VCs Open Up the Checkbook for Observability Startups

Observability continues to be one of the biggest tech trends in big data. It certainly has the attention of venture capitalists, who have committed hundreds of millions of dollars to data observability startups in recent weeks.

While data analysts and data scientists get the glory when their analytics and machine learning projects succeed, it’s often the result of data engineers working behind the scenes. The data engineers have the under-appreciated task of making sure the data is fit for the purpose their downstream colleagues have in mind.

By borrowing concepts from DevOps, the nascent data observability movement empowers data engineers to detect – and possibly fix – problems with data before it reaches downstream users like data analysts and data scientist. Data observability gives data engineers a powerful tool in their toolbox to wield against their sworn enemy: bad data.

Venture capitalists have also spotted the market opportunity that data observability represents. Here are five observability startups that have completed VC funding in the past month:

Observe

Observe develops a SaaS-based platform that’s one part application performance management (APM) tool and one part log analytics and monitoring. The offering, which runs within the Snowflake cloud, collects traces, logs, and metrics data from various monitored applications into a platform where they can be centrally monitored and any problems explored.

Users can get a quick update on the status of applications from centralized dashboards, called Landing Pages, from which they can drill down to explore root causes of problems. From each Landing Page, users can pull up Universe Maps that show how various data is related, providing cross-referencing capability. There are also Worksheets for when data engineers must engage with “hand to hand combat” with data, as well as Alerts that work with PagerDuty, Slack, and web hooks.

Observe was founded by Sutter Hill Ventures in 2017. The VC firm recruited four co-founders from Splunk, Snowflake, Wavefront, and Roblox to join the San Mateo, California company. In May, Sutter Hill Ventures announced a $70 million investment in the company, to go along with $44.5 million in prior financing.

MANTA

Another startup getting attention from VCs is MANTA, a Tampa, Florida-based company that develops what it calls an “automated data lineage platform” that provides visibility into data flows, sources of data, data transformations, and data dependencies.

MANTA claims that by automating the detection of changes in data pipelines and root-cause analysis, it can increase the productivity of data teams by up to 40%. That helps data engineers, as well as data analysts and data scientists, the company says.

In late May, MANTA announced the closing of a $35 million Series B round of funding led by led by Forestay Capital with participation from existing investors Bessemer Venture Partners, SAP.io, Senovo, Credo Ventures, Dan Fougere, and a new investor European Bank for Reconstruction and Development.

“We are compelled by MANTA’s product and vision to provide further visibility into data pipelines,” Alex Ferrara, a partner at Bessemer Venture Partners, says in a press release. “We believe this is a crucible moment for enterprises striving to be truly data-driven organizations.”

Monte Carlo

In May, Monte Carlo announced a $135 million Series D round at a valuation at a $1.6 billion valuation, making it one of the frontrunners in the nascent data observability space.

Data pipelines are growing expeditiously at the moment, as companies move huge amounts of data to data lakes and other systems where they can store it and process it at their leisure. However, the data is not always OK, and Monte Carlo aims to help companies detect some of the common problems that can crop up in data, most often around freshness, completeness, changing values, shifting schemas, and changes to data lineage.

Since being founded in 2019, Monte Carlo has attracted hundreds of paying customers with its data observability offerings, including companies Jet Blue, CNN, and AutoTrader UK. The San Franciso-based company, which had about 120 employees at the end of 2020, is set to expand significantly as customers look for solutions to get their data pipelines under control.

Cribl

Cribl has also emerged as a contender in the data observability space, albeit at a slightly different level. Instead of monitoring data pipelines directly, it keeps an eye on the metric, event, log, and trace, or MELT data, generated by products like Elasticsearch, Splunk, Grafana, Datadog, New Relic, and SumoLogic.

The company’s product, called LogStream, works as a sort of filter for log data generated by these products. LogStream also lets users re-direct the data from these systems, which is handy for customers who want to reduce cost by offloading MELT data into cheaper cloud object stores.

Two weeks ago, Cribl announced a $150 million Series D, bringing its total funding to $400 million. The round was led by Tiger Global Management with participation from existing investors CRV, IVP, Redpoint Ventures, Sequoia, and Greylock Partners. It also launched Cribl Search, which it says will enable users to run queries on any data in any format in real time.

Coralogix

Coralogix is another observability startup that aims to help coral the large amounts of log data flowing through the enterprise, including logs, metrics, traces, and security data.

The San Francisco company develops a SaaS-based offering that uses machine learning techniques to detect changes in the underlying observability data generated by applications, security systems, and other data sources. The company uses a unique term for its observability process: “loggregation.”

The company, which claims to have more than 2000 customers, also tapped into the VC gold rush with a $142 million Series D last week.

Related Items:

Monte Carlo Raises $135 Million to Grow Data Observability Biz

Cribl Announces $150M Series D, Launches Cribl Search

Coralogix Brings ‘Loggregation’ to the CI/CD Process