Databand Team (from website)
Databand, an Israeli startup, offers observability platform for data engineering teams. The company brought in $14.5 million in a Series A round of investment led by Accel Partners.
Founded in 2018 by Josh Benamram, Victor Shafran and Evgeny Shulman, Databand’s solution is a form of DevOps. DevOps is defined as a combination of software development (Dev) and IT operations (Ops). The idea here is to make things easier for IT teams so that they can work faster and be more productive.
Data engineering teams are responsible for managing a wide suite of powerful tools, but lack the utilities they need to make sure their ops are running properly. Databand says that it fills this gap with a solution that the company states enables teams to gain a global view of their data flows, make sure pipelines complete successfully, and keep tabs on resource consumption and costs. Databand boasts that it, “fits natively in the modern data stack, plugging seamlessly into tools like Apache Airflow, Spark, Kubernetes and various ML offerings from the major cloud providers.”
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Data engineers are the backbone of modern data teams, explains Databand. The average data engineer, however, has many challenges ensuring that systems are run successfully, data meets quality standards, and business stakeholders are satisfied. The company states that it built Databand to bridge the gap between DevOps and DataOps, “so that teams are more ready to scale production data infrastructure.”
The company says that it raised new capital to accelerate its product development and bring its solution to the wider market. “The demand out there is only growing, our product roadmap is extensive, and our backlog is heavy with exciting features that our clients are clamoring for,” Databand said in a blog post. “We need product managers, UX leaders, and engineers to deliver those experiences.”
Databand CEO and co-founder Josh Benamram Told TechCrunch, “Our users were in a constant battle with ETL (extract transform load) logic. Users didn’t know how to organize their tools and systems to produce reliable data products.”
