Roy Daniel definity CEO (Linkedin)
Israeli startup definity has come out of stealth and released a next-generation Data Application Observability & Remediation solution, purpose-built for Spark-first data platforms. The firm also secure $4.5 million in Seed funding led by StageOne Ventures.
A Spark-first data platform is a data architecture that prioritizes Apache Spark as the core engine for data processing, analytics, and machine learning workloads. This approach leverages Spark’s versatility, performance, and in-memory computing capabilities to handle diverse data processing tasks efficiently.
Spark serves as the foundation for batch, micro-batch, and stream processing, providing a consolidated approach to data management. Its in-memory processing capabilities accelerate data transformations and analytics, leading to faster insights and the extensive Spark ecosystem offers libraries and tools for machine learning (MLlib), SQL (Spark SQL), graph processing (GraphX), and more.
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Spark can handle massive datasets and complex workloads, making it suitable for large-scale data processing. It also integrates seamlessly with various data sources and systems, enabling data ingestion and export.
definity boats that the firm 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,” says the firm.
Data engineering is in crisis, says definity. Data has become the lifeblood of modern businesses, yet data engineers lack the tools to effectively manage and optimize their critical systems. While application development has been revolutionized by robust monitoring and troubleshooting platforms, data engineers are still relying on outdated methods. This reactive approach consumes valuable time and resources, hindering innovation and business growth.
The Spark ecosystem exacerbates these challenges manifold. Handling intensive, mission-critical workloads within a complex infrastructure, it sorely lacks the modern observability tools essential for effective management. The consequences are far-reaching. Data incidents and outages directly impact the bottom line and customer satisfaction. Data engineering teams are bogged down in firefighting rather than innovation, leading to wasted resources and delayed projects. Meanwhile, soaring infrastructure costs further strain budgets.
“Enterprise data engineers demand a new standard of observability that doesn’t exist today,” said Roy Daniel, co-founder and CEO at definity. “Traditional data monitoring focuses on the symptoms, assessing data quality at-rest in the data warehouse, which is too out-of-context, reactive, and simply not applicable for Spark. definity fills this void by taking a completely new approach focused on the data application itself, observing in-motion how data is processed and how the infrastructure operates, making Spark applications human-readable.”
