Data quality for the whole team. Built for more than code.

Yes. This comparison covers GX Core, the Python framework for defining and running data validations. Soda provides shared contracts, automated monitoring, diagnostics and dashboards in one platform. Engineers can work in code, while business users contribute through the UI or AI. Soda is a fit when your quality program needs participation beyond the engineers maintaining validation code.

Yes. Soda’s data observability includes Record-level Anomaly Detection (RAD), which analyzes patterns across columns, rows and segments to flag unusual records and changes in relationships between values. It helps find issues that aggregate metrics can miss, without requiring a separate check for every column. Teams can investigate affected records and add business checks to enforce known requirements.

Data quality checks whether data meets requirements such as completeness, accuracy and valid values. Data observability monitors how data behaves over time to identify unexpected changes. The two work together: explicit checks enforce known business rules, while anomaly detection finds changes you may not have anticipated. Soda combines both in one platform.

A Soda data contract is an executable definition of the quality a dataset must meet. It groups checks for requirements such as freshness, required values, uniqueness and valid data types. Teams can review changes and track versions. Soda runs the checks and reports failures, turning agreed requirements into tests that can run in data pipelines.

Yes. Business users and data stewards can define requirements through Soda’s UI or in plain language with AI. Engineers can contribute through code. Those contributions become part of the same dataset contract, so teams share the rules while each team contributes its own domain knowledge. A shared contract does not require one central team to write every check.

Soda Autopilot profiles datasets and drafts data contracts with checks for your team to review. Contract Copilot helps turn plain-language requirements into checks and refine existing contracts. AI provides a starting point; data owners add the business context that data alone cannot reveal, such as which values are valid for a particular process.

Yes. Start with a critical dataset and the requirements your existing checks enforce. Soda supports custom SQL checks, so you can express existing business logic in a contract, then compare results before retiring older checks. The work needed to adapt each check depends on its SQL dialect and implementation; there is no need to move every dataset at once.

Yes. Soda provides metric-level and record-level reconciliation checks. Record-level checks use keys to match rows between source and target, then compare values to find differences. Teams can inspect results from both datasets before switching systems. Reconciliation checks live in data contracts alongside the quality checks you continue running after the migration.

Yes. With a self-hosted Soda Runner, checks execute in your environment. A Diagnostics Warehouse keeps failed-row data in your warehouse for investigation. Soda Cloud receives metadata, aggregated check results and monitoring metrics. Your team can review these data flows against its access and security requirements.