table
  • Expanso: match expression with pattern matching
  • Result: Human-readable categories for analytics

5. Standardize Schema → BigQuery Format​

  • DataStage: Transformer with field mapping
  • Expanso: mapping with field assignment
  • Result: Clean, lowercase field names for BigQuery

6. Validate Before Load → Data Quality​

  • DataStage: Data Rules Stage
  • Expanso: mapping with conditional throw
  • Result: Reject bad records before they hit BigQuery

Why Process at the Edge?​

🔒 Data Sovereignty: Transform data before it leaves your data center ⚡ Reduced Egress: Send only clean, compressed data to GCP 📊 Real-time Audit: Lineage metadata generated at extraction time 💰 No License Fees: Replace per-CPU DataStage licensing with flat-rate edge nodes

What You'll Learn​

By the end of this guide, you'll be able to:

✅ Replace DataStage lookup stages with Expanso branch/mapping processors ✅ Add automatic lineage tracking for regulatory compliance ✅ Mask sensitive data at the edge before cloud transmission ✅ Deploy to edge nodes near your DB2 servers ✅ Schedule nightly migrations with production-ready error handling

Get Started​

Build the pipeline incrementally, understanding each DataStage replacement:

  1. Setup Guide - Prerequisites and environment
  2. Step 1: Add Lineage - Audit trail injection
  3. Step 2: Normalize Currency - Lookup replacement
  4. Step 3: Mask Accounts - PCI compliance
  5. Step 4: Categorize - MCC mapping
  6. Step 5: Standardize Schema - BigQuery format
  7. Step 6: Validate - Data quality gates

Option 2: Jump to Complete Pipeline​

Download the production-ready configuration:

→ Get Complete Pipeline

Who This Guide Is For​

  • Data Engineers replacing DataStage ETL jobs
  • Cloud Architects planning DB2 → BigQuery migrations
  • Compliance Teams needing audit trails for financial data
  • Platform Teams modernizing legacy ETL infrastructure

Prerequisites​

  • DB2 database with ODBC connectivity
  • GCP project with BigQuery access
  • Expanso Edge installed on a node with DB2 network access
  • Basic familiarity with YAML and SQL

Time to Complete​

  • Step-by-Step Tutorial: 45-60 minutes
  • Quick Deploy: 10 minutes

Real-World Impact​

Before (DataStage):

  • License cost: $50K+/year per CPU
  • Deployment: Days to weeks for changes
  • Audit trail: Manual, incomplete

After (Expanso Edge):

  • License cost: Flat rate, unlimited nodes
  • Deployment: Minutes via CLI
  • Audit trail: Automatic, complete lineage