# DQLabs PRIZM

## Docs

- [Converse MCP Tools](https://docs.prizmdata.ai/ai/converse-mcp.md): How Converse uses MCP tool integrations to execute workflows from natural language.
- [Glossary Generation](https://docs.prizmdata.ai/ai/glossary-generation.md): Automated business glossary creation using organization context and domain data.
- [GPT Prompts](https://docs.prizmdata.ai/ai/gpt-prompts.md): AI-driven prompts for metadata generation, recommendations, and quality workflows.
- [Model Configuration](https://docs.prizmdata.ai/ai/model-configuration.md): Configure Claude, GPT, and Gemini model routing by task complexity and cost.
- [AI-Powered Experiences](https://docs.prizmdata.ai/ai/overview.md): Intelligent, context-aware AI capabilities that enhance user decision-making and automate complex data workflows.
- [AI Recommendations](https://docs.prizmdata.ai/ai/recommendations.md): Quality metric, business metric, and governance recommendations powered by AI.
- [Alation Integration](https://docs.prizmdata.ai/alation.md): Connect Prizm to Alation to sync data quality metrics, lineage, and catalog metadata with your data intelligence platform.
- [Asset](https://docs.prizmdata.ai/architecture/asset.md): Any data object cataloged, governed, and monitored within DQLabs Prizm.
- [Asset Details](https://docs.prizmdata.ai/architecture/asset-details.md): In-depth view of a single asset
- [Attribute](https://docs.prizmdata.ai/architecture/attribute.md)
- [Context Module](https://docs.prizmdata.ai/architecture/context.md): Platform-level context management for AI reasoning and metadata enrichment.
- [Criticality Scoring](https://docs.prizmdata.ai/architecture/criticality.md): Know instantly which assets matter most, before they break.
- [Data Model](https://docs.prizmdata.ai/architecture/data-model.md): Core entity relationships and schema design for Prizm's MetaStore.
- [Entities](https://docs.prizmdata.ai/architecture/entities.md): Child (One level down) in the asset hierarchy
- [Entity List](https://docs.prizmdata.ai/architecture/entity-list.md)
- [Infrastructure](https://docs.prizmdata.ai/architecture/infrastructure.md): Kubernetes deployment topology, sizing, and infrastructure requirements.
- [Metrics](https://docs.prizmdata.ai/architecture/metrics.md): Metric types, execution engine, scheduling, and quality scoring pipeline.
- [Notifications](https://docs.prizmdata.ai/architecture/notifications.md): Alert routing to Slack, Email, Jira, PagerDuty, and ServiceNow.
- [Architecture Overview](https://docs.prizmdata.ai/architecture/overview.md): Prizm's cloud-native, containerized architecture designed for horizontal scalability and seamless enterprise integration.
- [Scheduling](https://docs.prizmdata.ai/architecture/scheduling.md): CRON, event-based, and AI-driven intelligent schedule management.
- [Score Entity](https://docs.prizmdata.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.
- [Usage](https://docs.prizmdata.ai/architecture/usage.md)
- [Asset Types](https://docs.prizmdata.ai/asset-types.md): Native classification assigned to a data object
- [Atlan Integration](https://docs.prizmdata.ai/atlan.md): Connect Prizm to Atlan to sync data quality metrics, lineage, and catalog metadata with your active metadata platform.
- [AWS Secrets Manager Integration](https://docs.prizmdata.ai/aws-secrets-manager.md): Use AWS Secrets Manager to securely store and rotate credentials used by Prizm connectors.
- [Azure Key Vault Integration](https://docs.prizmdata.ai/azure-key-vault.md): Use Azure Key Vault to securely store and manage credentials used by Prizm connectors — no plaintext secrets stored in Prizm.
- [Alerts: Automatic data drift and quality notifications](https://docs.prizmdata.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.
- [Assets: Discover and manage your context in PRIZM](https://docs.prizmdata.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.
- [Issues: Manage data quality problems from detection to fix](https://docs.prizmdata.ai/concepts/issues.md): PRIZM issues provide a structured workflow to assign, track, and resolve data quality problems across priority levels and lifecycle statuses.
- [Metrics: Monitor data quality and pipeline health in PRIZM](https://docs.prizmdata.ai/concepts/metrics.md): PRIZM metrics track data quality score, active pipeline count, system uptime, and total data volume — surfaced automatically on a single dashboard.
- [Exception Management](https://docs.prizmdata.ai/data-quality/exceptions.md): Capture, route, and resolve data quality violations through governance workflows.
- [Quality Metrics](https://docs.prizmdata.ai/data-quality/metrics.md): Configure completeness, freshness, validity, uniqueness, volume, and custom SQL metrics.
- [Data Quality Overview](https://docs.prizmdata.ai/data-quality/overview.md): Prizm's intelligent data quality engine — from automated profiling and adaptive rules to scoring, exceptions, and YAML-based DQaC.
- [Profile Insights](https://docs.prizmdata.ai/data-quality/profile.md)
- [Data Profiling](https://docs.prizmdata.ai/data-quality/profiling.md): Statistical sampling and baseline creation for anomaly detection and quality metrics.
- [Quality Scoring](https://docs.prizmdata.ai/data-quality/scoring.md): How Prizm computes, aggregates, and presents quality scores at every level.
- [YAML Metrics - DQaC](https://docs.prizmdata.ai/data-quality/yaml-dqac.md): Define quality metrics as code using YAML for version-controlled, CI/CD-integrated data quality.
- [Cloud Deployment](https://docs.prizmdata.ai/deployment/cloud.md): Fully managed SaaS deployment with automatic scaling and zero infrastructure burden.
- [Customer Onboarding](https://docs.prizmdata.ai/deployment/customer-onboarding.md): Structured onboarding program for new Prizm deployments.
- [Hybrid Deployment](https://docs.prizmdata.ai/deployment/hybrid.md): Control plane in cloud with data agents on-premises for maximum flexibility.
- [On-Premises Deployment](https://docs.prizmdata.ai/deployment/on-premises.md): Self-hosted deployment in your own data center or private cloud.
- [Deployment Overview](https://docs.prizmdata.ai/deployment/overview.md): Deploy Prizm in cloud, on-premises, or hybrid environments with flexible containerized architecture.
- [Email](https://docs.prizmdata.ai/email.md): Send Prizm quality alerts and anomaly notifications to email recipients and distribution lists.
- [PRIZM Frequently Asked Questions](https://docs.prizmdata.ai/faq.md): Common questions about PRIZM — answered.
- [Google Chat Integration](https://docs.prizmdata.ai/google-chat.md): Send Prizm data quality alerts, anomaly notifications, and observability digests to Google Chat spaces and direct messages.
- [Monitor query performance and usage with Analytics](https://docs.prizmdata.ai/guides/analytics.md): Use the PRIZM Analytics page to track total queries, success rates, average response times, active users, cache hit rates, and error rates.
- [Explore asset details, schema, lineage, and metrics](https://docs.prizmdata.ai/guides/asset-details.md): Use PRIZM's asset detail view to inspect schema, metrics, lineage, documentation, and recent alerts for any data asset in your catalog.
- [Use the PRIZM AI Chat Assistant to explore your data](https://docs.prizmdata.ai/guides/chat-assistant.md): Use PRIZM's AI Chat Assistant to query pipeline status, current alerts, and asset health in natural language — no SQL or technical expertise required.
- [View, triage, and respond to PRIZM data quality alerts](https://docs.prizmdata.ai/guides/managing-alerts.md): Navigate the Alerts page in PRIZM, understand severity levels, read drift status, interpret percent change values, and act on triggered alerts.
- [Navigate the PRIZM dashboard, nav bar, and search](https://docs.prizmdata.ai/guides/navigating-dashboard.md): Learn how to use the PRIZM dashboard and top navigation bar to access Assets, Metrics, Alerts, Issues, Analytics, and the global search.
- [Search and discover assets across your data ecosystem](https://docs.prizmdata.ai/guides/searching-assets.md): Use PRIZM's asset search to find tables, views, queries, pipelines, reports, and semantics across all your connected databases and platforms.
- [Track and resolve data quality issues with PRIZM Issues](https://docs.prizmdata.ai/guides/tracking-issues.md): Use the PRIZM Issues page to manage data quality problems from detection through resolution using priority levels and status workflows.
- [HashiCorp Vault Integration](https://docs.prizmdata.ai/hashicorp-vault.md): Use HashiCorp Vault to securely manage and inject secrets into Prizm connectors without storing credentials in Prizm.
- [What is PRIZM?  ](https://docs.prizmdata.ai/introduction.md): An introduction to DQLabs' AI-native platform that unifies data observability, quality, and context into one operational layer.
- [Key Concepts](https://docs.prizmdata.ai/introduction/key-concepts.md): Essential terminology and concepts you need to know when working with Prizm.
- [What is Prizm?](https://docs.prizmdata.ai/introduction/overview.md): DQLabs Prizm is a comprehensive, AI-native platform for data quality, observability, and cataloging — built for modern enterprises.
- [Quick Start](https://docs.prizmdata.ai/introduction/quick-start.md): Get Prizm connected to your data and generating quality insights in under an hour.
- [Impact Analysis](https://docs.prizmdata.ai/lineage/impact-analysis.md): Compute blast radius and assess risk before making any upstream changes.
- [Lineage](https://docs.prizmdata.ai/lineage/overview.md): Comprehensive data lineage tracking — from source to consumption — with automated impact analysis and root cause identification.
- [Upstream & Downstream Tracking](https://docs.prizmdata.ai/lineage/upstream-downstream.md): Trace data origins and map all downstream consumers with column-level precision.
- [Sign in, and create a PRIZM account](https://docs.prizmdata.ai/login.md): Learn how to sign in to PRIZM with your email, and create a new account from the login screen.
- [AI Stewardship](https://docs.prizmdata.ai/platform/ai-stewardship.md): Responsible, governed AI usage with transparent recommendations and human oversight.
- [Autonomous Intelligence](https://docs.prizmdata.ai/platform/autonomous-intelligence.md): Prizm's 5-level autonomous intelligence architecture that powers continuous, self-improving data management.
- [Converse — AI Chat Interface](https://docs.prizmdata.ai/platform/converse.md): Natural language data management powered by Claude and MCP tools — no code required.
- [Multi-Agent Architecture](https://docs.prizmdata.ai/platform/multi-agent-architecture.md): How Prizm's specialized AI agents coordinate to handle data management at scale.
- [Platform Overview](https://docs.prizmdata.ai/platform/overview.md): DQLabs Prizm is a multi-agentic, AI-native platform for data quality, cataloging, and observability.
- [Get up and running with PRIZM: a step-by-step guide](https://docs.prizmdata.ai/quickstart.md)
- [Compliance](https://docs.prizmdata.ai/security/compliance.md): GDPR, CCPA, HIPAA, SOC 2, and ISO 27001 compliance controls.
- [Data Protection](https://docs.prizmdata.ai/security/data-protection.md): Encryption, masking, tokenization, and audit logging for sensitive data.
- [Security Overview](https://docs.prizmdata.ai/security/overview.md): Prizm's comprehensive security architecture combining defense-in-depth, zero trust, and RBAC+ABAC access controls.
- [Role-Based Access Control](https://docs.prizmdata.ai/security/rbac.md): Detailed RBAC configuration, custom roles, and permission management.
- [SSO Integration](https://docs.prizmdata.ai/security/sso.md): Configure SAML 2.0, OAuth 2.0, and LDAP single sign-on for Prizm.
- [Slack Integration](https://docs.prizmdata.ai/slack.md): Send Prizm data quality alerts, anomaly notifications, and observability digests to Slack channels.
- [Azure Data Lake Storage](https://docs.prizmdata.ai/sources/adls.md): ADLS Gen2 integration with hierarchical namespace and file-level lineage.
- [Apache Airflow](https://docs.prizmdata.ai/sources/airflow.md): DAG discovery, task-level lineage, and execution metadata from Airflow.
- [BigQuery](https://docs.prizmdata.ai/sources/bigquery.md): Google BigQuery dataset discovery and query-level lineage extraction.
- [Databricks](https://docs.prizmdata.ai/sources/databricks.md): Unified Delta Lake, Unity Catalog, and Databricks SQL integration.
- [dbt](https://docs.prizmdata.ai/sources/dbt.md): Parse dbt manifests for model lineage, test results, and documentation sync.
- [Jira](https://docs.prizmdata.ai/sources/jira.md): Create Jira issues from data quality exceptions and track resolution.
- [Data Sources Overview](https://docs.prizmdata.ai/sources/overview.md): Connect Prizm to your entire data ecosystem — from cloud data warehouses to BI tools, pipelines, and ITSM platforms.
- [Power BI](https://docs.prizmdata.ai/sources/power-bi.md): Power BI dataset, report, and dashboard lineage with semantic model integration.
- [Amazon Redshift](https://docs.prizmdata.ai/sources/redshift.md): Redshift cluster and Serverless support with lineage extraction.
- [SAP HANA](https://docs.prizmdata.ai/sources/sap-hana.md): SAP HANA in-memory database connectivity for enterprise data assets.
- [ServiceNow](https://docs.prizmdata.ai/sources/servicenow.md): Auto-create and sync incidents and change requests from Prizm quality events.
- [Snowflake Overview](https://docs.prizmdata.ai/sources/snowflake.md): What Snowflake is, why connecting it to Prizm unlocks data intelligence, and what capabilities Prizm supports.
- [FAQ](https://docs.prizmdata.ai/sources/snowflake-faq.md): Common questions about connecting Snowflake to Prizm, permissions, scoping, and feature behaviour.
- [What Prizm Collects from Snowflake](https://docs.prizmdata.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 four job types.
- [SQL Server](https://docs.prizmdata.ai/sources/sql-server.md): Microsoft SQL Server and Azure SQL Database integration.
- [Tableau](https://docs.prizmdata.ai/sources/tableau.md): Workbook and datasource discovery with field-level lineage back to source tables.
- [Setup](https://docs.prizmdata.ai/sources/test.md): Prerequisites, authentication options, and step-by-step instructions for connecting Snowflake to Prizm.
- [Microsoft Teams Integration](https://docs.prizmdata.ai/teams.md): Send Prizm data quality alerts, anomaly notifications, and observability digests to Microsoft Teams channels.
- [Troubleshoot login, assets, alerts, and performance in PRIZM](https://docs.prizmdata.ai/troubleshooting.md): Step-by-step solutions for login failures, missing assets, confusing alert counts, and slow page loads you may encounter while using PRIZM.