Collate Data Quality and Data Catalog

Collate data testing

Collate runs no-code or SQL tests on tables and DataFrames, compares two tables row by row, and shows the rows that fail. Data contracts define the schema, semantics, ownership and quality tests of each asset.

Compare: data contracts tools · data testing tools · data comparison tools

• SQL Table Tests: No-code and SQL test cases cover null counts (completeness), uniqueness, value ranges (accuracy), accepted values (validity), and referential integrity via reference table parameters (consistency) (docs).

• DataFrame Tests: The Data Quality as Code Python SDK includes a DataFrameValidator that validates Pandas DataFrames before loading to destinations, running OpenMetadata test definitions in-memory (docs).

• Data Diff: The built-in 'Compare 2 Tables for Differences' test matches rows by key columns and compares data values between two tables, surfacing the count of mismatched rows — supports cross-platform diff (e.g., Snowflake → Redshift migration validation) (docs).

• Data Contracts: Collate Data Contracts define expected schema structure, semantics, ownership, and quality tests for a table asset, with enforcement tracked over time (docs).

• Inspect Failed Rows: The Root Cause Analysis page in the Incident Manager shows a sample of failed rows in a table format for supported test types, with options to run inspection queries and share them with team members. Navigation of all failing rows or bulk export is not documented (docs).

• Unstructured Data Tests: No test types for unstructured data (free-text, JSON blobs, documents); all native tests target structured table columns (docs).

• Table Comparison Tests: No built-in test for comparing aggregate metrics (row count, sums, totals) between two tables; the cross-table test ('Compare 2 Tables for Differences') is a row-level data diff, not an aggregate comparison (docs).

Collate data observability

Collate profiles tables and columns, and its dynamic assertions learn from profiling history to flag deviations. Monitors are set up in the UI or as YAML, and Collate monitors pipeline runs on all plans.

Compare: data profiling tools · data observability tools

• Data Profiling: Table & Column Data Profiler computes null %, distinct count, min/max, and value distributions for all connected SQL tables (pricing).

• Anomaly Detection: Dynamic Assertions learn from historical profiling data to flag statistical deviations without requiring manually-defined thresholds (docs).

• Freshness Monitors: The Test Library provides a reserved parameter for 'maximum age of the most recent record in days', supporting native freshness checks (docs).

• Volume Monitors: Anomaly detection covers spikes and drops in row count trends; the table profiler also tracks row count over time (docs).

• Schema Monitors: Collate continuously ingests metadata and surfaces schema changes (column additions, removals, type changes) as events with alerts (docs).

• Field Monitors: The column profiler tracks null %, distinct count, mean, min/max, and distributions per column as time series with anomaly detection (docs).

• Distribution Monitors: The column profiler tracks value distributions over time and the anomaly detection engine flags statistical deviations in column distributions (docs).

• Segment Monitors: Dimensional Validation groups column-level test results by a categorical dimension column (e.g., region, product_type) to surface per-segment quality breakdowns (docs).

• Custom SQL Monitors: The Test Library lets administrators define arbitrary SQL expressions as reusable custom test definitions with dynamic parameters (docs).

• UI-Based Monitors: No-Code Test Cases can be created entirely through the UI without writing YAML or SQL (docs).

• Monitors-as-Code: Tests can be defined and deployed via YAML configuration files for version-controlled, code-based workflows (docs).

• Pipeline Monitoring: Pipeline Monitoring is available on all plans, tracking data pipeline job health and run status (pricing).

• Window-Based Monitors: Anomaly detection learns from historical patterns without configurable tumbling/sliding time windows; the 'Table Row Inserted Count to be Between' test uses a lookback interval but evaluates a single metric, not window-based quality scoring (docs).

• Partition-Based Monitors: The 'Table Row Inserted Count to be Between' test applies a timestamp-column time-filter predicate matching the feature definition, but the docs state it cannot execute on tables that have a partition configured in Collate — which undercuts the core partition-aware use case (docs).

Collate incident management

Collate sends alerts and tracks incidents through triage and resolution. It has no ticketing or on-call integrations.

• Alert Notifications: Alerts and Notifications are available on all plans via email, Slack, and Microsoft Teams (unlimited on Premium+) (pricing).

• Incident Triage: The Incident Manager provides a structured workflow to classify failures, assign owners, and track resolution status (docs).

• Incident Resolution: Collate's Resolution Workflow surfaces root-cause context and lets teams mark incidents as resolved with documented fixes (docs).

• Ticketing Integrations: Collate includes a ServiceNow connector for ingesting ServiceNow metadata into the data catalog, but this is a metadata connector rather than a ticketing integration that creates incident tickets from data quality failures. No Jira ticketing integration is documented (docs).

• On-Call Integrations: No native PagerDuty or Opsgenie integration; alert channels are limited to Slack, MS Teams, Google Chat, email, and generic webhooks (docs).

• Alert Feedback: Collate supports resolving incidents and marking them as false positives, but dedicated alert muting or snooze windows are not explicitly documented (docs).

Collate data quality analytics

Collate's dashboards and health scores show data quality.

• Data Quality Dashboards: Test Case Reporting, Coverage KPIs, and Data Health Reports provide built-in quality dashboards (limited on Free, full on Premium+) (pricing).

• Data Health Scores: Data Health Reports (Premium+) compute quality scores and provide governance trend charts for data assets (pricing).

• Incident Resolution Metrics: The Incident Manager tracks status (New/ACK/Assigned/Resolved), assignee, root cause and a timeline per incident, but has no MTTD/MTTR dashboard, incident volume trend chart, or resolution-rate-by-owner analytics (docs).

Collate data lineage

Collate extracts lineage automatically from databases, pipelines and dashboards, at table and column level, with impact analysis.

Compare: data lineage tools

• Table-Level Lineage: Column & Table Lineage is available on all plans (limited in Free, full in Premium/Enterprise) (pricing).

• Column-Level Lineage: Column-level lineage is supported with automated SQL parsing, as documented in the lineage how-to guides (docs).

• End-to-End Lineage: Lineage spans databases, pipeline services, and dashboard tools end-to-end, updated hourly on Enterprise (docs).

• Impact Analysis: The lineage view shows downstream dependencies to scope the blast radius of quality issues or code changes (docs).

Collate data governance

Collate's catalog, glossary, classification agent and data products make it a full data governance platform.

Compare: data catalog tools · data stewardship tools · data classification tools

• Data Catalog: Searchable data catalog with full-text search and filters for type, tier, tag, owner, and service, a business glossary, ownership, and Collate AI Chat for plain-language questions about assets (docs).

• Data Classification: Auto-Classification workflow scans column names via regex and actual row values via NLP entity recognition to classify columns into sensitivity categories (PII.Sensitive, PII.NonSensitive) (docs).

• Data Products: Data Products are a first-class entity grouping datasets/assets under a Domain (data mesh model), and a 'Data Product' filter is available directly within Data Quality parameters, tying quality status to the data-product grouping (docs).

• Data Catalog Integration: Collate IS a data catalog (built on OpenMetadata); it does not push quality metadata into external catalogs such as Alation or Collibra (website).

Collate FinOps

Collate's Cost Optimizer shows warehouse compute costs and recommends optimizations.

• Cost Monitoring: The Cost Optimizer provides warehouse compute cost visibility and optimization recommendations, available on Premium and Enterprise plans (pricing).

• Query Performance: Collate does not provide SQL query performance monitoring; it focuses on metadata, data quality checks, and pipeline job status (pricing).

Collate workflows

Collate has a REST API, a Python SDK, webhooks, an MCP server and a dbt integration. It doesn't run checks in CI/CD pipelines.

• Scheduling: Test frequency is configurable per plan: weekly (Free), every 8 hours (Premium), or hourly (Enterprise) (pricing).

• Webhooks: Webhook is listed as a supported third-party integration channel for alerts alongside email, Slack, and Teams (pricing).

• REST / GraphQL API: API Access is available on all plans; full APIs & SDK support is listed as a Premium/Enterprise feature (pricing).

• MCP Server: Collate has a built-in MCP server exposing metadata context to AI coding agents including Claude Desktop, Cursor, Goose, and VS Code (docs).

• Python SDK: The OpenMetadata Python SDK includes TestRunner and DataFrameValidator, enabling programmatic test execution and in-memory Pandas DataFrame validation via pip install (docs).

• dbt Integration: Collate natively integrates with dbt, importing test results and lineage from dbt projects (available on Premium+ plans) (pricing).

• CI/CD Integrations: Tests can be defined as YAML/code, but there is no documented integration with GitHub Actions, GitLab CI, or similar pipelines to run checks and block deployments on failure (docs).

Collate AI agents

Collate has AskCollate, an assistant that answers questions about your data, writes tests from prompts and generates tests automatically. AI agents triage issues and classify data. An MCP server is also available.

• AI Chat Assistant: AskCollate is an in-product conversational AI assistant that answers natural language questions about data assets, generates SQL, explores lineage, suggests quality tests, and manages governance — all within the Collate UI (docs).

• Natural Language Test Generation: AskCollate lets users type a prompt like "Create test cases for the products table" and it generates and can create actual data quality test cases (completeness, uniqueness, range, integrity) via profiling/lineage context (docs).

• Automated Test Generation: The Quality Agent automatically creates technical and business quality tests based on table metadata and business context (pricing).

• AI Issue Triaging: AskCollate is a conversational AI that queries metadata, lineage, and quality results to surface context for incident triage (docs).

• AI Issue Resolution: The AI Quality Agent suggests and deploys tests; AskCollate assists with investigation. Neither proposes SQL fixes, triggers pipeline reruns, or automates remediation actions (docs).