PALO ALTO — Ascend, a provider of the world’s first Autonomous Dataflow Service, has emerged from stealth with $19 million in funding to de-risk big data projects and accelerate digital transformations. Ascend operates the only solution with which data engineering teams can quickly build, scale, and operate continuously optimized, Apache Spark-based pipelines.
Venture capital firm Accel led the Series A round with participation from Sequoia Capital, Lightspeed Venture Partners, and 8VC. Technology executives including Kevin Scott, CTO of Microsoft; Scott McNealy, former Sun Microsystems CEO; Maynard Webb, Board Member, Salesforce and Visa; and Deep Nishar, Senior Managing Partner of Softbank Vision Fund, also bring their talents as advisors to the company’s leadership team.
“The market is on the cusp of a new wave,” said Scott McNealy, Ascend advisor and former Sun Microsystems CEO. “The winners of the past decade are those who best leveraged data to fuel their business, yet increased competition necessitates they do more, faster, and with greater efficiency. We’ve seen automation transform every industry and this will be no exception. I have worked with dozens of startups, and it’s rare a company possesses both the experience to define the root of a monumental problem and the talent to do something amazing about it. Ascend is such a company, and their innovation will unequivocally usher in a new era of data engineering.”
“I’ve worked with hundreds of companies over the years and have seen firsthand the challenges encountered with big data and digital transformation,” said Sean Knapp, Founder and CEO of Ascend. “I founded Ascend to fix data pipelines, a critical and ubiquitous component of data architectures that have been neglected until now. By streamlining data pipeline development and automatically optimizing its ongoing performance, we have changed the game for data engineers and the data consumers that depend on them.”
Data pipelines are the lifeblood of every big data project and transformation strategy. Building these pipelines, however, is a time-consuming process for data engineers, requiring fragmented infrastructure and specialized tooling, extensive manual coding, and painful trial and error. Even then, these pipelines become more brittle and prone to failure as data changes, dependencies grow, and the interconnectedness of data movement among systems becomes increasingly complex. As a result, scarce data engineers spend the majority of their time combing through code and logs just to keep everything running, rather than building for new business opportunities.
The Ascend Autonomous Dataflow Service eliminates these challenges. Its automation and continuous operation of pipelines radically improves data engineering, enabling pipeline creation with 85% less code and reducing the time spent from prototype to production by 90%. Data engineers can now build using declarative configurations and compact code, while the Ascend Dataflow Control Plane automates the management of cloud infrastructure – leveraging its powerful Spark engine to handle massive scale – while perpetually operating and optimizing users’ pipelines in response to inevitable data changes.