Sold by: Paradime Labs, Inc.
Deployed on AWS
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Paradime is Cursor for Data, an AI-native platform for agentic data engineering that replaces dbt Cloud™. Teams using Paradime move 10X faster.
Overview
Paradime is Cursor for Data, an AI-native platform for agentic data engineering that replaces dbt Cloud™. Teams use Paradime to:
- Code data pipelines up to 10X faster for analytics and AI using dbt™ and Python
- Reduce Mean time to repair (MTTR) by up to 70% when running state-aware data pipelines for analytics and AI
- Monitor and reduce warehouse cost by up to 20% using AI-agents that run 24/7
Highlights
- AI-native Code IDE that helps teams develop 10X faster with dbt™ and Python, with built-in warehouse, and column-level lineage context.
- Bolt scheduler supports running state-aware dbt™ and Python pipelines with 70% less MTTR. Run CI tests with column-level impact analysis.
- Run Fivetran ingestion before, and refresh Tableau, PowerBI and other applications after data transformation pipelines finish.
Details
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Pricing
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Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
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Dimension | Description | Cost/12 months |
|---|---|---|
Number of Users | The number of developer and admin users. | $4,000.00 |
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The customer is not charged during the trial period. All purchases are final and Paradime does not provide a refund policy.
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Delivery details
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Support
Vendor support
Support business hours are from Monday through Friday, from 9 AM to 6 PM (North America time zones (EST/CST/PST/MST), Central Europe (CET), United Kingdom (BST). Support business hours exclude local holidays in each timezone. Additional Advisory Services and 24/7 dedicated support can be purchased separately.
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AI-Native Code Development Environment
Integrated development environment with AI capabilities for coding data pipelines using dbt and Python, featuring built-in warehouse access and column-level lineage context.
State-Aware Pipeline Scheduling
Scheduler supporting state-aware execution of dbt and Python data pipelines with column-level impact analysis for CI testing.
Data Lineage and Impact Analysis
Column-level lineage tracking and impact analysis capabilities for understanding data dependencies and transformation effects across pipelines.
Warehouse Cost Optimization
AI-agent based monitoring and optimization system operating continuously to reduce warehouse operational costs.
Data Pipeline Orchestration Integration
Support for orchestrating multi-tool data workflows including Fivetran ingestion, data transformation pipelines, and downstream application refreshes for Tableau and PowerBI.
Environment Automation
Manages Snowflake infrastructure as configuration and code with deployment and lifecycle management capabilities for data applications and products.
Pipeline Orchestration
Builds end-to-end data pipelines and orchestrates data ingestion, modeling, and testing tools with integrated pipeline construction capabilities.
Continuous Integration/Continuous Deployment
Provides automated CI/CD workflows for building, testing, and deploying data products and applications on Snowflake.
Unified Observability
Collects, unifies, manages, and shares operational metadata to provide comprehensive visibility across data products and infrastructure.
Data Product Lifecycle Management
Delivers full lifecycle management for data products including development, testing, debugging, validation, and execution of Snowflake workloads.
dbt Integration
Seamless integration with dbt Cloud and Core for automated testing of data transformations and model changes
Automated Data Validation
Automated testing capability that validates data model changes without requiring manual test writing or custom queries
CI/CD Pipeline Integration
Integration into continuous integration workflows to enable automated validation during development, deployment, and migration phases
Change Impact Analysis
Detection and identification of breaking changes, metric shifts, and edge cases in data transformations before production deployment
Data Quality Reporting
Generation of impact reports and visibility into data changes for stakeholder communication and deployment decision-making
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