Full-Stack Data Observability
for Spark
Monitor, troubleshoot, and optimize any Spark workload – out-of-the-box and in real-time.
On-prem, cloud, or kubernetes.
Why Do I Need Full-Stack Observability
for Spark?
Today
Spark UI is Limited & Cumbersome
Lacking native monitoring and anomaly detection for data quality, jobs health, and SLAs across runs – forcing teams to build custom scripts.
No Execution Context or Lineage
Hard to pinpoint issues & failures, manually correlating Spark logs, stages, tasks, and env changes – requiring effort & expertise.
Limited Performance Monitoring
No continuous profiling of CPU, Memory, and performance (e.g., skew, spill) – hiding waste, slowdowns, and degradations.
No Validation Framework
Spark version upgrades are risky, leading to performance regressions, job failures, and data anomalies.
definity
360° Real-Time Observability
Monitor data quality and execution health – out-of-the-box, inline with job execution.
Intelligent RCA & Lineage
Root-cause incidents in minutes with intelligent insights & unified transformation-level execution context, including deep data+job lineage.
Job-Level Performance Optimizations
Automatically profile performance, detect degradations in real-time, and auto-tune jobs in 1-click.
Seamless Validation in CI
Validate version upgrades & code changes before deployment, ensuring data reliability & performance stability.
Contextualized Transformation-Level Observability
Deploy in Minutes – No Code Changes
Instrument across all workloads centrally, via simple config in Spark Submit
Migrating Spark platforms?
Automate workloads validation
Upgrading Spark versions, moving from on-prem to cloud, or moving to Spark on Kubernetes? Accelerate migration by months, with seamless workload validation on real data:
Ensure data reliability & pipeline stability, and easily pinpoint unexpected changes – in CI
Maintain job performance & prevent regressions
Avoid cost overruns & runtime surprises
