Observability continues to be one of the biggest technological trends in the field of big data. It certainly attracts the attention of venture capitalists, who have committed hundreds of millions of dollars to start-ups of data observability in recent weeks.
While data analysts and data scientists gain glory when their analysis and machine learning projects succeed, this is often the result of data engineers working behind the scenes. Data engineers have the underestimated task of ensuring that data is appropriate for the purpose of their downstream colleagues.
By borrowing concepts from DevOps, the growing movement of data observability empowers data engineers to detect - and possibly fix - data problems before they reach downstream users, such as data analysts. and data researchers. Data observability gives data engineers a powerful tool in their toolbox to handle against their sworn enemy: bad data.
Venture capitalists have also noticed the market opportunity represented by data observability. Here are five observability startups that completed VC funding in the last month:
Observe is developing a SaaS-based platform, which is part of the application performance management tool (APM) and part of analytics and journal monitoring. The offer, which runs in the Snowflake cloud, collects traces, logs and measurement data from various monitored applications in a platform where they can be centrally monitored and any issues can be explored.
Users can get a quick update on the status of applications in centralized dashboards called Landing Pages, from which they can detail to explore the root causes of problems. From each landing page, users can view maps of the universe showing how different data is linked, providing a 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. VC 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, which will go along with a previous funding of $ 44.5 million.
Another startup that draws attention from VC is MANTA, a Tampa, Florida-based company that is developing what it calls an “automated data generation platform” that provides visibility into data streams, data sources, and data transformations. and data dependencies.
MANTA claims that by automating the detection of changes in data pipes 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, the company says.
At the end of May, MANTA announced the closure of a $ 35 million Series B financing round led by Forestay Capital, with the participation of existing investors, Bessemer Venture Partners, SAP.io, Senovo, Credo Ventures, Dan Fougere and a new European Bank investor. . for Reconstruction and Development.
“We are committed to MANTA’s product and vision to provide more visibility into data pipelines,” said Alex Ferrara, a partner at Bessemer Venture Partners, in a press release. “We believe this is a crucial time for businesses to strive to be truly data-driven organizations.”
In May, Monte Carlo announced a $ 135 million round of the D-Series at a valuation of $ 1.6 billion, making it one of the leaders in the observable data space under development.
Data pipes are growing rapidly at this time, as companies move huge amounts of data to data repositories and other systems where they can store and process them at will. However, the data is not always in order, and Monte Carlo aims to help companies detect some of the common problems that can arise in data, most often related to freshness, completeness, changes in values, changes in patterns and changes in offspring. data.
Since its founding in 2019, Monte Carlo has attracted hundreds of paying customers with its data observability offerings, including Jet Blue, CNN and AutoTrader UK. The San Francisco-based company, which had about 120 employees by the end of 2020, is set to expand significantly as customers look for solutions to control their data pipelines.
Cribl has also emerged as a candidate in the field of data observability, albeit at a slightly different level. Instead of directly monitoring data pipes, keep an eye on metrics, event, log, and tracking data or MELT generated by products such as Elasticsearch, Splunk, Grafana, Datadog, New Relic, and SumoLogic.
The company’s product, called LogStream, works 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 handy for customers who want to reduce costs by downloading MELT data to cheaper cloud object stores.
Two weeks ago, Cribl announced a $ 150 million D-Series, bringing the total funding to $ 400 million. The round was led by Tiger Global Management, with the participation of existing investors CRV, IVP, Redpoint Ventures, Sequoia and Greylock Partners. It has also launched Cribl Search, which it says will allow users to query any data in any format in real time.
Coralogix is another observability startup that aims to help coral large amounts of log data flowing through the enterprise, including logs, values, traces, and security data.
The San Francisco-based company is developing 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 2,000 customers, also entered the VC gold rush with a D series of 142 million dollars last week.
Monte Carlo raises $ 135 million to increase data observability
Cribl announces $ 150 million D series and launches Cribl Search
Coralogix brings the “aggregation” of the CI / CD process