The new standard in data transformations
Scale your data team and platform built on the hardest lessons learned managing petabyte-scale pipelines.
with your data pipelines
If you’re working with hundreds of models, you're probably spending too much time on redundant transformations and preventable errors.
This can make anyone hesitant to make updates to the data warehouse, because they’re so costly and prone to errors.
There's got to be a better way...
The proper way to perform data transformations
Updates
- Instant development environments at near-zero cost
- State-aware processing that tracks previous transformations
- Impact analysis showing exactly what needs to be rebuilt
Performance
- faster model build times through smart, incremental processing
- dramatic productivity boost for teams through state-aware transformations
- significant reduction in monthly data warehouse costs
Collaboration
- Multi-player development environments
- Performance history tracking
in a visual interface - User-friendly dashboard interface
Making operational efficiency and data correctness non-negotiables
Swipe left/right to see the entire diagram.
Scales SQLMesh into an Enterprise Experience
Scheduler
- Native, automated per-model job scheduling
- No CI/CD configuration
- Unlimited concurrent running jobs
- Pause individual production or model runs
- Fully Managed State/Data Catalog
- Isolated Python environments
Alerts
- Runtime alerts and root-cause identification for failures
- Configurable alerts for custom measurements
- Context-aware troubleshooting guidance
- Notifications through pager duty, email, and Slack. Webhook and Datadog integrations coming soon
Debugger
- Comprehensive, centralized view on errors, including pull requests, query consoles, logs, and DMs
- Traceability for each plan or run, including up/downstream dependencies, code definition at time of failure, recent model modifications, and other contextual execution metadata
Warehouse Cost Tracking
- Understand which models are contributing most to warehouse cost
- View warehouse cost changes over time
Advanced Change Categorization
- Automatic detection of downstream processes requiring updates after column modifications or removals
- Advanced column-level lineage impact analysis to minimize backfills
Cross-Database Diffing
- Detect discrepancies between datasets across multiple databases to validate migrations
- Leverage hashing algorithm for data comparison without costly full joins
Transformation and Modeling Framework
Deployment
Debug transformation errors before you run them in your warehouse
Tobiko parses your SQL code in advance, so you can spot syntax and compile time issues in seconds... then fix them without waiting on the warehouse.
Create isolated development environments with near-zero data warehouse costs
We track state and run history to create virtual data environments using views, so you'll see:
- An exact copy of production data for development
- True blue-green deployments promoting data back into production
- Near-zero warehouse processing costs
See what impacts
your pipeline with
column-level lineage
Tobiko Cloud goes way beyond a visual of column data flow — it instantly shows how your changes impact downstream columns and tables (both breaking and non-breaking).
Save time and costs, using state-aware architecture
Unlike dbt™, which wastes resources processing everything, we track what data’s been modified and run only the necessary transformations — saving you time and money.
Tobiko Cloud works with the tools you’re already using
Tobiko cloud perfectly centralized our pipeline monitoring, so we can now confidently troubleshoot user plans in real-time during development cycles and track runs' behavior. We ditched our unscalable BigQuery state store and reclaimed engineering time. What began as a scalability fix is now core to our data reliability.
Before Tobiko, we were operating solely on read replicas of prod Postgres databases, and we had no concept of building transformations for analytical purposes. Now we're able to build downstream analytics, assemble clean training sets for ML experiments, and iterate quickly in a collaborative fashion for anything data-related.
See how we raise the bar for developer experience and productivity!
No need to rewrite your whole project.
Tobiko Cloud is backwards-compatible with dbt to make the switch easy.
Run parts of your dbt and SQLMesh projects in harmony.
Questions
Got a pressing question that’s not in this list?
We are integrated with: Databricks, Snowflake, BigQuery, Redshift, MotherDuck, DuckDB, Athena, MySQL, MSSQL, Postgres, and GCP Postgres.
If you use a warehouse, engine, or other solution that's not listed here, talk to us or send us an email at hello@tobikodata.com
Tobiko Cloud has built-in scheduling and orchestration capabilities, but we also support Airflow (with Dagster to follow in late Q1 '25). Stay tuned for other integration announcements and roadmap updates by joining our Slack community.
We provide three flexible options: cloud-only, metadata in the cloud with self-hosted runners, or fully on-premise. Contact us to find the setup that works best for your organization.
This will depend on your preferred setup:
- Cloud-only: Full data access is required
- Cloud with self-hosted runners: Only your SQL metadata access is needed; all warehouse operations stay in your environment.
- Fully on-premise: No data access is required
No, Tobiko Cloud is a standalone system. However, if you're already using dbt™, there's no need to redo any existing work. Tobiko Cloud is backwards-compatible with dbt™, so you can easily make the switch.
Tobiko Cloud’s pricing consists of two components: a platform fee for access to all features and a pay-as-you-go fee for consumption.
Our pricing model does not limit the number of seats or projects. Contact our sales team to learn more and explore a pricing plan tailored to your needs.
