Unified Data Quality Tools
Tools combining data quality, data observability, data lineage, and a data catalog in one product.
Collate
Managed agentic data platform built on OpenMetadata that connects to Oracle with a user holding CREATE SESSION and SELECT_CATALOG_ROLE, reads table metadata from the data dictionary, and profiles tables it can SELECT.
Best for teams that want profiling, data quality tests, and lineage in one catalog on Oracle with an open-source core (OpenMetadata).
OpenMetadata
Open-source context platform that connects to Oracle with a user holding CREATE SESSION and SELECT_CATALOG_ROLE, reads table metadata from the data dictionary, and profiles tables it can SELECT.
Best for teams that want profiling, data quality tests, and lineage in one catalog on Oracle with Collate as the managed option.
Ataccama ONE
Agentic data trust platform that connects to Oracle over a JDBC string with a username and password, with optional mTLS driver properties, then browses and profiles tables.
Best for teams that want profiling and data quality rules on Oracle with governance and MDM in the same platform.
Bigeye
Enterprise AI trust platform that connects to Oracle with a read-only user that reads ALL_TABLES, ALL_VIEWS, and ALL_TAB_COLUMNS, then profiles tables and runs metrics.
Best for teams that want automatic metrics and thresholds on Oracle with monitoring next to their other warehouses.
Sifflet
Data and AI observability control plane that connects to Oracle with a dedicated user over a service name, reads table metadata, and runs read-only monitor queries tagged with a Sifflet comment.
Best for teams that want monitors with business context on Oracle with lineage shared with business users.
Decube
Data observability, quality, and lineage platform that connects to Oracle with a read-only user, collecting metadata and running profiling plus freshness, volume, field health, and custom SQL monitors.
Best for teams that want profiling and data quality monitors on Oracle with a catalog in the same tool.
SelectZero
Self-hosted data observability platform with value-level data validation, column-level lineage, a data catalog, and a business glossary.
Best for enterprises that cannot let data leave their environment and want one self-hosted tool, deployed with Docker.
Data Observability Tools
Tools focusing on detecting data quality issues with data monitors and anomaly detection.
Monte Carlo
Data + AI observability platform that connects to Oracle with a read-only user (CREATE SESSION, SELECT ANY TABLE), collecting table metadata and running custom SQL and volume monitors.
Best for teams that want ML monitors and lineage on Oracle with incident management at enterprise scale.
Qualytics
Data quality control layer that connects to Oracle through the JDBC Thin driver over TCP or TCPS as a user with SELECT on tables, then discovers, profiles, and scans them.
Best for teams that want inferred data quality checks on Oracle with checks generated from profiles.
DQOps
Open-source data quality operations center that connects to Oracle with a database user, imports selected tables, and runs profiling and monitoring checks on them.
Best for teams that want free, self-hosted profiling and monitoring checks on Oracle with an open-source core.
Validio
Agentic data quality and observability platform that connects to Oracle with a service account over optional TLS, reads catalog views and tables, and can read the unified audit trail for lineage.
Best for teams that want validators with segmentation on Oracle with scheduled polling.
Lightup
Enterprise data observability platform that connects to Oracle with a user granted SELECT on each table and scans schemas hourly, daily, or weekly to monitor tables and views.
Best for teams that want scheduled data quality scans on Oracle with enterprise deployment options.
Acceldata
Autonomous data and AI platform that connects to Oracle over JDBC with a password or an Autonomous Database wallet, as a user with dictionary access, and crawls named databases on a schedule.
Best for teams that want Oracle cataloged with lineage, rather than checks on the data.
Coalesce Quality
Data quality product by Coalesce, built on SYNQ, that connects to Oracle with a user that reads dictionary views, optimizer row counts, and V$SQL query logs, so default monitors need no table data.
Best for teams that want Oracle cataloged with lineage, rather than checks on the data.
Pantomath
AI-powered data operations center with data and pipeline observability, cross-platform pipeline lineage, and automated root-cause analysis.
Best for data operations teams that need to trace failures across pipelines and route incidents to Jira or ServiceNow.
Anomalo
Autonomous data quality monitoring platform where AI agents monitor, investigate, and report issues across structured and unstructured data.
Best for enterprises that want ML-based monitoring with little rule-writing, including quality checks on unstructured data such as documents.
FirstEigen DataBuck
Zero-code data quality software where AI agents auto-discover rules, validate and reconcile data, and score data trust across sources.
Best for enterprises in regulated industries that need automated data reconciliations and validations across legacy and cloud sources.
DQLabs
AI-native platform combining data observability, data quality checks, and business context, with anomaly detection, end-to-end lineage, and pipeline observability.
Best for enterprises that want data quality and observability in one tool, connected to their data catalog and issue management tools.
Shift-Left Data Quality Tools
Tools focusing on preventing data quality issues with data contracts, data-diff, and CI/CD.
Soda Cloud
AI-native data quality platform built on data contracts that connects to Oracle with a user and a connect string or service name, and runs quality scans on the database.
Best for teams that want data quality checks and contracts as code on Oracle with YAML in Git and CI/CD.
Soda Core
Open-source data contracts engine that connects to Oracle with a user and a connect string or service name, and runs quality scans on the database.
Best for teams that want data quality checks and contracts as code on Oracle with YAML in Git and CI/CD.
Datafold
AI-powered data engineering platform that connects to Oracle as a dedicated user with SELECT ANY TABLE and creates temporary tables in its own schema to run data diffs, including across databases.
Best for teams that want row-level data diffs on Oracle with migration and pull request checks.
Data Contract CLI
Open-source command-line tool for ODCS data contracts that tests Oracle tables with a username and password, taking host, port, and service name from the contract's servers block.
Best for teams that want Oracle DB data tested against vendor-neutral ODCS data contracts in CI/CD with one command.
Related integrations
- Data quality tools for Databricks SQL (34)
- Data quality tools for Snowflake (34)
- Data quality tools for Google BigQuery (33)
- Data quality tools for Amazon Redshift (32)
- Data quality tools for Azure Synapse (19)
- Data quality tools for Teradata (16)
- Data quality tools for ClickHouse (15)
- Data quality tools for IBM DB2 (14)
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