The partnership brings full-stack data observability, performance tuning, and proactive validation capabilities across the Databricks and Spark ecosystem.
Data leaders today face increasing pressure to deliver reliable, cost-effective, and high-performing data pipelines at scale. This is especially pronounced for teams building in the lakehouse and Apache Spark™ ecosystem, where complexity and mission-critical demands continue to grow alongside the push toward AI readiness.
As organizations scale their use of the Databricks Data Intelligence Platform to power data and AI workloads, they can encounter challenges with data and pipeline stability, resource utilization, and operational control.
That’s why we’re excited to announce our partnership with Databricks, helping data teams monitor, troubleshoot, optimize, and continuously validate their pipelines and workloads — to enable more reliable outcomes, faster delivery, and better cost optimization.
A Natural Fit for Data Engineering and Platform Teams
Teams working on the Databricks Data Intelligence Platform can easily build and run pipelines, with foundational governance capabilities. definity expands these with purpose-built full-stack data observability and workload optimization.
By embedding definity’s lightweight observability agents directly in Spark workloads, teams gain end-to-end visibility and control in real time, without disrupting execution or requiring code changes. With actionable insights into pipeline execution, data quality, and job performance at the compute and transformation levels, data teams become more proactive and minimize time spent detecting, fixing, and optimizing.
With definity, teams using Databricks can:
- Optimize performance and costs — profile job-level performance, detect over-provisioning and degradations, and auto-tune with one click.
- Monitor pipelines in real time — automated anomaly detection for data quality, job execution, and performance — inline with job execution.
- Root-cause issues quickly — with intelligent insights, transformation-level execution context, and deep job and data lineage.
- Validate in CI — test code changes on real data to proactively avoid runaway costs, failures, SLA misses, and data integrity issues.
For teams migrating to Databricks, definity helps accelerate and de-risk the migration by validating reliability, performance, and cost behavior — ensuring workloads run as expected before production rollout.
Why This Matters for Enterprise Data Teams
Without definity, data teams risk unexpected pipeline failures, uncontrolled spend, and delays in delivering AI-ready data products. With definity’s actionable insights, recommendations, and validation, teams can now easily optimize spend, proactively fix issues before they escalate, and confidently move faster.
"As more enterprises standardize the lakehouse for their data and AI workloads, ensuring reliable, high-performance pipelines has never been more important," said Roy Daniel, CEO of definity. "Our partnership with Databricks allows data teams to gain deep observability and optimization insights that ensure reliability, reduce costs, and accelerate innovation."
To learn how definity can help your team optimize and observe workloads on the Databricks Data Intelligence Platform, visit our definity x Databricks page or contact us to get started.