VCs open the checkbook for observability startups

Observability continues to be one of the biggest technology 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. Data engineers have the understated task of ensuring data is fit for the purpose their downstream colleagues have in mind.

Borrowing concepts from DevOps, the nascent data observability movement enables data engineers to detect — and possibly fix — data-related issues before they reach downstream users such as data analysts. and data scientists. Data observability gives data engineers a powerful tool in their toolbox to fight against their nemesis: bad data.

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

Observe

Observe develops a SaaS platform that is both an application performance management (APM) tool and a part of log analysis and monitoring. The offering, which runs in the Snowflake cloud, collects traces, logs, and metrics data from various monitored applications into one platform where they can be centrally monitored and any issues explored.

Users can get a quick update on the status of applications from centralized dashboards, called Landing Pages, from which they can explore the root causes of issues. From each landing page, users can extract universe maps that show how various data is related, providing cross-reference capability. There are also worksheets for when data engineers need to engage in “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 venture capital firm recruited four co-founders from Splunk, Snowflake, Wavefront, and Roblox to join the San Mateo, California-based company. In May, Sutter Hill Ventures announced a $70 million investment in the company, to accompany pre-funding of $44.5 million.

MANTA

Another startup that’s catching the eye of VCs is MANTA, a Tampa, Florida-based company that’s developing what it calls an “automated data lineage platform” that provides visibility into data streams, data sources, 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%. This helps data engineers, as well as data analysts and data scientists, according to the company.

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

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

Monte Carlo

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

Data pipelines are growing rapidly right now as companies transfer massive amounts of data to data lakes and other systems where they can store and process it as they see fit. However, data is not always correct, and Monte Carlo aims to help companies catch some of the common issues that can arise in data, most commonly around freshness, completeness, changing values, patterns changes and changes in data lineage.

Since its inception in 2019, Monte Carlo has attracted hundreds of paying customers through its data observability offerings, including companies Jet Blue, CNN and AutoTrader UK. The San Francisco-based company, which had around 120 employees at the end of 2020, is expected to grow significantly as customers seek solutions to master their data pipelines.

Screen

Cribl has also become a competitor in the field of data observability, although on a slightly different level. Instead of directly monitoring data pipelines, it keeps tabs on 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, functions as a sort of filter for the log data generated by these products. LogStream also allows users to redirect data from these systems, which is convenient for customers who want to save costs by offloading MELT data to cheaper cloud object stores.

Two weeks ago, Cribl announced a $150 million Series D round, 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 has also launched Cribl Search, which it says will allow users to run queries on any data in any format in real time.

Coralogix

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

The San Francisco company is developing a SaaS-based offering that uses machine learning techniques to detect changes in 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 2,000 customers, also benefited from the VC gold rush with a $142 million Series D last week.

Related articles:

Monte Carlo raises $135 million to grow data observability business

Cribl announces $150 million Series D and launches Cribl Search

Coralogix brings “loggregation” to the CI/CD process

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