Keeping your Domo instance organized as adoption grows can be difficult. RXA @ OneMagnify is your new clean-up crew!
We know how easily a Domo instance can get cluttered, especially when you achieve widespread adoption at your company. Engage RXA’s Domo Governance team to help you identify excess and optimize your instance. Optimization and governance are important because Domo pricing depends on topics like user and row counts. Optimization will also save you more than money – when users can easily find the data they need, they have more time to create insights, bringing valuable efficiency to your team.
Row count is one of the key factors that Domo considers when pricing the platform. If you are struggling to keep your Data Warehouse below your row count limits, our team can review your current structure and implement long-term solutions to reduce row count.
Domo is offering a new pricing model to some customers based on several consumption variables like dataflow executions, connections, and use of Data Science tiles. We can optimize your usage of these credits to get the best pricing available.
User licenses are another key factor in the price of your Domo instance. We can help you see who is engaging with Domo consistently and who is not, to help your company get the most out of purchased licenses.
We are experienced in building new ETL and MySQL dataflows, as well as reviewing data processes to identify opportunities to optimize current data architecture in any instance. We can also help your team with dataflow version control.
Beast Modes are a powerful tool that Domo offers to transform data outside of a dataflow – but they can be hard to QA when implemented by various team members. Our Domo Governance tools can help your team validate the Domo Beast Modes you have and manage the addition of new ones.
The RXA team has spent years building our Domo Governance dashboards utilizing DomoStats and Domo Governance datasets. These dashboards give you the power to monitor your instance health, including:
There are best practices that every company should adopt to keep their instance clean on an ongoing basis. The RXA team can train you on topics like naming conventions, quarterly reviews, DomoStats, and Domo Governance assets to keep your team on top of your instance health.
Domo is talking to some customers about a new pricing model based on usage of certain features within the platform. The execution credit model is an efficient way for your team to gain access to the full Domo ecosystem, while paying for what you actually use, when you use it. The below structure is the most up-to-date information we have at this time, and is subject to change by the Domo team.
If you have any questions, don’t hesitate to contact us at learn@rxa.io
| Execution Type | Execution Definition | Number of Execution Credits |
|---|---|---|
| Data Ingest | Completed activity that appends, replaces, deletes, or upserts data into the Domo Platform via a connector, file upload, API, CLI, SDK, workbench, webhooks, or through a custom ingestion solution | 1 per table created/updated |
| Data Exported/Written | Completed activity that writes data to an external system (writeback), data export, webhooks, card export or scheduled report | 1 per table exported/written |
| Dataflows (Magic v1, Magic v2, Redshift, MySQL) | Completed execution for MagicETL v1, MagicETL v2, Redshift, or MySQL Dataflows | 1 per output table created/updated |
| Dataflows (Adrenaline) | Completed execution for Adrenaline Dataflows | 3 per output table created/updated |
| Data Science | Completed MagicETL v1 and v2 Dataflow execution that contains Data Science tiles, or R or Python tiles in use | 3 per output table created/updated |
| AutoML (Training) | Completed AutoML model training run, or a re-training run | 2 per run |
| AutoML (Prediction) | Completed Dataflow execution with AutoML inference tile in use | 2 per output table created/updated |
