Monte Carlo plans to scale data observability platform

To further strengthen our commitment to provide industry-leading coverage of data technology, Venturebeat is excited to welcome Andrew Burst and Tony Bear as regular contributors. Look for their articles in the data pipeline.

Data, the lifeblood of all information technology is the blood that circulates and trades in devices, if full of impurities it can damage the system.

With data stores constantly filling up and analytics applications diving in to find business value, Monte Carlo, based in San Francisco, California, wants to ensure that all data is clean, securely stored and ready to be used in any data store at any time. Is. – Cloud or on-premises. This requires a serious dose of data observability that the startup is already providing for several hundred enterprise clients.

Monte Carlo’s machine learning-powered platform provides enterprise data analysts with a holistic view of data reliability for near-real-time complex business and data product use cases, chief engineer and co-founder Lior Gavisch told VentureBeat.

The 3-year-old company announced today that it has raised $ 135M in Series D funding from a group of IVP-led investors, giving it a valuation of 1.6 billion. Its frontline product is the SOC-2 Type II certified data observability platform that works with an intuitive user interface.

Data observability hot VC space

The IT data space has never been hotter in Venture Capital World. In the last year alone, BigQuery has reached 1.5 billion in valuations; Snowflake hits 1.2 billion; Databrix came in at $ 800 million. Monte Carlo is just the latest to follow this trend. The company claims to be the first data observability toolmaker to achieve the billion-dollar valuation by joining the ranks of Databricks, Fivetran, Starburst and dbt labs as Data Unicorn.

Gavisch told VentureBeat that the company intends to use the new capital to improve experiences for its hundreds of customers, scale the data observability category to new verticals, and advance its US and EMEA go-to-market and engineering teams.

“Data is in many places, isn’t it?” Gavisch said. “Some of them are legacy. Some of them are in modern data stacks, and some of them are up-and-coming, like streaming. (Data) Reliability problem cannot be solved as a point solution. , You will inevitably fail because reliability issues occur everywhere in every part of the stack that processes data. ”

Monte Carlo supports as many IT stacks as possible, Gavisch said. “I’m trying to make the whole stack as observable as possible. And so we work hand in hand with our customers to understand what data store stores are and what data processing mechanisms they are adopting.

“We make sure we support it in our solution; We also support all major data warehouses, all major data leak technologies, all major BI equipment, all major orchestration equipment. And we continue to add and develop it based on our customers’ demand, ”Gavisch said.

Increase the future of data reliability

As companies ingest more data and pipelines become more complex, teams need a way to ensure their decisions and the data that power digital products are reliable and efficient, Gavisch said.

Going too far into the production stream can result in data-quality problems that can be costly to fix when the use exceeds a certain point. Reflecting on the rise of application performance monitoring (APM) tools such as Datadog and NewRelic to keep software downtime at bay, Data Observations solves the problem of data downtime by providing end-to-end coverage and visibility in data health to their modern data. Stack

Money in cloud databases

In 2021, organizations spent $ 39.2 billion on cloud databases such as Snowflake, Databrix and Google BigQuery, yet Gartner estimates that the average organization spends ડાઉન 12.9M per year on data downtime and poor data quality. While the Monte Carlo research shows a correlation between data events and the amount of data the organization handles, the average business experiences at least one data event for every 15 tables in their environment, Gavisch said.

“Companies continue to invest in technologies that drive smart decision-making and power digital services, so the need for high-quality data is never greater,” Kev Wilhelm, general partner at IVP, said in a media advisory.

Since the announcement of its Series C in August 2021, Monte Carlo has more than doubled its revenue every quarter and achieved 100% customer retention in 2021. Its list of 100 customer companies includes JetBlue, Gusto, Affirm, CNN, MasterClass, Auth0 and SoFi; Partners include Snowflake, Databrix and DBT Labs.

“Just having data is not enough – it needs to be searchable, accessible and reliable,” said Bar Moses, CEO and co-founder of Monte Carlo, in a media advisory. “Monte Carlo has created the world’s leading data observation platform for data downtime to speed up the process of adopting reliable data by reducing detection and resolution time.”

The company is supported by Excel, GGV Capital, Redpoint Ventures, Iconic Growth, Salesforce Ventures, GIC Singapore and IVP. Competitors in the data observability market include ICT Reverse, Toussaint Technology, Mathematica and Zertifica General.

Similar Posts

Leave a Reply

Your email address will not be published.