Run
Run self-healing pipelines with Bolt
Run self-healing ingestion, dbt™, Python, agents, and dashboard refreshes in one pipeline. Get alerts when things break with smart retries.
Fivetran1m 08s
Airbyte52s
dlt41s
Run DinoAI Background Agent
Run dbt™ Command
Run Python Script
Run pre-commit
Run Elementary
Run MonteCarlo CLI
Refresh PowerBI Dataset
Refresh Tableau WorkbookRefresh Tableau Data Source
Run Lightdash
Run Hex Project
Sync Fivetran Connector
Sync Airbyte Connection
Sync Hightouch
Run BigQuery Scheduled Query
Control GCP Datastream
Trigger Airflow DAG
Run Matillion Pipeline
Run Azure Data Factory Pipeline
Run AWS Step Functions
Trigger GitHub Actions
Trigger Zapier
Run AWS Glue
Invoke AWS Lambda
Run AWS ECS Task
Run AWS SageMaker Pipeline
Run GCP Dataflow
Invoke GCP Cloud Function
Run GCP Cloud Run Job
Run GCP Dataproc Job
Push to DataHubToggle Paradime Bolt ScheduleRefresh Paradime Catalog
dbt build4m 12s
Python script37s
DinoAI agent1m 20s
Tableau44s
PowerBI31s
Hightouch18s
Slack#data-alerts
MS TeamsData platform
PagerDutyIncidentsWhen a run fails, a DinoAI agent diagnoses it in the failure thread, implements a fix, and opens a pull request. It never merges on its own.
DinoAI
GitHubFeatures
What's inside Bolt?
A modern data orchestrator for the post-Airflow generation
Paradime State
Paradime State brings state-aware orchestration to dbt™ v2.0 pipelines. Only changed data models, and tests consume time and compute resulting in faster pipelines and 25%+ less cost.
Self-healing schedules
Add a self-healing agent in a schedule, which will be triggered when the pipeline fails. DinoAI finds the root cause, implements a fix, and opens a pull request. You can fully configure how it behaves.
SnowflakeMultiple environments
Create run-time environments with custom data connections in seconds and give each schedule a custom execution environment from staging to production.
Run the full stack in one schedule
Chain commands like sync Fivetran, run dbt™, and check quality with Elementary. Then refresh Power BI or Tableau, trigger Hightouch, and finally trigger a DinoAI agent.
Get alerts where you work
Get alerts on Slack, Microsoft Teams, or email. Choose per destination whether success, failure, or an SLA breach will trigger the alert. The SLA alert fires when a run has exceeded its threshold.
Retry failed pipelines
Re-run failed runs from their point of failure. Retry from failure re-runs only the failed and skipped commands.
Workflows
What teams run on Bolt
From the first schedule to CI/CD on every pull request.
Schedule
Deploy in the UI or as code
Build schedules in the UI, then deploy it directly, or as YAML. You can also build and deploy schedules as YAML from your IDE or Claude Code.
CI
Test changes on every PR
Build and test changed models in a temporary schema with TurboCI and get the status on the pull request. Works with GitHub, GitLab, Azure DevOps, and Bitbucket.
Review
See the blast radius in the PR
The column-level lineage diff adds the blast radius of change in the PR. It lists every downstream model, Looker, Tableau, or ThoughtSpot asset affected by a renamed, or removed column.
Deploy
Deploy to prod on merge
Trigger schedules when a pull request is merged. It defers to the last run, so only changed models are built. Use it in GitHub, Azure, GitLab, or Bitbucket.
Inspect
Detailed logs with AI summaries
Access logs, and files generated in each run of a schedule. Logs come with an AI summary and "Fix with DinoAI" option to use agents to manually fix broken pipelines.
Trace
Audit variables for compliance
Inject audit variables such as runID, schedule name, trigger type, and git SHA into every dbt™ command and ensure every new data row has the commit that generated it.
Join industry leaders winning with DinoAI agents
Data engineers and analysts at the most AI-native startups and enterprises use Paradime's DinoAI agents everyday to become market leaders.