Databricks

amberflo for databricks usage and cost tracking amberflo makes it easy to track, allocate, and optimize your databricks usage and costs using a unified, finops aligned approach as a focus compatible platform, amberflo can ingest and normalize databricks usage data without requiring any custom etl work—so your teams can focus on insights and optimization, not integration what this enables with amberflo, databricks usage becomes fully integrated into your enterprise wide finops strategy you gain real time visibility into dbu and machine hour usage accurate allocation using tags and dimensions standardized reporting using the focus format automated ingestion with no custom pipelines optimization insights based on usage patterns this enables you to manage databricks alongside your other cloud and isv workloads with consistent tooling, logic, and reporting how it works usage export databricks delivers billable usage logs in csv format to a cloud storage bucket these logs include key metrics such as dbus machine hours metadata (workspace, cluster id, tags, etc ) native ingestion amberflo connects directly to the storage location and automatically ingests the csv files there is no need for manual parsing or custom pipelines each record is transformed into one or more standardized meter events (e g , dbus, machinehours) metadata is preserved as dimensions (e g , workspace, clusterid, nodetype, custom tags) tags such as dept or project can be mapped to internal identifiers for chargeback and reporting normalization and enrichment ingested records are automatically normalized to align with the focus standard this ensures that data is consistent with other isv and cloud usage historical usage is enriched with business context events are available for downstream reporting, allocation, and alerting key capabilities end to end usage visibility view usage by workspace, cluster, node type, or business unit analyze dbus and machine hours at daily, hourly, or per event granularity filter and group by dimensions such as environment, team, or region accurate allocation and chargebacks use tags from databricks to allocate usage and cost to internal teams combine with amberflo’s allocation rules and business units for structured showbacks or chargebacks apply custom rates or markups as needed standardized finops reporting fully compatible with the focus data model from the finops foundation enables unified views across cloud and third party providers supports internal benchmarking, trending, and forecasting automation and simplicity no custom etl pipelines required compatible with your existing databricks export process amberflo handles parsing, normalization, and enrichment automatically business value integrating databricks usage into amberflo delivers immediate value to it, finance, and engineering teams transparency understand exactly where and how databricks resources are being consumed accountability attribute cost to the right teams, projects, or customers with precision efficiency eliminate the need for custom scripts or manual data processing comparability analyze databricks usage in the same way as aws, azure, gcp, or other isvs