Why Migrate from Netezza to BigQuery?

Organizations migrate from IBM Netezza to Google BigQuery to eliminate expensive hardware refresh cycles, reduce operational complexity, and leverage serverless scalability. Netezza's end-of-life status and high maintenance costs make cloud migration essential.

Key Migration Drivers

Hardware Costs: Eliminate $2M-$10M appliance refresh cycles
Operational Burden: Reduce DBA overhead by 80% with serverless architecture
Scalability: Auto-scale from GB to PB without capacity planning
Modern Analytics: Native ML, streaming, and data lake integration

Cost Comparison: Netezza vs BigQuery

Cost Component	Netezza (3 Years)	BigQuery (3 Years)	Savings
Hardware/Infrastructure	$4,500,000	$0	$4,500,000
Software Licenses	$1,800,000	$0	$1,800,000
Storage (100TB)	Included	$60,000	-
Compute (Queries)	Included	$450,000	-
DBA/Operations	$900,000	$180,000	$720,000
Maintenance/Support	$600,000	$0	$600,000
Total 3-Year TCO	$7,800,000	$690,000	$7,110,000 (91%)

* Based on 100TB data warehouse with 500 daily queries. Actual costs vary by usage patterns.

AI-Powered Migration Process

Discovery & Assessment (Week 1)

AI analyzes Netezza system catalog, query workload, and data distribution to create comprehensive migration plan with cost projections.

Automated schema extraction from _v_table, _v_column
Query log analysis from _v_qryhist
Data distribution and skew analysis

Schema & SQL Conversion (Weeks 2-3)

Automated conversion of Netezza DDL, NZPLSQL procedures, and SQL queries to BigQuery Standard SQL with 93% accuracy.

Distribution key to clustering/partitioning conversion
NZPLSQL to BigQuery scripting translation
Zone map to partition pruning optimization

Data Migration (Weeks 4-6)

Parallel bulk data transfer using external tables and Cloud Storage with automatic compression and format optimization.

Parallel unload to GCS using nz_backup or external tables
Automatic Parquet conversion for optimal BigQuery loading
Row-level validation and reconciliation

Testing & Cutover (Weeks 7-8)

Comprehensive query validation, performance testing, and zero-downtime cutover with instant rollback capability.

Automated query result comparison
Performance benchmarking and optimization
Phased application cutover with monitoring

Automated SQL Conversion Examples

Distribution Key to Clustering

Netezza DDL:

CREATE TABLE sales (
  sale_id INT,
  sale_date DATE,
  customer_id INT,
  amount DECIMAL(10,2)
)
DISTRIBUTE ON (customer_id);

BigQuery DDL:

CREATE TABLE sales (
  sale_id INT64,
  sale_date DATE,
  customer_id INT64,
  amount NUMERIC(10,2)
)
CLUSTER BY customer_id;

NZPLSQL to BigQuery Scripting

Netezza NZPLSQL:

CREATE PROCEDURE update_totals()
RETURNS BOOLEAN
LANGUAGE NZPLSQL AS
BEGIN_PROC
  DECLARE v_count INT;
  
  UPDATE summary
  SET total = (
    SELECT SUM(amount)
    FROM sales
  );
  
  GET DIAGNOSTICS v_count = ROW_COUNT;
  RETURN TRUE;
END_PROC;

BigQuery Script:

CREATE PROCEDURE update_totals()
BEGIN
  DECLARE v_count INT64;
  
  UPDATE summary
  SET total = (
    SELECT SUM(amount)
    FROM sales
  )
  WHERE TRUE;
  
  SET v_count = @@row_count;
END;

DataMigration.AI vs Traditional Netezza Migration

Feature	DataMigration.AI	Traditional Tools
Migration Timeline	6-8 weeks	9-18 months
NZPLSQL Conversion Accuracy	93%	60-70%
Automated Schema Conversion		Partial
Distribution Key Optimization	AI-powered	Manual
Zone Map to Partition Conversion
Query Workload Analysis		Limited
Parallel Data Transfer	Optimized	Basic
Cost Savings	82%	40-50%
Performance Testing	Automated	Manual
Zero Downtime		Requires planning

Netezza to BigQuery Migration: Complete Guide for 2025