Announcing the General Availability of definity's Data Application Observability & Remediation solution, together with $4.5M in Seed funding
Today, we’re thrilled to announce the General Availability of definity – the next-generation Data Application Observability & Remediation solution, purpose-built for Spark-first data platforms.
Alongside this milestone, we’re excited to share that we’ve secured $4.5M in Seed funding led by StageOne Ventures, with participation from Hyde Park Venture Partners and an incredible group of current and former founders.
Why We’ve Built definity
definity came to life out of our own personal experiences leading data engineering and product in tech-forward enterprises like Paypal and FIS/Worldpay. We’ve lived through the day-to-day challenges of data producers, building and maintaining complex data applications (pipelines), and through the impact of bad data on data consumers and their customers. But our teams did not have the solution they needed.
Data engineering today is broken. Over the past decade, data applications have evolved into one of the most mission-critical software in almost every organization's technology stack. Yet while application engineers are armed with a robust APM toolset to monitor their applications and infrastructure, pinpoint issues, and quickly resolve downtimes, data engineers do not. They’re forced into a reactive stance, spending more time firefighting than driving new business value.
The problem is amplified tenfold in the Spark ecosystem, which typically involves heavy and mission-critical workloads and a more obscure infrastructure, but lacks modern observability tooling.
Altogether the impact on the business and on the data engineering and platform team is significant – data incidents and downtime that lead to direct business loss and customer impact; data engineering capacity waste and low velocity; and skyrocketing infrastructure costs.
We knew that for observability to truly benefit data engineering teams, it must be:
- Seamless: To drive developer adoption and scalability
- Ubiquitous: Covering every pipelines and dataset)
- In-motion: Detecting issues in real-time and enabling immediate response
- Application focused: Integrating data,application execution, and infrastructure performance (rather than just analyzing outputs)
- Tailored for Spark: Optimized for Spark environments, whether on-prem, hybrid or in the cloud
- Provide value from day-1: generating health and optimization insights and highlighting cost saving opportunities instantly
definity – The Next-Gen Data Application Observability & Remediation Platform
That’s why we embarked this exciting journey to reinvent how data engineering teams develop, maintain, and optimize data applications (pipelines).
Spark data engineering teams deserve a better way to ensure the reliability of their data applications, optimize performance and curb infra cost, and prevent data incidents.
definity is delivering the next-generation data observability solution. It is the industry’s first data application native solution, providing in-motion and contextualized insights into data pipeline execution, data quality, and data infrastructure performance. Using a unique agent-based architecture, definity establishes ubiquitous observability with zero code-changes—in on-prem, hybrid, or cloud environments.
With definity, data engineers can seamlessly observe, fix, and optimize their Spark pipelines, in-motion. They can detect & resolve issues faster than ever before and start cutting costs from day one!
Full Speed Ahead!
What drives us every day is the value we deliver to data engineering teams! We’re highly appreciative of the early enterprise teams who’ve been sharing our vision and partnering with us to transform data application observability.
We're also incredibly proud of the world-class team that joined us early on and is instrumental to this journey of challenging the status quo.
This is just the very beginning. We continue to push the boundaries of what’s possible in data engineering.
Spark Observability? definity!
Join the innovation-forward enterprise teams who are shaping the future of data engineering with us!
Tom, Ohad, and Roy
Read more on TechCrunch or the press release.