# DQLabs PRIZM

> Industry's first AI Native platform where Data Observability, Data Quality, and Context work as one system—turning trust from a manual, reactive process into autonomous operations governed by the enterprise.

- [What is PRIZM?  ](https://docs.dqlabs.ai/introduction.md): An introduction to DQLabs' AI-native platform that unifies data observability, quality, and context into one operational layer.
- [Sign in & Invite your team](https://docs.dqlabs.ai/quickstart.md): How to sign in to PRIZM, and how admins can invite and manage team members.
- [Get up and running with PRIZM: a step-by-step guide](https://docs.dqlabs.ai/login.md): Learn how to sign in to PRIZM with your email, and create a new account from the login screen.
- [Assets: Discover and manage your context in PRIZM](https://docs.dqlabs.ai/concepts/assets.md): Assets are the tables, views, attributes, queries, pipelines, reports, and semantic models PRIZM tracks across your connected databases and data platforms.
- [Metrics: Monitor data quality and pipeline health in PRIZM](https://docs.dqlabs.ai/concepts/metrics.md): Metrics are the continuous measurements PRIZM runs against every asset, source, and system to answer two questions: is this data trustworthy, and is this pipeline healthy?
- [Alerts: Automatic data drift and quality notifications](https://docs.dqlabs.ai/concepts/alerts.md): PRIZM alerts fire automatically when a measure threshold is breached. Each alert carries a severity level, a drift status, and a percent change value.
- [Issues: Manage data problems from detection to fix](https://docs.dqlabs.ai/concepts/issues.md): PRIZM issues provide a structured workflow to assign, track, and resolve data reliability and quality problems across priority levels and lifecycle statuses.
- [Platform Overview](https://docs.dqlabs.ai/platform/overview.md): DQLabs Prizm is a multi-agentic, AI-native platform for data quality, cataloging, and observability.
- [Multi-Agent Architecture](https://docs.dqlabs.ai/platform/multi-agent-architecture.md): How Prizm's specialized AI agents coordinate to handle data management at scale.
- [Autonomous Intelligence](https://docs.dqlabs.ai/platform/autonomous-intelligence.md): Prizm's 5-level autonomous intelligence architecture that powers continuous, self-improving data management.
- [AI & ML Capabilities](https://docs.dqlabs.ai/platform/ai-and-ml-capabilities.md): AI/ML capabilities, availability, and trust overview for Prizm.
- [Converse — AI Chat Interface](https://docs.dqlabs.ai/platform/converse.md): Natural language data management powered by Claude and MCP tools — no code required.
- [AI Stewardship](https://docs.dqlabs.ai/platform/ai-stewardship.md): The human + AI governance layer that ensures autonomous actions stay transparent, accountable, and under control
- [Architecture Overview](https://docs.dqlabs.ai/architecture/overview.md): PRIZM's core components - context, observability and quality, and how they connect.
- [Criticality Scoring](https://docs.dqlabs.ai/architecture/criticality.md): Know instantly which assets matter most, before they break.
- [Asset Overview](https://docs.dqlabs.ai/architecture/asset.md): Any data object cataloged, governed, and monitored within DQLabs Prizm.
- [Asset Detail](https://docs.dqlabs.ai/architecture/asset-details.md): In-depth view of a single asset
- [Asset Type](https://docs.dqlabs.ai/architecture/asset-type.md): Native classification assigned to a data object
- [Entities](https://docs.dqlabs.ai/architecture/entities.md): Child (One level down) in the asset hierarchy
- [Entity List](https://docs.dqlabs.ai/architecture/entity-list.md): A comprehensive parent-child object mapping of the entities Prizm catalogs across warehouses, pipelines, BI tools, and transformation layers.
- [Attribute](https://docs.dqlabs.ai/architecture/attribute.md): Reference table of every field tracked on a Prizm attribute (column), including alerts, criticality, data type, distinct count, null count, and score.
- [Data Profiling](https://docs.dqlabs.ai/architecture/data-profiling.md): Statistical sampling and baseline creation for anomaly detection and quality metrics.
- [Profile Insights](https://docs.dqlabs.ai/architecture/profile-insights.md): A complete reference table of Prizm's profile insights — distribution, frequency, pattern, and statistical metrics used to assess data quality.
- [Metric Overview](https://docs.dqlabs.ai/architecture/metric.md): Metric types, execution engine, scheduling, and quality scoring pipeline.
- [Overview](https://docs.dqlabs.ai/architecture/metrics/volume/overview.md): What Volume metrics are in Prizm — track the size and quantity of data flowing through an asset to catch pipeline failures early.
- [Configure](https://docs.dqlabs.ai/architecture/metrics/volume/configure.md): How to configure Prizm's Volume metric, including automated and custom thresholds for row count and data size alerts.
- [Overview](https://docs.dqlabs.ai/architecture/metrics/freshness/overview.md): What Freshness metrics are in Prizm — measure how recently data was loaded or updated to catch pipeline delays and stale datasets early.
- [Configure](https://docs.dqlabs.ai/architecture/metrics/freshness/configure.md): How to configure Prizm's Freshness metric, including time-based automated and custom thresholds for staleness alerts.
- [Overview](https://docs.dqlabs.ai/architecture/metrics/schema/overview.md): What Schema metrics are in Prizm — monitor the structure of a data asset for column, type, and constraint changes that can silently break pipelines.
- [Configure](https://docs.dqlabs.ai/architecture/metrics/schema/configure.md): How to configure Prizm's Schema metric, including column-count thresholds and structural change detection settings.
- [Overview](https://docs.dqlabs.ai/architecture/metrics/credits/overview.md): What a Credits metric is in Prizm — tracks compute credit consumption at the database and schema level for cost governance and budget alerting.
- [Configure](https://docs.dqlabs.ai/architecture/metrics/credits/configure.md): How to configure Prizm's Credits metric, including scoring options and threshold settings for compute credit consumption alerts.
- [Overview](https://docs.dqlabs.ai/architecture/metrics/queries/overview.md): What Queries metrics are in Prizm — measure the volume, efficiency, and reliability of query workloads against a database or schema.
- [Configure](https://docs.dqlabs.ai/architecture/metrics/queries/configure.md): How to configure Prizm's Queries metric, including thresholds for query volume, success rate, and execution time alerts.
- [Overview](https://docs.dqlabs.ai/architecture/metrics/usage/overview.md): What Usage metrics are in Prizm — track daily query volume against a database or schema to reveal workload trends and unexpected spikes.
- [Overview](https://docs.dqlabs.ai/architecture/metrics/distribution/overview.md): What Distribution metrics are in Prizm — capture the character- and value-level composition of a column, including nullness, whitespace, and sign.
- [Configure](https://docs.dqlabs.ai/architecture/metrics/distribution/configure.md): How to enable and configure Prizm's Distribution metrics per column, including thresholds for null, blank, and character-composition checks.
- [Overview](https://docs.dqlabs.ai/architecture/metrics/frequency/overview.md): What Frequency metrics are in Prizm — measure how often specific values, lengths, and ranges appear in a column to catch format and enum violations.
- [Configure](https://docs.dqlabs.ai/architecture/metrics/frequency/configure.md): How to enable and configure Prizm's Frequency metrics per column, including regex patterns and thresholds for value and length distribution.
- [Overview](https://docs.dqlabs.ai/architecture/metrics/pattern/overview.md): What Pattern metrics are in Prizm — auto-discover the structural format template of column values to detect format violations and drift.
- [Configure](https://docs.dqlabs.ai/architecture/metrics/pattern/configure.md): How to enable and configure Prizm's Pattern metrics per column, including short and long pattern detection and thresholds.
- [Overview](https://docs.dqlabs.ai/architecture/metrics/statistics/overview.md): What Statistics metrics are in Prizm — compute numeric summaries of column values like mean, median, quantiles, and distribution shape.
- [Configure](https://docs.dqlabs.ai/architecture/metrics/statistics/configure.md): How to enable and configure Prizm's Statistics metrics per column, including thresholds for mean, sum, and distribution-shape alerts.
- [Overview](https://docs.dqlabs.ai/architecture/metrics/conditional/overview.md): What a Conditional metric is in Prizm — a user-defined, row-level quality rule that every record in an asset must satisfy.
- [Setup](https://docs.dqlabs.ai/architecture/metrics/conditional/setup.md): Step-by-step guide to creating a Conditional metric in Prizm, covering rule types, select criteria, and threshold configuration.
- [Use Cases](https://docs.dqlabs.ai/architecture/metrics/conditional/usecases.md): Real-world Conditional metric examples in Prizm — value range enforcement, completeness rules, and cross-column consistency checks.
- [Overview](https://docs.dqlabs.ai/architecture/metrics/query/usecases.md): What Query metrics are, how they run custom SQL expressions, and how to make them reusable with runtime parameters.
- [Setup](https://docs.dqlabs.ai/architecture/metrics/query/setup.md): Step-by-step instructions for creating a Query metric and adding runtime parameters.
- [Use Cases](https://docs.dqlabs.ai/architecture/metrics/query/examples.md): Real-world examples of Query metrics — custom SQL checks and parameterized patterns.
- [Overview](https://docs.dqlabs.ai/architecture/metrics/standalone/overview.md): What a Standalone Query metric is in Prizm — an independent, scheduled quality measurement not tied to any specific asset or attribute.
- [Setup](https://docs.dqlabs.ai/architecture/metrics/standalone/setup.md): Step-by-step guide to creating a Standalone Query metric in Prizm, including SQL query writing, run schedule, and thresholds.
- [Use Cases](https://docs.dqlabs.ai/architecture/metrics/standalone/usecases.md): Real-world Standalone Query metric examples in Prizm — Daily Active Users KPI tracking and cross-source revenue reconciliation.
- [Overview](https://docs.dqlabs.ai/architecture/metrics/behavioural/overview.md): What a Behavioral metric is in Prizm — a user-defined check that models expected data behavior over time and flags deviations from that model.
- [Setup](https://docs.dqlabs.ai/architecture/metrics/behavioural/setup.md): Step-by-step guide to creating and configuring a Behavioral metric in Prizm, including grouping dimensions, comparison context, and thresholds.
- [Use Cases](https://docs.dqlabs.ai/architecture/metrics/behavioural/usecases.md): Real-world Behavioral metric examples in Prizm — transaction volume trend monitoring and ML feature drift detection.
- [Overview](https://docs.dqlabs.ai/architecture/metrics/comparison/overview.md): What a Comparison metric is in Prizm — validates consistency between two datasets for migration, reconciliation, and pipeline checkpoint verification.
- [Setup](https://docs.dqlabs.ai/architecture/metrics/comparison/setup.md): Step-by-step guide to creating a Comparison metric in Prizm, including source selection, comparison mode, alignment, and thresholds.
- [Use Cases](https://docs.dqlabs.ai/architecture/metrics/comparison/usecases.md): Real-world Comparison metric examples in Prizm — data migration validation, staging vs. production parity, and pipeline checkpoint reconciliation.
- [Overview](https://docs.dqlabs.ai/architecture/metrics/lookup/overview.md): What a Lookup metric is in Prizm — validates column values against a reference source for referential integrity and controlled vocabulary checks.
- [Setup](https://docs.dqlabs.ai/architecture/metrics/lookup/setup.md): Step-by-step guide to creating a Lookup metric in Prizm, including source types, key mapping, matching logic, and thresholds.
- [Use Cases](https://docs.dqlabs.ai/architecture/metrics/lookup/usecases.md): Real-world Lookup metric examples in Prizm — product catalog integrity, ISO country code validation, and cross-source customer ID checks.
- [Overview](https://docs.dqlabs.ai/architecture/metrics/metric-template/overview.md): How Metric Templates (Rule Library) let you write a parameterized SQL rule once and assign it to many assets, each with its own parameter overrides.
- [Setup](https://docs.dqlabs.ai/architecture/metrics/metric-template/setup.md): How to create a Metric Template, assign it to assets with parameter overrides, manage propagation, and run all assignments at once.
- [Anomaly Detection](https://docs.dqlabs.ai/architecture/anomaly-detection.md): How Prizm's anomaly detection builds statistical baselines, scores drift and outliers, and assigns alert priority, illustrated with a volume metric example.
- [Metric Results](https://docs.dqlabs.ai/architecture/metric-result.md): How Prizm calculates a metric's anomaly distribution, drift, outlier percentage, and health state, plus the Overview, Timeline, and Audit tabs.
- [Pipeline Metric](https://docs.dqlabs.ai/architecture/pipeline-metric.md): PRIZM extends its Operational, Performance, and Business metric contexts to pipelines — tracking model runs, test outcomes, and job executions so pipeline problems surface as alerts, not just numbers on a dashboard.
- [Lineage](https://docs.dqlabs.ai/architecture/lineage.md): Comprehensive data lineage tracking — from source to consumption — with automated impact analysis and root cause identification.
- [Lineage Extraction](https://docs.dqlabs.ai/architecture/lineage-extraction.md): How Prizm extracts, models, and uses table and column-level lineage, including multi-hop lineage, the lineage data model, and derived metrics.
- [Usage](https://docs.dqlabs.ai/architecture/usage.md): How the Usage tab shows query volume, performance, cost, and user activity for a Prizm asset, including how usage feeds criticality scoring.
- [Logging Overview](https://docs.dqlabs.ai/architecture/log/logging-overview.md): An overview of Prizm's three logging surfaces — Asset Audit, Audit Log, and Activity Log — and how they relate to one another.
- [Asset Audit](https://docs.dqlabs.ai/architecture/log/asset-audit.md): How the asset-level Audit tab tracks CRUD operations, links, views, and job triggers for a single asset, with retention and export details.
- [Audit Log](https://docs.dqlabs.ai/architecture/log/audit-log.md): How Prizm's organization-wide Application Audit Log captures every API call, AI action, and background job execution across the platform.
- [Activity Log](https://docs.dqlabs.ai/architecture/log/activity-log.md): Session-level view of user activity across the platform
- [Documentation](https://docs.dqlabs.ai/architecture/documentation.md): Write, upload, and AI-assist asset documentation directly from the asset page
- [Scoring](https://docs.dqlabs.ai/architecture/score.md): The Score entity stores data quality metrics and scoring results for assets — the foundation of Prizm's quality monitoring and reporting.
- [Overview](https://docs.dqlabs.ai/architecture/semantics/overview.md): How Domain, Product, Application, Tag, and Glossary work together to give data assets business context in Prizm.
- [Domain](https://docs.dqlabs.ai/architecture/semantics/domain.md): How to use Domains and Sub-domains to organize data assets by business area and assign accountability in Prizm.
- [Product](https://docs.dqlabs.ai/architecture/semantics/product.md): How to create and manage Data Products — logical groupings of data assets organized around a business concept with defined quality expectations.
- [Application](https://docs.dqlabs.ai/architecture/semantics/application.md): How to register Applications in Prizm — the source systems, processing platforms, and consumption tools connected to your data.
- [Tag](https://docs.dqlabs.ai/architecture/semantics/tag.md): How to create and apply Tags in Prizm — flexible labels for classifying, filtering, and targeting assets and attributes.
- [Glossary](https://docs.dqlabs.ai/architecture/semantics/glossary.md): How to build and use Prizm's business vocabulary — Glossaries, Categories, and Terms that define the authoritative meaning of data attributes.
- [overview](https://docs.dqlabs.ai/architecture/exceptions/overview.md): Overview of exception records in Prizm — how flagged data quality violations are tracked, resolved, and audited.
- [Exception Records](https://docs.dqlabs.ai/architecture/exceptions/exception-records.md): How to view, filter, manage, and remediate individual exception records in Prizm.
- [Notifications](https://docs.dqlabs.ai/architecture/exceptions/notifications.md): How Prizm sends notifications when new exception records are created or existing records change status.
- [Exception Dashboard](https://docs.dqlabs.ai/architecture/exceptions/exception-dashboard.md): How to use the Exception Analytics dashboard to track exception volume, age, status distribution, and trends across assets and owners.
- [Alerts Overview](https://docs.dqlabs.ai/architecture/alerts/overview.md): How alerting works in Prizm — threshold types, alert states, suppression, and clusters.
- [Alert Detail](https://docs.dqlabs.ai/architecture/alerts/details.md): What the alert detail page shows and how to use AI-powered triage, impact analysis, root cause investigation, and alert clusters.
- [Alert Management](https://docs.dqlabs.ai/architecture/alerts/management.md): How to manage the alert lifecycle in Prizm — alert states, resolution, issue linkage, and suppression during maintenance windows.
- [Alert Thresholds](https://docs.dqlabs.ai/architecture/alerts/thresholds.md): How to configure Auto, Limit, and Custom alert thresholds for Prizm metrics — including multi-criteria custom rules, sensitivity tuning, and per-condition actions.
- [Actions](https://docs.dqlabs.ai/architecture/actions.md): Configurable trigger → condition → action workflows that automate business processes across the Prizm platform.
- [SLA](https://docs.dqlabs.ai/architecture/sla.md): Time-based service commitments tracked consistently across Alerts and Issues, with breach status synced to linked Jira and ServiceNow tickets.
- [Data Sources Overview](https://docs.dqlabs.ai/sources/overview.md): Connect Prizm to your entire data ecosystem — from cloud data warehouses to BI tools, and pipelines.
- [ADLS Overview](https://docs.dqlabs.ai/sources/adls/overview.md): What the Azure Data Lake Storage connector covers, the two ways Prizm can process your files, and what Unity Catalog Iceberg materialization adds.
- [ADLS Setup](https://docs.dqlabs.ai/sources/adls/setup.md): Prerequisites, authentication, Unity Catalog configuration, and step-by-step instructions for connecting ADLS to Prizm — with or without your own Databricks cluster.
- [What We Collect](https://docs.dqlabs.ai/sources/adls/what-we-collect.md): Job types, compute metric types, and how Prizm handles Delta Lake tables and Iceberg materialization for ADLS.
- [FAQ](https://docs.dqlabs.ai/sources/adls/faq.md): Common questions and troubleshooting for the ADLS + Databricks connector.
- [Snowflake Overview](https://docs.dqlabs.ai/sources/snowflake/overview.md): What Snowflake is, why connecting it to Prizm unlocks data intelligence, and what capabilities Prizm supports.
- [Snowflake Setup](https://docs.dqlabs.ai/sources/snowflake/setup.md): Prerequisites, authentication options, and step-by-step instructions for connecting Snowflake to Prizm.
- [What Prizm Collects from Snowflake](https://docs.dqlabs.ai/sources/snowflake/what-we-collect.md): Complete field-level breakdown of every metadata object, quality metric, and signal Prizm extracts from Snowflake across all platform jobs.
- [Snowflake FAQ](https://docs.dqlabs.ai/sources/snowflake/faq.md): Common questions about connecting Snowflake to Prizm, permissions, scoping, and feature behaviour.
- [Oracle Overview](https://docs.dqlabs.ai/sources/oracle/overview.md): What the Oracle Database connector covers, why connecting it to Prizm unlocks data intelligence, and what capabilities Prizm supports.
- [Oracle Setup](https://docs.dqlabs.ai/sources/oracle/setup.md): Prerequisites, authentication options, and step-by-step instructions for connecting Oracle to Prizm.
- [What We Collect](https://docs.dqlabs.ai/sources/oracle/what-we-collect.md): Field-level breakdown of every metadata object, observability signal, and lineage edge Prizm extracts from Oracle.
- [FAQ](https://docs.dqlabs.ai/sources/oracle/faq.md): Common questions about the Oracle connector.
- [Sigma Overview](https://docs.dqlabs.ai/sources/sigma/overview.md): What the Sigma Computing connector covers, why connecting it to Prizm unlocks data intelligence, and what capabilities Prizm supports.
- [Sigma Setup](https://docs.dqlabs.ai/sources/sigma/setup.md): Prerequisites, authentication options, and step-by-step instructions for connecting Sigma to Prizm.
- [What We Collect](https://docs.dqlabs.ai/sources/sigma/what-we-collect.md): Field-level breakdown of every metadata object, observability signal, and lineage edge Prizm extracts from Sigma.
- [FAQ](https://docs.dqlabs.ai/sources/sigma/faq.md): Common questions about the Sigma connector.
- [Databricks Overview](https://docs.dqlabs.ai/sources/databricks/overview.md): What Databricks is, why connecting it to Prizm unlocks data intelligence, and what capabilities Prizm supports for Unity Catalog workspaces.
- [Setup](https://docs.dqlabs.ai/sources/databricks/setup.md): Prerequisites, authentication options, and step-by-step instructions for connecting Databricks to Prizm.
- [What Prizm Collects from Databricks](https://docs.dqlabs.ai/sources/databricks/what-we-collect.md): Complete field-level breakdown of every metadata object, quality metric, and signal Prizm extracts from Databricks Unity Catalog across all platform jobs.
- [FAQ](https://docs.dqlabs.ai/sources/databricks/faq.md): Common questions about connecting dbt to Prizm, artifact ingestion, lineage, test results, and troubleshooting.
- [SQL Server Overview](https://docs.dqlabs.ai/sources/sql/overview.md): What Microsoft SQL Server is, why connecting it to Prizm unlocks data intelligence, and what capabilities Prizm supports.
- [Setup](https://docs.dqlabs.ai/sources/sql/setup.md): Prerequisites, authentication options, and step-by-step instructions for connecting Microsoft SQL Server to Prizm.
- [What Prizm Collects from SQL Server](https://docs.dqlabs.ai/sources/sql/what-we-collect.md): Complete field-level breakdown of every metadata object, quality metric, and signal Prizm extracts from SQL Server across all platform jobs.
- [FAQ](https://docs.dqlabs.ai/sources/sql/faq.md): Common questions about connecting Microsoft SQL Server to Prizm, permissions, scoping, and feature behaviour.
- [dbt Overview](https://docs.dqlabs.ai/sources/dbt/overview.md): Connect Prizm to dbt Cloud or dbt Core to ingest transformation metadata, build end-to-end lineage, monitor job health, and surface data quality signals from dbt test results.
- [Setup](https://docs.dqlabs.ai/sources/dbt/setup.md): Connect Prizm to dbt Cloud via API and webhook, or configure the dbt Core CLI plugin to push artifacts from your existing pipeline.
- [What Prizm Collects from dbt](https://docs.dqlabs.ai/sources/dbt/what-prizm-collects.md): Artifacts Prizm ingests from dbt and how dbt objects appear in the Prizm UI.
- [FAQ](https://docs.dqlabs.ai/sources/dbt/faq.md): Common questions about connecting dbt to Prizm, artifact ingestion, lineage, test results, and troubleshooting.
- [Tableau Overview](https://docs.dqlabs.ai/sources/tableau/overview.md): Connect Prizm to Tableau Cloud or Tableau Server to ingest workbook metadata, track lineage, monitor freshness, and surface data quality signals directly in Tableau.
- [Tableau Setup](https://docs.dqlabs.ai/sources/tableau/setup.md): Prerequisites and step-by-step instructions for connecting Prizm to Tableau Cloud or Tableau Server.
- [What Prizm Collects from Tableau](https://docs.dqlabs.ai/sources/tableau/what-we-collect.md): Complete breakdown of every job, object, and field Prizm ingests from Tableau Cloud and Tableau Server.
- [Tableau FAQ](https://docs.dqlabs.ai/sources/tableau/faq.md): Common questions about connecting Tableau to Prizm, authentication, asset selection, lineage, and quality propagation.
- [Overview](https://docs.dqlabs.ai/sources/powerbi/overview.md): What the Power BI connector does, which asset types Prizm catalogs and monitors, and how lineage flows from source systems through semantic models to reports and dashboards.
- [Setup](https://docs.dqlabs.ai/sources/powerbi/setup.md): Prerequisites, authentication methods, and step-by-step instructions for connecting Microsoft Power BI to Prizm.
- [What We Collect](https://docs.dqlabs.ai/sources/powerbi/what-we-collect.md): Field-level breakdown of every metadata object Prizm extracts from Power BI — workspaces, semantic models, reports, dashboards, dataflows, and data sources.
- [FAQ](https://docs.dqlabs.ai/sources/powerbi/faq.md): Common questions about the Power BI connector — authentication, scoping, lineage, and semantic models.
- [Airflow Overview](https://docs.dqlabs.ai/sources/airflow/overview.md): How Prizm connects to Apache Airflow to catalog DAGs and tasks, monitor pipeline health, and stitch lineage across Airflow and the warehouses it orchestrates.
- [Setup](https://docs.dqlabs.ai/sources/airflow/setup.md): Create an Airflow source in Prizm and configure API Pull or CLI mode. For Plugin mode, see the Plugin Setup guide.
- [What We Collect](https://docs.dqlabs.ai/sources/airflow/what-we-collect.md): Field-level breakdown of every metadata object Prizm extracts from Apache Airflow — DAGs, tasks, run history, and operational metrics.
- [Plugin Setup](https://docs.dqlabs.ai/sources/airflow/plugin-setup.md): Install and configure the Prizm Airflow Plugin so DAG and task run metadata is pushed to Prizm automatically the moment each run completes.
- [Overview](https://docs.dqlabs.ai/integrations/alation/overview.md): Connect Prizm to Alation to push data quality metrics, alerts, issues, and scores to your catalog and pull domains and tags back into Prizm.
- [Setup](https://docs.dqlabs.ai/integrations/alation/setup.md): Connect Prizm to Alation — authentication, datasource selection, push and pull configuration, and scheduling.
- [Integration](https://docs.dqlabs.ai/integrations/alation/integration.md): How Prizm pushes quality data to Alation catalog pages, what Prizm pulls from Alation, and how to manage catalog sync jobs.
- [Overview](https://docs.dqlabs.ai/integrations/jira/overview.md): Connect Prizm to Jira to automatically create and track data quality issues as Jira tickets with full bidirectional sync.
- [Setup](https://docs.dqlabs.ai/integrations/jira/setup.md): Connect Jira to Prizm via OAuth, configure project and priority mappings, and set up the webhook for bidirectional sync.
- [Integration](https://docs.dqlabs.ai/integrations/jira/integration.md): How Prizm automatically creates Jira issues, what data is included, and how bidirectional sync works between Prizm and Jira.
- [Overview](https://docs.dqlabs.ai/integrations/servicenow/overview.md): Connect Prizm to ServiceNow to automatically create incidents from data quality issues and sync status, comments, and resolution data bidirectionally.
- [Setup](https://docs.dqlabs.ai/integrations/servicenow/setup.md): Connect ServiceNow to Prizm, configure authentication, set up incident routing and mandatory fields, and enable the webhook for bidirectional sync.
- [Integration](https://docs.dqlabs.ai/integrations/servicenow/integration.md): How Prizm creates ServiceNow incidents, what data is included, how bidirectional sync works, and how resolution data and conversations are pulled back into Prizm.
- [Overview](https://docs.dqlabs.ai/integrations/githubaction/overview.md): What the Prizm GitHub Action integration is — posts pre-merge lineage impact analysis comments on pull requests that change dbt models.
- [Setup](https://docs.dqlabs.ai/integrations/githubaction/setup.md): Step-by-step guide to adding the Prizm dbt Impact Analysis GitHub Action to a repository, including secrets, variables, and the workflow file.
- [Integration](https://docs.dqlabs.ai/integrations/githubaction/integrations.md): How the Prizm GitHub Action posts dbt pull request impact comments, including what triggers it and how downstream impact is calculated.
- [Overview](https://docs.dqlabs.ai/integrations/githubapp/overview.md): Install the Prizm Pulse GitHub App to monitor merged pull requests and automatically link dbt code changes to data quality alerts.
- [Setup](https://docs.dqlabs.ai/integrations/githubapp/setup.md): Step-by-step guide to installing the Prizm Pulse GitHub App, configuring its webhook, and registering repositories and branches in Prizm.
- [Overview](https://docs.dqlabs.ai/integrations/email/overview.md): What the Email integration is in Prizm — delivers alert notifications to email recipients via OAuth-authenticated Gmail or Outlook.
- [Setup](https://docs.dqlabs.ai/integrations/email/setup.md): Step-by-step guide to connecting Gmail or Outlook to Prizm via OAuth so alert notifications can be sent by email.
- [Overview](https://docs.dqlabs.ai/integrations/googlechat/overview.md): What the Google Chat integration is in Prizm — delivers alert notifications to Google Chat spaces via webhook or OAuth.
- [Setup](https://docs.dqlabs.ai/integrations/googlechat/setup.md): Step-by-step guide to connecting Google Chat to Prizm via OAuth or webhook so alert notifications post to a chat space.
- [Overview](https://docs.dqlabs.ai/integrations/teams/overview.md): What the Microsoft Teams integration is in Prizm — delivers alert notification cards to Teams channels via the native Prizm app.
- [Setup](https://docs.dqlabs.ai/integrations/teams/setup.md): Step-by-step guide to connecting Microsoft Teams to Prizm and registering channels by Channel Name and Channel ID.
- [Overview](https://docs.dqlabs.ai/integrations/slack/overview.md): What the Slack integration is in Prizm — delivers real-time alert notifications to Slack channels via OAuth, no webhook tokens required.
- [Setup](https://docs.dqlabs.ai/integrations/slack/setup.md): Step-by-step guide to connecting Slack to Prizm via OAuth and routing alert notifications to public or private channels.
- [Overview](https://docs.dqlabs.ai/integrations/azure-key-vault/overview.md): What the Azure Key Vault integration is in Prizm — retrieves connector credentials at runtime via a service principal instead of storing them.
- [Prerequisites](https://docs.dqlabs.ai/integrations/azure-key-vault/prerequisites.md): Azure configuration required before connecting Azure Key Vault to Prizm — service principal, client secret, and Key Vault access.
- [Setup](https://docs.dqlabs.ai/integrations/azure-key-vault/setup.md): Step-by-step instructions for connecting Azure Key Vault to Prizm using a service principal.
- [Overview](https://docs.dqlabs.ai/integrations/hashicorp-vault/overview.md): What the HashiCorp Vault integration does and how Prizm retrieves credentials using App Role authentication.
- [Prerequisites](https://docs.dqlabs.ai/integrations/hashicorp-vault/prerequisites.md): HashiCorp Vault configuration required before connecting to Prizm — App Role setup, policy, and secret storage.
- [Setup](https://docs.dqlabs.ai/integrations/hashicorp-vault/setup.md): Step-by-step guide to connecting HashiCorp Vault to Prizm using App Role authentication so connector credentials are retrieved at runtime.
- [Overview](https://docs.dqlabs.ai/integrations/aws-secret-manager/overview.md): What the AWS Secret Manager integration does and how Prizm retrieves connector credentials at runtime.
- [Prerequisites](https://docs.dqlabs.ai/integrations/aws-secret-manager/prerequisites.md): AWS configuration required before connecting AWS Secrets Manager to Prizm — IAM policy, user, and access key setup.
- [Setup](https://docs.dqlabs.ai/integrations/aws-secret-manager/setup.md): Step-by-step guide to connecting AWS Secrets Manager to Prizm so connector credentials are retrieved at runtime instead of stored in Prizm.
- [Security Overview](https://docs.dqlabs.ai/security/overview.md): Prizm's comprehensive security architecture combining defense-in-depth, zero trust, and RBAC+ABAC access controls.
- [rbac](https://docs.dqlabs.ai/security/rbac.md): How Prizm's Role-Based Access Control (RBAC) with optional ABAC filters works — roles, groups, permission sets, and access evaluation logic.
- [SSO Integration Overview](https://docs.dqlabs.ai/security/sso.md): Configure SAML 2.0, OAuth 2.0, and LDAP single sign-on for Prizm.
- [Azure Active Directory/Entra ID](https://docs.dqlabs.ai/security/sso/azure.md): Step-by-step guide to configuring Microsoft Entra ID (Azure AD) as the SAML 2.0 identity provider for single sign-on into Prizm.
- [Okta](https://docs.dqlabs.ai/security/sso/okta.md): Step-by-step guide to configuring Okta as the SAML 2.0 identity provider for single sign-on into Prizm.
- [IBM Verify](https://docs.dqlabs.ai/security/sso/ibm-verify.md): Step-by-step guide to configuring IBM Security Verify as the SAML 2.0 identity provider for single sign-on into Prizm.
- [Ping Identity](https://docs.dqlabs.ai/security/sso/ping-identity.md): Step-by-step guide to configuring Ping Identity as the SAML 2.0 identity provider for single sign-on into Prizm.
- [SCIM Integration](https://docs.dqlabs.ai/security/sso/scim.md): Automated user and group lifecycle management in Prizm — configure SCIM provisioning for Okta, Azure AD, Ping Identity, and IBM Security Verify.
- [Data Protection](https://docs.dqlabs.ai/security/data-protection.md): Encryption, masking, tokenization, and audit logging for sensitive data.
- [AI Governance](https://docs.dqlabs.ai/security/aigovernance.md): Transparent, auditable, human-governed AI by design.
- [Compliance](https://docs.dqlabs.ai/security/compliance.md): GDPR, CCPA, HIPAA, SOC 2, and ISO 27001 compliance controls.
- [Deployment Overview](https://docs.dqlabs.ai/deployment/overview.md): Deployment models, environments, and hosting options for PRIZM.
- [FAQ](https://docs.dqlabs.ai/faq.md): Frequently asked questions about Prizm — features, deployment, metrics, integrations, and access.
- [Troubleshooting](https://docs.dqlabs.ai/help/troubleshooting.md): Common errors when connecting to Prizm, deploying agents, and using the platform — and how to resolve them.
- [Glossary](https://docs.dqlabs.ai/help/glossary.md): Definitions for key terms used throughout the Prizm documentation.
- [Platform](https://docs.dqlabs.ai/Platform.md): Configure platform mode, AI model routing, access tokens, cloud storage, and metadata extensions in Prizm.
- [Security](https://docs.dqlabs.ai/settings.md): Configure single sign-on, manage organization members, groups, roles, and permission sets in Prizm.
- [Remediation](https://docs.dqlabs.ai/remediation.md): How Prizm's Remediation settings configure exception writing, metadata capture, and external Spark compute for remediation workflows.
- [Color Mode](https://docs.dqlabs.ai/color-mode.md): How to switch between Light, Dark, and Color modes in Prizm, and what each mode changes in the interface.
- [Overview](https://docs.dqlabs.ai/organization-overview.md): Configure your organization's core identity, AI agent knowledge, and visual branding in Prizm.
- [MCP Access Tokens](https://docs.dqlabs.ai/ai/mcp-access-tokens.md): How to generate, verify, and use MCP access tokens to connect AI clients like Claude Desktop and Cursor to the Prizm platform.
- [1.3.3 Release Notes](https://docs.dqlabs.ai/Release_Notes_v1.3.3.md): Prizm 1.3.3 release notes: Airflow pipeline observability, an ADLS connector, Issue and SLA modules, Microsoft Purview integration, AI guardrails, and vault-based secrets management.
- [1.3.4 Release Notes](https://docs.dqlabs.ai/Release_Notes_v1.3.4.md): Prizm 1.3.4 release notes: Oracle and Sigma Computing connectors, Power BI semantic models, Actions workflow automation, SLA tracking, and expanded vault integration.
- [API Overview](https://docs.dqlabs.ai/api-reference/overview.md): What the Prizm REST API covers, how it's organized, and where to go next.
- [Authentication](https://docs.dqlabs.ai/api-reference/authentication.md): Generate a Prizm API access token and use it to authenticate REST API requests.
- [Response & Error Format](https://docs.dqlabs.ai/api-reference/response-format.md): The response envelope, list/filter pattern, and status codes shared across Prizm API endpoints.
- [Asset](https://docs.dqlabs.ai/api-reference/core/asset.md): Asset endpoints: Get Export Data, Get Asset, Get Asset Schema, Get Lineage, Get Assets, Update Asset, Update Asset Attribute
- [Conversation](https://docs.dqlabs.ai/api-reference/core/conversation.md): Conversation endpoints: Create Conversation, Get Conversations By Object, Get Conversation, Update Conversation, Delete Conversation
- [Cost Performance](https://docs.dqlabs.ai/api-reference/core/cost-performance.md): Get cost performance for a specific asset.
- [Document](https://docs.dqlabs.ai/api-reference/core/document.md): Document endpoints: Create Documentation, Get Latest Documentations, Get Documentations By Object, Get Documentation, Get Documentations List, Update Documentation, Delete Documentation
- [Lineage](https://docs.dqlabs.ai/api-reference/core/lineage.md): Lineage endpoints: Create Lineage, Get Export Data
- [Source](https://docs.dqlabs.ai/api-reference/core/source.md): Source endpoints: Create Source, Get Source By Id, Get Sources, Update Source, Delete Source
- [Source Type](https://docs.dqlabs.ai/api-reference/core/source-type.md): Get all source types from the database.
- [Usage](https://docs.dqlabs.ai/api-reference/core/usage.md): Usage endpoints: Get Usage, Get Usage Users
- [Alert](https://docs.dqlabs.ai/api-reference/metrics/alert.md): Alert endpoints: Get Alerts, Update Alert
- [Alert Cluster](https://docs.dqlabs.ai/api-reference/metrics/alert-cluster.md): Alert Cluster endpoints: Get Alert Cluster By Alert, Get Alert Cluster, List Alert Clusters, Update Alert Cluster
- [Detail](https://docs.dqlabs.ai/api-reference/metrics/detail.md): Detail endpoints: Create Metric Detail, Get Metric Details By Asset, Get Metric Detail, Update Metric Detail, Delete Metric Detail
- [Issue](https://docs.dqlabs.ai/api-reference/metrics/issue.md): Issue endpoints: Create Issue, Get Issue, Get Issues, Update Issue, Delete Issue
- [Parameter](https://docs.dqlabs.ai/api-reference/metrics/parameter.md): Parameter endpoints: Create Parameter, Get Parameter, Get Parameters, Update Parameter, Delete Parameter
- [Steward](https://docs.dqlabs.ai/api-reference/metrics/steward.md): Steward endpoints: Get Actions, Update
- [Pattern](https://docs.dqlabs.ai/api-reference/metrics/pattern.md): Pattern endpoints: Create Pattern, Get Attribute Pattern, Get Pattern, Get Patterns, Update Pattern, Delete Pattern
- [Views](https://docs.dqlabs.ai/api-reference/metrics/views.md): Views endpoints: Get Export Data, Get Metrics From Latest View, Get Metric Latest
- [Template](https://docs.dqlabs.ai/api-reference/metrics/template.md): Template endpoints: Create Metric Template, Get Metric Template, List Metric Templates, List Metric Template Metrics, Update Metric Template, Delete Metric Template
- [Schedule](https://docs.dqlabs.ai/api-reference/schedule/schedule.md): Schedule endpoints: Create Schedule, Get Jobs, Get Job Status, List Autonomous Schedules, List Schedules, Update Schedule, Delete Schedule
- [Notification](https://docs.dqlabs.ai/api-reference/notification/notification.md): Notification endpoints: Get Notifications, Get Notifications List
- [Integrations Overview](https://docs.dqlabs.ai/api-reference/integrations/overview.md): Get all integrations for current user.
- [Channel](https://docs.dqlabs.ai/api-reference/integrations/channel.md): Channel endpoints: Create Channel, Get Channels, Get Channel, Get Channel List, Update Channel, Delete Channel
- [Application](https://docs.dqlabs.ai/api-reference/governance/application.md): Application endpoints: Create Application, Get Export Data, Get Application, Get Applications, Update Application, Delete Application
- [Asset Governance](https://docs.dqlabs.ai/api-reference/governance/asset-governance.md): Asset Governance endpoints: Create Asset Governance, Get Asset Governance By Asset, Get Asset Governance By Asset Raw, Get Asset Governance By Source, Get Asset Governance, Get Asset Governance Records, Update Asset Governance, Delete Asset Governance
- [Domain](https://docs.dqlabs.ai/api-reference/governance/domain.md): Domain endpoints: Create Domain, Get Export Data, Get Domain, Get Domains, Get Domains Tree List, Update Domain, Delete Domain
- [Field](https://docs.dqlabs.ai/api-reference/governance/field.md): Field endpoints: Create Field, Get Field Properties, Get Tabs By Level, Get Tabs And Groups, Get Fields, Update Field Properties, Update Field Group, Update Field Tab, Update Tab Icon, Update Field, Delete Field Group, Delete Field Tab, Delete Field
- [Glossary](https://docs.dqlabs.ai/api-reference/governance/glossary.md): Glossary endpoints: Create Glossary, Get Tree View, Get Glossary, Get Glossaries, Update Glossary, Delete Glossary
- [Glossary Category](https://docs.dqlabs.ai/api-reference/governance/glossary-category.md): Glossary Category endpoints: Create Glossary Category, Get Glossary Category, Get Glossary Categories, Update Glossary Category, Delete Glossary Category
- [Product](https://docs.dqlabs.ai/api-reference/governance/product.md): Product endpoints: Create Product, Get Export Data, Get Product, Get Product Lineage, Get Output Port, Get Products, Update Product, Update Product From Yaml, Delete Product
- [Tag](https://docs.dqlabs.ai/api-reference/governance/tag.md): Tag endpoints: Create Tag, Get Export Data, Get Tag, Get Tags, Get Tags Tree List, Update Tag, Delete Tag
- [Term](https://docs.dqlabs.ai/api-reference/governance/term.md): Term endpoints: Create Term, Get Export Data, Get Term, Get Active Terms, Get Terms, Update Term, Delete Term
- [Logs](https://docs.dqlabs.ai/api-reference/logging/logs.md): Logs endpoints: Get Logs, Get Audit Logs, Get Imports, Get User Activity Logs