Faros AI - A Modern Data Stack for Engineering Operations
The Faros AI infrastructure leverages a proven modern data stack: Airbyte, Hasura, Metabase, n8n, and dbt — specially customized to handle the nuances of Engineering Operations data. And unlike blackbox solutions, it is designed to grow with the growing needs of your engineering organization. Read On ...
December 15, 2022
Engineering organizations everywhere are looking to leverage the vast amount of operational data that they produce every day in order to be more efficient, data-driven and more predictable, just like their Sales and Marketing counterparts.
Most engineering organizations today manage and maintain operational data in spreadsheets – a tedious and error-prone process. Some look to static metrics vendors or point solutions that provide insight into specific slices of the operational data. But this can be quite limiting. For example, DORA/Jira/Git metrics vendors often require teams to change how they work in order for the metrics to be meaningful — with no possibility of historical measurements, or of digging further down or laterally into adjacent data sources. Others, yet, have invested in centralized platform teams to ETL operational data to a central location — an undifferentiated and expensive proposition.
It seems that when it comes to leveraging operational data, thus far, one could only hope to get any two of three properties simultaneously: functionality, correctness, or cost-effectiveness. But not all three at the same time. This is why we built Faros AI.
A Complete Data Infrastructure for Engineering Operations Data
Faros AI is designed from the ground up to be a complete data infrastructure for engineering operations data, so organizations can leverage it for Analytics, Automation, and Information Catalogs.
Faros AI is connected, extensible, and trusted as it is built on top of a modern data stack.
Compared to point solutions, Faros has 70+ sources that map to an expansive engineering operations schema — from Teams to Tasks and Pull Requests to Deployments to Incidents and more. Custom homegrown systems can also be supported with custom Airbyte connectors and a real-time Events API.
Faros AI connects the dots both within and across systems - e.g. it resolves the various identities of a teammate across Jira/GitHub/PagerDuty, or connects Pull Requests to Jira tickets - regardless of order-of-ingestion. It automatically enriches the data when possible. For instance, it automatically infers changesets between consecutive application deployments, building a complete, connected picture of the entire software development lifecycle.
We play well with over 70+ integrations
The Faros AI infrastructure provides a lot more than static metrics! It is customizable and extensible at every level:
- Data: Users can connect up home-grown data sources via custom Airbyte connectors or the real-time Events API.
- Business Logic/ Transforms: Custom flows, including data transformation using the Transformation API or dbt, can be defined for any process, e.g., a deploy process, incident resolution process etc., that tracks entities across systems to uncover bottlenecks.
Incident Management Flow
- Visualization: With our embedding of Metabase, users have access to a full-blown Analytics solution on top of their data, pre-configured with a rich library of state-of-the-art engineering metrics dashboards - like DORA metrics, that can be fully customized to a team’s unique needs.
Velocity Metrics Dashboard
- Catalogs: Customizable pages can be configured for every resource type (e.g. teams and services) with a rich set of drag-drop widgets to provide valuable catalogs for the organization!
Catalogs with Customizable Pages & Drag-drop Widgets
- Integrations: A graphQL API powered by Hasura provides flexibility and ease of querying data both within and across data sources
- Automations: With our n8n integration, teams can create automation workflows (e.g. remind teammates on slack about Pull Requests waiting for reviews)
Automation Workflow - Integration with Slack
Unlike black box metrics solutions, Faros AI is:
- Transparent: Metrics and chart definitions can be inspected and updated, and the underlying data explored. This transparency combined with the extensibility mentioned above means that you can start instrumenting and automating with no change in process or behaviors in an incremental way while having access to historical measurements.
- Contextual: Since Faros maps your data to your hierarchical organization, All metrics can be broken down and filtered on your organization chart. This allows you to quickly understand how systemic an issue is throughout your organization, and make much more targeted, relevant, and meaningful decisions.
Contextual Insights - By Organization, Team, and Individual
- Secure: Faros comes with a comprehensive Role Based Access Control mechanism which scopes the data one can leverage for proper privacy, and has several deployment modes: multi-tenant or single-tenant SaaS, on-premise or hybrid where you run the connectors from your VPC.
A Modern Data Stack
As you can see, the Faros AI infrastructure leverages a proven modern data stack: Airbyte, Hasura, Metabase, n8n, and dbt — specially customized to handle the nuances of Engineering Operations data. And unlike blackbox solutions, it is designed to grow with the growing needs of your engineering organization.
Faros Architecture - A Modern Data Stack
See Faros AI in Action
The power of Faros AI comes from its flexibility; it works for all types of data, all types of questions, all types of roles. Whether you are a senior engineering leader trying to better understand your entire engineering org, or a team member looking to play around with specific data to answer your own questions, Faros AI can help you move beyond guess-work and start making data-driven decisions for better outcomes.
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