Anomalo
AI data quality monitoring
Overview
Anomalo monitors data warehouses (Snowflake, BigQuery, Databricks) for anomalies, schema changes, and quality issues — no-code rule creation plus ML-based detection. Used by Notion, Square, Discover and others.
Use cases
ASEAN Perspective
Anomalo in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
Anomalo is an enterprise data-quality platform that uses machine learning to automatically detect anomalies, schema drift, and broken pipelines across warehouses like Snowflake, BigQuery, and Databricks, with little manual rule-writing. Its no-code monitoring and root-cause analysis genuinely reduce the toil of building data tests by hand, and it now layers in LLM-based unstructured-data checks.
It fits mid-to-large data teams with real warehouse scale and budget; it is overkill and likely unaffordable for small teams who can get by with dbt tests or Great Expectations. Pricing is enterprise and opaque, with no transparent self-serve tier. ASEAN usage works via cloud warehouses, but there is no localised presence.
About this listing
This entry was compiled from publicly available data including Anomalo's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with Anomalo unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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