Snapshot
Continue to About ↓- What it does
- definity is an agentic data engineering platform that installs inside existing Spark workloads on Databricks, Amazon EMR, Google Dataproc, or self-managed Spark with no code changes.
More detail
- It auto-tunes jobs to cut cloud compute spend, monitors pipeline health and data quality in motion, and validates code or platform changes in CI before they reach production.
- The company states it runs in your environment so data never leaves.
- Best for
- Enterprise data engineering and platform teams running large Spark workloads who need to reduce cloud compute cost, protect SLAs, and speed up incident root-cause analysis.
More detail
- It also suits teams planning Spark version upgrades or Spark-to-Databricks/EMR/Dataproc migrations that need pre-production validation on real data.
- Pricing
- Explorer is free ($0) for up to 10 Spark jobs and 3 users on SaaS.
More detail
- Pro starts at $0.003 per vCore-hour, so cost scales with the size of the monitored Spark footprint, with up to 10 users, SaaS deployment, and email support
- it is also available on AWS Marketplace and no credit card is required to st
- Main limitation
- Teams without Spark or lakehouse workloads, or small shops that only need basic monitoring, since the free tier caps at 10 Spark jobs and 3 users and enterprise features require custom pricing.
More detail
- Buyers who need external Slack/email alerting or hard pipeline stops today should verify current capabilities first, as the docs describe these as not yet available.
- Record
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- Structured profile ↗
About
- definity is an agentic data engineering platform that installs inside existing Spark workloads on Databricks, Amazon EMR, Google Dataproc, or self-managed Spark with no code changes.
- It auto-tunes jobs to cut cloud compute spend, monitors pipeline health and data quality in motion, and validates code or platform changes in CI before they reach production.
- The company states it runs in your environment so data never leaves.
What it helps with
- Over-provisioned Spark compute, data skew, and missing parallelization driving up cloud costs.
- Lack of real-time job-level monitoring, since native tools like Spark UI, CloudWatch, and Dataproc History Server are ag
- Manual, at-rest data-quality checks that detect bad data only after it reaches downstream systems.
- Slow, fragmented root-cause analysis across logs, lineage, and environment changes.
- Risky Spark version upgrades and platform migrations with no pre-production validation framework.
Capabilities
A practical view of what is available and where limitations apply.
| Offering | Available | Detail |
|---|---|---|
| Auto-Tune | Yes | Adjusts executors, cores, memory, and shuffle partitions one step at a time, keeps beneficial changes, and re-tunes on drift. |
| In-motion observability | Yes | Real-time execution monitoring, pre-run input validation, runtime anomaly detection, and automatic run preemptions. |
| Automated tests from history | Limited | Auto-generated per-metric tests require a baseline of about 4–5 successful pipeline runs, so new pipelines get no automated testing immediately. |
| Automated root-cause analysis | Yes | Correlates code, configuration, environment, schema, and data changes to help engineers root-cause incidents in minutes. |
| Change validation in CI | Yes | Side-by-side re-runs on full-scale data in CI/staging before changes reach production. |
| Data and job lineage | Yes | Column-level lineage across transformations and datasets. |
| Zero-code installation | Yes | Central one-time install via Spark config, Databricks init script, or Spark Submit; the company claims setup in under 15 minutes. |
| External alerting (email/Slack) | Limited | Docs state test-triggered alerts are currently internal in definity, with email/Slack integration to be added. |
| Pipeline stop on test failure ('Break' action) | No | Docs state this action is currently not supported. |
| Independent benchmark verification | Unknown | Performance claims such as 40%+ infra cost reduction are first-party and were not independently verified in the sources reviewed; no independent reviews were located. |
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Evidence
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- Verification
- Scribble researched these facts from the linked public sources.
- The owner has not manually confirmed every field.
- Last reviewed
- Primary source
- definity.ai
Technical profile details
Record ID
0a8cd064-c5bb-4936-a1b9-2192808ee1a6Canonical URLhttps://profiles.scribble.network/listing/definity-0a8cd064