Paradime Help Docs
Get Started
  • 🚀Introduction
  • 📃Guides
    • Paradime 101
      • Getting Started with your Paradime Workspace
        • Creating a Workspace
        • Setting Up Data Warehouse Connections
        • Managing workspace configurations
        • Managing Users in the Workspace
      • Getting Started with the Paradime IDE
        • Setting Up a dbt™ Project
        • Creating a dbt™ Model
        • Data Exploration in the Code IDE
        • DinoAI: Accelerating Your Analytics Engineering Workflow
          • DinoAI Agent
            • Creating dbt Sources from Data Warehouse
            • Generating Base Models
            • Building Intermediate/Marts Models
            • Documentation Generation
            • Data Pipeline Configuration
            • Using .dinorules to Tailor Your AI Experience
          • Accelerating GitOps
          • Accelerating Data Governance
          • Accelerating dbt™ Development
        • Utilizing Advanced Developer Features
          • Visualize Data Lineage
          • Auto-generated Data Documentation
          • Enforce SQL and YAML Best Practices
          • Working with CSV Files
      • Managing dbt™ Schedules with Bolt
        • Creating Bolt Schedules
        • Understanding schedule types and triggers
        • Viewing Run History and Analytics
        • Setting Up Notifications
        • Debugging Failed Runs
    • Migrating from dbt™ cloud to Paradime
  • 🔍Concepts
    • Working with Git
      • Git Lite
      • Git Advanced
      • Read Only Branches
      • Delete Branches
      • Merge Conflicts
      • Configuring Signed Commits on Paradime with SSH Keys
      • GitHub Branch Protection Guide: Preventing Direct Commits to Main
    • dbt™ fundamentals
      • Getting started with dbt™
        • Introduction
        • Project Strucuture
        • Working with Sources
        • Testing Data Quality
        • Models and Transformations
      • Configuring your dbt™ Project
        • Setting up your dbt_project.yml
        • Defining Your Sources in sources.yml
        • Testing Source Freshness
        • Unit Testing
        • Working with Tags
        • Managing Seeds
        • Environment Management
        • Variables and Parameters
        • Macros
        • Custom Tests
        • Hooks & Operational Tasks
        • Packages
      • Model Materializations
        • Table Materialization
        • View​ Materialization
        • Incremental Materialization
          • Using Merge for Incremental Models
          • Using Delete+Insert for Incremental Models
          • Using Append for Incremental Models
          • Using Microbatch for Incremental Models
        • Ephemeral Materialization
        • Snapshots
      • Running dbt™
        • Mastering the dbt™ CLI
          • Commands
          • Methods
          • Selector Methods
          • Graph Operators
    • Paradime fundamentals
      • Global Search
        • Paradime Apps Navigation
        • Invite users to your workspace
        • Search and preview Bolt schedules status
      • Using --defer in Paradime
      • Workspaces and data mesh
    • Data Warehouse essentials
      • BigQuery Multi-Project Service Account
  • 📖Documentation
    • DinoAI
      • Agent Mode
        • Use Cases
          • Creating Sources from your Warehouse
          • Generating dbt™ models
          • Fixing Errors with Jira
          • Researching with Perplexity
          • Providing Additional Context Using PDFs
      • Context
        • File Context
        • Directory Context
      • Tools and Features
        • Warehouse Tool
        • File System Tool
        • PDF Tool
        • Jira Tool
        • Perplexity Tool
        • Terminal Tool
        • Coming Soon Tools...
      • .dinorules
      • Ask Mode
      • Version Control
      • Production Pipelines
      • Data Documentation
    • Code IDE
      • User interface
        • Autocompletion
        • Context Menu
        • Flexible layout
        • "Peek" and "Go To" Definition
        • IDE preferences
        • Shortcuts
      • Left Panel
        • DinoAI Coplot
        • Search, Find, and Replace
        • Git Lite
        • Bookmarks
      • Command Panel
        • Data Explorer
        • Lineage
        • Catalog
        • Lint
      • Terminal
        • Running dbt™
        • Paradime CLI
      • Additional Features
        • Scratchpad
    • Bolt
      • Creating Schedules
        • 1. Schedule Settings
        • 2. Command Settings
          • dbt™ Commands
          • Python Scripts
          • Elementary Commands
          • Lightdash Commands
          • Tableau Workbook Refresh
          • Power BI Dataset Refresh
          • Paradime Bolt Schedule Toggle Commands
          • Monte Carlo Commands
        • 3. Trigger Types
        • 4. Notification Settings
        • Templates
          • Run and Test all your dbt™ Models
          • Snapshot Source Data Freshness
          • Build and Test Models with New Source Data
          • Test Code Changes On Pull Requests
          • Re-executes the last dbt™ command from the point of failure
          • Deploy Code Changes On Merge
          • Create Jira Tickets
          • Trigger Census Syncs
          • Trigger Hex Projects
          • Create Linear Issues
          • Create New Relic Incidents
          • Create Azure DevOps Items
        • Schedules as Code
      • Managing Schedules
        • Schedule Configurations
        • Viewing Run Log History
        • Analyzing Individual Run Details
          • Configuring Source Freshness
      • Bolt API
      • Special Environment Variables
        • Audit environment variables
        • Runtime environment variables
      • Integrations
        • Reverse ETL
          • Hightouch
        • Orchestration
          • Airflow
          • Azure Data Factory (ADF)
      • CI/CD
        • Turbo CI
          • Azure DevOps
          • BitBucket
          • GitHub
          • GitLab
          • Paradime Turbo CI Schema Cleanup
        • Continuous Deployment with Bolt
          • GitHub Native Continuous Deployment
          • Using Azure Pipelines
          • Using BitBucket Pipelines
          • Using GitLab Pipelines
        • Column-Level Lineage Diff
          • dbt™ mesh
          • Looker
          • Tableau
          • Thoughtspot
    • Radar
      • Get Started
      • Cost Management
        • Snowflake Cost Optimization
        • Snowflake Cost Monitoring
        • BigQuery Cost Monitoring
      • dbt™ Monitoring
        • Schedules Dashboard
        • Models Dashboard
        • Sources Dashboard
        • Tests Dashboard
      • Team Efficiency Tracking
      • Real-time Alerting
      • Looker Monitoring
    • Data Catalog
      • Data Assets
        • Looker assets
        • Tableau assets
        • Power BI assets
        • Sigma assets
        • ThoughtSpot assets
        • Fivetran assets
        • dbt™️ assets
      • Lineage
        • Search and Discovery
        • Filters and Nodes interaction
        • Nodes navigation
        • Canvas interactions
        • Compare Lineage version
    • Integrations
      • Dashboards
        • Sigma
        • ThoughtSpot (Beta)
        • Lightdash
        • Tableau
        • Looker
        • Power BI
        • Streamlit
      • Code IDE
        • Cube CLI
        • dbt™️ generator
        • Prettier
        • Harlequin
        • SQLFluff
        • Rainbow CSV
        • Mermaid
          • Architecture Diagrams
          • Block Diagrams Documentation
          • Class Diagrams
          • Entity Relationship Diagrams
          • Gantt Diagrams
          • GitGraph Diagrams
          • Mindmaps
          • Pie Chart Diagrams
          • Quadrant Charts
          • Requirement Diagrams
          • Sankey Diagrams
          • Sequence Diagrams
          • State Diagrams
          • Timeline Diagrams
          • User Journey Diagrams
          • XY Chart
          • ZenUML
        • pre-commit
          • Paradime Setup and Configuration
          • dbt™️-checkpoint hooks
            • dbt™️ Model checks
            • dbt™️ Script checks
            • dbt™️ Source checks
            • dbt™️ Macro checks
            • dbt™️ Modifiers
            • dbt™️ commands
            • dbt™️ checks
          • SQLFluff hooks
          • Prettier hooks
      • Observability
        • Elementary Data
          • Anomaly Detection Tests
            • Anomaly tests parameters
            • Volume anomalies
            • Freshness anomalies
            • Event freshness anomalies
            • Dimension anomalies
            • All columns anomalies
            • Column anomalies
          • Schema Tests
            • Schema changes
            • Schema changes from baseline
          • Sending alerts
            • Slack alerts
            • Microsoft Teams alerts
            • Alerts Configuration and Customization
          • Generate observability report
          • CLI commands and usage
        • Monte Carlo
      • Storage
        • Amazon S3
        • Snowflake Storage
      • Reverse ETL
        • Hightouch
      • CI/CD
        • GitHub
        • Spectacles
      • Notifications
        • Microsoft Teams
        • Slack
      • ETL
        • Fivetran
    • Security
      • Single Sign On (SSO)
        • Okta SSO
        • Azure AD SSO
        • Google SAML SSO
        • Google Workspace SSO
        • JumpCloud SSO
      • Audit Logs
      • Security model
      • Privacy model
      • FAQs
      • Trust Center
      • Security
    • Settings
      • Workspaces
      • Git Repositories
        • Importing a repository
          • Azure DevOps
          • BitBucket
          • GitHub
          • GitLab
        • Update connected git repository
      • Connections
        • Code IDE environment
          • Amazon Athena
          • BigQuery
          • Clickhouse
          • Databricks
          • Dremio
          • DuckDB
          • Firebolt
          • Microsoft Fabric
          • Microsoft SQL Server
          • MotherDuck
          • PostgreSQL
          • Redshift
          • Snowflake
          • Starburst/Trino
        • Scheduler environment
          • Amazon Athena
          • BigQuery
          • Clickhouse
          • Databricks
          • Dremio
          • DuckDB
          • Firebolt
          • Microsoft Fabric
          • Microsoft SQL Server
          • MotherDuck
          • PostgreSQL
          • Redshift
          • Snowflake
          • Starburst/Trino
        • Manage connections
          • Set alternative default connection
          • Delete connections
        • Cost connection
          • BigQuery cost connection
          • Snowflake cost connection
        • Connection Security
          • AWS PrivateLink
            • Snowflake PrivateLink
            • Redshift PrivateLink
          • BigQuery OAuth
          • Snowflake OAuth
        • Optional connection attributes
      • Notifications
      • dbt™
        • Upgrade dbt Core™ version
      • Users
        • Invite users
        • Manage Users
        • Enable Auto-join
        • Users and licences
        • Default Roles and Permissions
        • Role-based access control
      • Environment Variables
        • Bolt Schedules Environment Variables
        • Code IDE Environment Variables
  • 💻Developers
    • GraphQL API
      • Authentication
      • Examples
        • Audit Logs API
        • Bolt API
        • User Management API
        • Workspace Management API
    • Python SDK
      • Getting Started
      • Modules
        • Audit Log
        • Bolt
        • Lineage Diff
        • Custom Integration
        • User Management
        • Workspace Management
    • Paradime CLI
      • Getting Started
      • Bolt CLI
    • Webhooks
      • Getting Started
      • Custom Webhook Guides
        • Create an Azure DevOps Work item when a Bolt run complete with errors
        • Create a Linear Issue when a Bolt run complete with errors
        • Create a Jira Issue when a Bolt run complete with errors
        • Trigger a Slack notification when a Bolt run is overrunning
    • Virtual Environments
      • Using Poetry
      • Troubleshooting
    • API Keys
    • IP Restrictions in Paradime
    • Company & Workspace token
  • 🙌Best Practices
    • Data Mesh Setup
      • Configure Project dependencies
      • Model access
      • Model groups
  • ‼️Troubleshooting
    • Errors
    • Error List
    • Restart Code IDE
  • 🔗Other Links
    • Terms of Service
    • Privacy Policy
    • Paradime Blog
Powered by GitBook
On this page

Was this helpful?

Last updated 7 months ago

Was this helpful?

For new Elementary data users, we recommend starting out to understand the core concepts and implementation.

With Elementary, an open-source data observability solution, data and analytics engineers can achieve instant visibility into their dbt projects. It streamlines the process of detecting data anomalies, issuing targeted alerts, and unraveling both impact and root causes.

Getting started

Paradime seamlessly integrates with Elementary, offering a ready-to-use installation of the Elementary CLI.

1. Install Elementary dbt™️ package

To start using Elementary, you need to add the Elementary dbt package to your dbt project.

2. Add elementary to packages.yml

Add the following to your packages.ymlfile (if missing, create it on the same path s yourdbt_project.yml):

3. Configure Elementary Profile in your dbt_project.yml

Paradime simplifies the setup process by providing a pre-configured Elementary profile that works with your default connections for both IDE and Scheduler.

3.1 - Add the following code to you dbt_project.yml:

This configuration ensures Elementary models have their own schema.

3.2 - Add this flag to your dbt_project.yml:

Starting from dbt 1.8, you must explicitly allow the Elementary packages to override default dbt™ materializations. Elementary needs this permission to collect samples and failed row counts for dbt tests.

These flags may trigger a deprecation warning. Elementary is collaborating with the dbt core team on a permanent solution. To mute the warning, to mute this warning, add the following flag:

Important: Materialization config

Elementary requires some models to be created as incremental tables. Ensure there are no global materialization configurations that might interfere with Elementary. For example, avoid settings like:

Instead, structure your dbt_project.yml like this:

4. Build Initial Elementary Models

Once you've configured your Elementary in your packages.yml and dbt_project.yml, Run the following dbt commands to install Elementary and materialize the initial elementary models in your data warehouse:

This will mostly create empty tables, that will be updated with artifacts, metrics and test results in your future dbt™ executions. (This is required for both environments, IDE and Scheduler.)

5. Generate Elementary Tests

When complete, execute execute dbt testto verify setup.

6. (Optional) Configure Slack / Teams notifications

7. (Optional) Run Elementary in Production

By default, the Elementary schema name is set as <target_schema>_elementary. You can override this by setting a system variable called PARADIME_EDR_SCHEMA. For instructions on configuring environment variables, click .

After installing and configuring Elementary, you're ready generate a variety on test, including: , , etc.

Additionally, you can leverage you can leverage DinoIA's one-click command, to automatically create comprehensive tests tailored to your dbt™ model's structure and content. This ensures a seamless setup of data observability across your project.

Paradime Alerts allow you to set up and manage notifications for your dbt™ projects, keeping you informed of important events and anomalies. With support for both Slack and Microsoft Teams, you can customize your alerts to fit your team's communication preferences. See for details.

While this documentation demonstrates running Elementary in a development environment, you can execute Elementary command in your Production Schedules ()

dbt_project.yml
models:
  elementary:
    +schema: "elementary"
    ## To disable elementary for dev, uncomment this:
    # enabled: "{{ target.name in ['prod','analytics'] }}"
dbt_project.yml
flags:
  require_explicit_package_overrides_for_builtin_materializations: False
  source_freshness_run_project_hooks: True
dbt_project.yml
flags:
  mute_ensure_materialization_override: TRUE
dbt_project.yml
materialized: "{{ 'table' if target.name == 'prod-cloud' else 'view' }}"
dbt_project.yml
models:
  my_project:
    materialized: "{{ 'table' if target.name == 'prod-cloud' else 'view' }}"
  elementary:
    +schema: "elementary"
Terminal
dbt deps
dbt run --select elementary
  1. 📖Documentation
  2. Integrations
  3. Observability

Elementary Data

PreviousObservabilityNextAnomaly Detection Tests
  • Getting started
  • 1. Install Elementary dbt™️ package
  • 2. Add elementary to packages.yml
  • 3. Configure Elementary Profile in your dbt_project.yml
  • 4. Build Initial Elementary Models
  • 5. Generate Elementary Tests
  • 6. (Optional) Configure Slack / Teams notifications
  • 7. (Optional) Run Elementary in Production
here
Anomaly Detection Tests
Schema Tests
docs
Bolt
dbt_project.yml
packages:
  - package: elementary-data/elementary
    version: 0.15.0
    # check latest package version on https://hub.getdbt.com/elementary-data/elementary/latest/
Elementary Data's Tutorial Guide
Generate Elementary Tests
dbt - Package hub
Logo
Example Slack Notifications