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CDC simulation

CDC-style synthetic data helps test pipelines that consume inserts, updates, deletes, sequence numbers, and event timestamps.

Great Generator creates synthetic data. It does not anonymize, mask, de-identify, or transform production records.

Getting started API reference GitHub

Generate CDC records

CDC output can include operation type, event time, ingestion time, sequence information, and late-arriving or duplicate indicators where configured.

from great_generator import generate_cdc

cdc = generate_cdc(domain="banking", table="customers", rows=1000)

Testing use cases

Use CDC simulation for ingestion tests, ordering tests, schema-evolution exercises, and data quality checks in non-production environments.

Related documentation

Keep exploring

Anomaly injection for data quality testingInject controlled nulls, duplicates, late records, outliers, invalid values, and other synthetic data quality issues for validation testing. Spark, Databricks, Delta, and lakehouse workflowsUse Great Generator in Spark and Databricks notebooks to create synthetic datasets and write to Parquet, Delta, DBFS, ADLS, S3, GCS, or configured connectors. Query-aware synthetic dataGenerate synthetic data with required values, partition coverage, selectivity targets, and join coverage for SQL and data engineering tests. API reference overviewReview the main Great Generator public APIs for schema generation, SQL DDL parsing, relational generation, CDC, anomalies, advisors, and exports. GitHub repositorySource code, issues, releases, and project history. PyPI packageInstall Great Generator from Python Package Index.