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Prefect (public preview)

🧑‍🎓 Monte Carlo University

  • Getting Started | MC University
  • Detect
    • 🧑‍🎓 Assets
    • 🧑‍🎓 Lineage
    • 🧑‍🎓 Freshness Monitor
    • 🧑‍🎓 Volume Monitor
    • 🧑‍🎓 Schema Monitor
    • 🧑‍🎓 Metric Monitor
    • 🧑‍🎓 SQL Monitors
    • 🧑‍🎓 Comparison Rules
    • 🧑‍🎓 Monitors as Code
    • 🧑‍🎓 Validation Monitor
    • 🧑‍🎓 JSON Schema Monitor
    • 🧑‍🎓 Query Performance Monitor
    • 🧑‍🎓 Data Products
    • 🧑‍🎓 Data Profiler
    • 🧑‍🎓 Warehouse Navigation
  • Triage
    • 🧑‍🎓 Key Assets
    • 🧑‍🎓 Domains
    • 🧑‍🎓 Alert Feed
    • 🧑‍🎓 Incident Management Overview
    • 🧑‍🎓 Status Updates
    • 🧑‍🎓 Integrating Airflow
    • 🧑‍🎓 Notifications as Code
    • 🧑‍🎓 Triaging Notifications
  • Resolve
    • 🧑‍🎓 Circuit Breakers
    • 🧑‍🎓 Monte Carlo Python SDK
    • 🧑‍🎓 Insights
    • 🧑‍🎓 Resolving Notifications
    • 🧑‍🎓 Daily Digest
    • 🧑‍🎓 Usage Management
    • 🧑‍🎓 Performance
    • 🧑‍🎓 Exclusion Windows
    • 🧑‍🎓 Monitor Templates
  • Measure
    • 🧑‍🎓 Monitor Tags & Dimensions
    • 🧑‍🎓 Data Quality Dashboard
    • 🧑‍🎓 Data Operations Dashboard
    • 🧑‍🎓 Table Health Dashboard
    • 🧑‍🎓 Monte Carlo API
  • Settings
    • 🧑‍🎓 SSO, User Groups and Permissions
    • 🧑‍🎓 Billing Page
    • 🧑‍🎓 User Profiles and Email Digest
  • Monte Carlo Mastery Series
    • 🧑‍🎓 How To Avoid Alert Fatigue & Manage Incidents Effectively
    • 🧑‍🎓 Creating an Alert Strategy that Guarantees Action
    • 🧑‍🎓 Creating a Proactive Monitoring Strategy with Monte Carlo
    • 🧑‍🎓 How to Scale Data Quality with Data Observability + Data Testing
    • 🧑‍🎓 Setting Up Your Monitoring Strategy for Maximum Impact
    • 🧑‍🎓 How to Optimize Cost, Stakeholder Experience and Data Trust with Performance
    • 🧑‍🎓 Optimize Data Quality for Key Assets with Data Products
    • 🧑‍🎓 How to Roll Out Data Observability to Your Organization

Product Usage

  • Monte Carlo at a Glance
  • Quick Start Guide
  • Alerts
    • Interacting with Alerts
    • Alert Statuses
    • Introducing: Alerts
    • Marking Alerts as Incidents
  • Dashboards
    • Data operations
    • Data quality
    • Table health
    • Insights
      • Importance Score Calculation
      • Insight: Key Assets
      • Insight: Coverage Overview
      • Insight: Table Cleanup Suggestions
      • Insight: Field-level Cleanup Suggestions
      • Insight: Field importance scores
      • Insight: Events
      • Insight: Rule Results
      • Insight: Field Health Suggestions
      • Insight: Misconfigured Monitors
      • Insight: BI Dashboard Analytics
      • Insight: Alert History
      • Insight: Notifications by custom monitor
      • Insight: Data Product Recommendations
    • Activity
  • Performance
  • Assets
    • Using Assets
    • Muting
    • Asset tags
    • Field Lineage
    • Data Profiler
  • Data Products
  • Settings
    • Notifications
    • Authorization
    • Single Sign On (SSO)
      • Map SSO Groups to Authorization Groups
      • FAQs and Troubleshooting
    • SCIM
      • SCIM Provision with Okta
      • SCIM Provision with Microsoft Entra ID
    • Table Monitors
      • Recommended monitoring strategies for tables
  • Data exports (public preview)
  • User Profile
    • Weekly Data Reliability Summary Email

Monitors

  • Monitors Overview
  • Table
    • Freshness
    • Volume
    • Schema Change
  • Metrics
    • Metric Monitors
      • Available Metrics
      • Custom Metrics
      • Segmentation
      • Backfill History
    • Comparison Monitors
  • Validations
    • Validation Monitors
      • Available Conditions
    • Custom SQL
      • Templates
    • Freshness Rules
    • Volume Rules
  • Job
    • Query Performance
  • Additional Resources
    • Reducing noise from monitors
    • Partition filtering for monitors
    • Failure notifications
    • Misconfigured monitors
    • Allocating query cost
    • Monitor tags
    • Changes to legacy monitors

Anomaly Detection

  • Overview
  • Tuning thresholds
    • Exclusion Windows
    • Managing Holidays

Investigation

  • Root Cause Analysis (RCA) Insights
  • Pull requests (public preview)

Architecture & Security

  • Overview
  • Deployments
    • AWS: Data Store Deployment
    • AWS: Agent Deployment
    • Azure: Data Store Deployment
    • Azure: Agent Deployment
    • GCP: Data Store Deployment
    • GCP: Agent Deployment
  • Security and Compliance
    • Data Sampling
  • Network Connectivity
    • AWS PrivateLink
      • AWS Endpoint Services
    • Azure Private Link
  • Data Collector
    • Upgrading the Collector
  • Additional Resources
    • Agent Management
    • Rotating Keys
    • Integration Specific Deployments
      • Snowflake Native App (public preview)
    • Platform Migrations
      • Migrating from a Remote Data Collector

Developer Tools

  • Overview
  • API
    • API Authentication
    • API Explorer
    • API Example Use Cases
      • General
      • Integrations
      • Lineage
      • Monitors
    • Troubleshooting and FAQs: API
  • SDK
  • CLI
    • Secret management with the CLI
  • Monitors as Code
    • Locally setting up Monitors as Code
    • Generating MC monitors from dbt tests
    • Notifications as Code

Integrations

  • Data Warehouses
    • BigQuery
    • Redshift
    • Snowflake
    • Azure Synapse (public preview)
    • Teradata
    • MotherDuck (public preview)
    • Dremio (public preview)
    • Adding Additional Warehouses
  • Databricks
    • Troubleshooting & FAQ: Databricks
    • End of life for Notebook-based Metadata Collection on Databricks
    • Databricks Workflows (public preview)
  • Data Lakes
    • Metastores
      • Glue
      • Hive Metastore
    • Query Engines
      • Athena
      • Presto (public preview)
      • Hive SQL (public preview)
      • Spark
      • Presto, Hive, & EMR: Query Logs
    • Troubleshooting & FAQ: Data Lakes
    • Appendix: Data Lakes
      • Creating IAM Roles
      • Auto-generating IAM Policy Documents
      • Creating event notifications
      • Adding Additional Connections
  • Transactional Databases
    • Azure SQL Database (public preview)
    • MySQL (public preview)
    • Oracle DB (public preview)
    • Postgres
    • SAP HANA (public preview)
    • SQL Server
  • Vector Databases
    • Pinecone (public preview)
    • Chroma
    • Databricks Vector Search
    • pgvector
  • BI Tools
    • Looker
      • Troubleshooting and FAQs: Looker
    • Mode BI
    • Periscope/Sisense (public preview)
    • Power BI
      • Power BI - Credential Creation Process
    • Sigma (public preview)
    • Tableau - Connected Apps version, recommended
      • Troubleshooting and FAQs: Tableau
      • Tableau - PAT version, CLI-only initial setup
      • Troubleshooting and FAQs: Tableau (PAT version, CLI-only)
    • Adding or Modifying BI Connections
    • Hex (public preview)
  • Orchestration & Transformation
    • Airflow
      • Airflow in Lineage
      • Airflow Alerts and Task Observability
    • dbt Integration
      • dbt Cloud
      • dbt Core
      • dbt Failures As Monte Carlo Alerts
    • Fivetran Integration
      • Troubleshooting and FAQ: Fivetran
    • Informatica (public preview)
    • Kafka (public preview)
      • Confluent Cloud (public preview)
      • Self-Hosted Kafka (public preview)
      • MSK (public preview)
    • Prefect (public preview)
    • Circuit breakers
      • Using providers
    • Azure Data Factory (public preview)
      • Additional Support for ADF in Lineage
  • Notifications & Collaboration
    • GitHub
    • Jira
    • Incident.io
    • Microsoft Teams
    • [Legacy version] Microsoft Teams
    • Opsgenie
    • PagerDuty
    • ServiceNow
    • Slack
    • Webex
    • Webhooks
    • GitLab (public preview)
    • Azure DevOps (Public Preview)
    • Google Chat
  • Catalogs
    • Alation
    • Atlan
    • Collibra (public preview)
    • data.world
    • Secoda
    • Select Star
  • Data Collection: Details per Integration
  • Data Asset Validators
  • Using self-hosted credentials
  • Updating an Integration

Resource Center

  • Advanced Usage
    • PII Filtering
    • Muting schemas/tables using regex
    • Audit Logging
  • Integration & Feature Lifecycles

Prefect (public preview)

Suggest Edits

With the integration between Monte Carlo and Prefect teams can seamlessly manage the reliability of their data engineering workflows. This includes augmenting linage, enriching metadata, executing circuit breakers, and more.

Documentation is available on Prefect's site:

  • Prefect tasks and flows to interact with Monte Carlo

Updated 5 months ago