Data observability platform Acceldata nabs $35M

Data observability platform Acceldata today announced that it raised $35 million in a series B round led by Insight Partners, with participation from March Capital, Lightspeed, Sorenson Ventures, and Emergent Ventures. The proceeds bring the company’s total raised to $46 million to date, and they’ll be used to fund development and go-to-market efforts as Acceldata expands the size of its workforce, according to CEO Rohit Choudhary.

Over the next two years, thanks to the increasing adoption of cloud technologies, data collection in the enterprise is expected to climb at a 42.2% annual rate. But visibility into this data remains a challenge. While 76% of organizations in a 2020 survey said that monitoring their cloud infrastructure is either moderately or very important, less than 20% said they can monitor these environments properly.

Acceldata aims to address the challenge with products that offer observability across data quality, data pipelines, and data system infrastructure. Leveraging AI and machine learning algorithms, the platform exposes interactions between data, users, and applications, ensuring that the data — and the corresponding databases — are optimized in terms of performance, reliability, and cost.

While working for Hortonworks, InMobi, and Zalando, Acceldata cofounders Choudhary and Ashwin Rajeeva witnessed customers struggle to build complex, large-scale data systems. Despite the migration to the cloud and advances in technology, large organizations were beset by low-quality data and production bottlenecks, according to Choudhary.

“[Companies] were investing heavily in data initiatives but not reaping the benefits they expected. Although data science gets the majority of the attention and glory, Rajeeva and and I realized that data engineering and operations weaknesses are what most frequently undermines large companies’ ability to transform themselves into high-performing, data-driven enterprises,” Choudhary told VentureBeat via email. “Enterprises [were failing] to capitalize on their data investments because they lacked visibility into their data pipelines, and, more specifically, the data applications that were being built to power the advanced analytics and AI workloads that provide unique customer experiences and competitive differentiation.”

Acceldata

The three-year-old Acceldata competes with a number of startups in the over $1 billion data observability market. Others include Cribl, Monte Carlo, and Bigeye, which have raised hundreds of millions in venture capital to date.

Choudhary asserts that Acceldata is differentiated by its combination of event correlation, machine learning, and trending analysis, which it uses to predict and fix issues including data system performance, insufficient resources, and cost overruns. Acceldata Pulse monitors real-time compute performance and infrastructure usage, while Acceldata Torch and Acceldata Flow deliver data reliability and quality by helping to visualize data pipelines.

As Choudhary explains, Pulse can automatically correct some data problems with predefined actions and playbooks as well as simulators that model and recommend configurations based on a customer’s requirements. As for Torch, it can scan, profile, and tag data to facilitate data discovery. Once connected to data sources, Torch uses machine learning to create heat maps that visualize data characteristics, usage, and connections between assets, showing where enterprises have duplicate, outdated, or frequently used data.

“Torch can save customers time by automatically recommending data quality rules, eliminating time-consuming manual work [and] leading to broader data quality coverage and greater data accuracy. Importantly, Torch is able to track data quality through the entire data pipeline, from data ingestion to consumption, and covers data at rest, data in motion, and data under transformation,” Choudhary explained. “It reconciles data by measuring variance between source and destination assets and monitors for data and schema drift, which is critical for today’s AI and advanced analytics workloads that frequently rely on complex pipelines, multiple technologies, and distributed data sources.”

Acceldata’s newest product, Flow, is a software developer kit that enables teams to observe data pipeline health and performance. Currently available to design partner customers and expected to be generally available in 2022, Flow helps data engineers resolve pipeline issues and optimize resources by integrating with and monitoring the performance of existing workflow platforms. The tool taps into open-source workflow platforms like Apache Airflow and uses APIs, data sources, and transformation tools to build custom workflows.

“Flow helps enterprises optimize business process performance, reduce costs, and maximize return on data investment by providing full visibility into the underlying data pipelines,” Choudhary said. “Since it’s pre-integrated with Pulse and Torch, data teams can not only assess data pipeline integrity but uncover underlying data processing or data quality issues that are impacting performance from a unified user interface.”

San Jose, California-based Acceldata tripled revenue between 2019 and 2020 and has more than 20 large enterprise customers including Oracle, PubMatic, True Corporation, and PhonePe. With the new funding, the company plans to expand its 100-person workforce to 150 by the end of the year.