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Monte Carlo

Monte Carlo

Monte Carlo is a San Francisco-based software and data company developing a digital data reliability platform for monitoring and alerting users around missing or inaccurate data.

OverviewStructured DataIssuesContributors

Contents

montecarlodata.com
Is a
Organization
Organization
Company
Company

Company attributes

Industry
Database
Database
Information technology
Information technology
Analytics
Analytics
Enterprise software
Enterprise software
Technology
Technology
Software
Software
Big data
Big data
Location
San Francisco
San Francisco
B2X
B2B
B2B
CEO
Barr Moses
Barr Moses
Founder
Lior Gavish
Lior Gavish
Barr Moses
Barr Moses
AngelList URL
angel.co/company/mo...arlo-data-1
Pitchbook URL
pitchbook.com/profiles...437715-73
Legal Name
Monte Carlo, Inc.
Number of Employees (Ranges)
51 – 200
Email Address
info@montecarlodata.com
Investors
GIC Private Limited
GIC Private Limited
The Accel Partners
The Accel Partners
Institutional Venture Partners
Institutional Venture Partners
0
Salesforce Ventures
Salesforce Ventures
ICONIQ Capital
ICONIQ Capital
GGV Capital
GGV Capital
Redpoint Ventures
Redpoint Ventures
Founded Date
2019
Total Funding Amount (USD)
236,000,000
Latest Funding Round Date
May 24, 2022
Competitors
Dynatrace
Dynatrace
Cribl, Inc.
Cribl, Inc.
SingleStore
SingleStore
SolarWinds Database Performance Monitoring
SolarWinds Database Performance Monitoring
Zabbix
Zabbix
Datadog
Datadog
Business Model
Subscription
Also Known As
Monte Carlo Data
Latest Funding Type
Series D
Series D
Wellfound ID
monte-carlo-data-1

Other attributes

Company Operating Status
Active
Latest Funding Round Amount (USD)
135,000,000

Monte Carlo is a developer of a digital data reliability platform intended to monitor and offer alerts for missing or inaccurate data. The company's platform resolves data problems, leading to stronger data teams and insights that deliver true business value. The company was founded in 2019 and is based in San Francisco.

Platform
Data Collector
Data collector architecture to maintain data privacy and security.

Data collector architecture to maintain data privacy and security.

Monte Carlo uses a data collector to connect the company's platform to data warehouses, data lakes, and business intelligence tools in order to collect metadata, logs, and statistics about the company. The service is designed to maintain all data in a user's environment, such that the data cannot leave Monte Carlo's cloud, especially in reference to individual data records or personally identifiable information, which Monte Carlo does not collect. Furthermore, connectivity to the data warehouses, data lakes, and business intelligence tools are never exposed outside of Monte Carlo's cloud. These goals are accomplished using a specific architecture that allows users to install the data collector in their AWS environment, rather than external to that environment.

Integrations

With data warehouse and data lake integrations, Monte Carlo is able to track the health of data in tables and pipelines. By pulling metadata, query logs, and metrics from a data warehouse, Monte Carlo works to provide users end-to-end data observability. While the integration of business intelligence tools allows Monte Carlo to track metadata and lineage for any dashboards or reports used in an environment.

Data health monitoring

Monte Carlo works to develop and deploy three types of data health monitoring in order to be able to increase the number of relevant issues detected by the platform, while minimizing the operational and computation costs associated with creating those monitors. This is achieved through the use of full coverage machine learning-driven detection, opt-in coverage machine learning-detection, and customer rule-based detection.

Monte Carlo data monitoring types

Data monitor
Issues detected

Automatic machine learning-driven detection

This data monitor works to detect how often each table in an environment is updated, if there are any delays to those updates, how much data is added, removed, or updated for each table update, and alerts users if the tables grow or shrink unexpectedly. As well, this monitor watches all schema in an environment and alerts users if any is added, removed, or updated.

Custom rule-based detection

This data monitor allows data teams to define custom rules using SQL statements to check for specific conditions and alert those teams when those conditions are breached.

Opt-in machine learning-driven detection

This data monitor works to calculate and monitor multiple metrics and statistics and alert a user if there are substantial changes in any of those metrics or statistics. As well, it monitors the frequency of field values and alerts users of unexpected changes in the distribution. The monitor also watches fields nested in JSON values for changes in the structure, and alerts users if there are changes to that structure.

Customers

Customers and users of Monte Carlo's data monitoring platform include Fox, Hearst, Hippo, Affirm, ThredUp, Kolibri, Vimeo, Mercari, PagerDuty, AutoTrader, Hotjar, The Farmer's Dog, Compass, Blinkist, Resident, Yotpo, Mindbody, Optoro, New Relic, and Eventbrite.

Timeline

No Timeline data yet.

Funding Rounds

Products

Acquisitions

SBIR/STTR Awards

Patents

Further Resources

Title
Author
Link
Type
Date

Data observability startup Monte Carlo raises $60M

Kyle Wiggers

https://venturebeat.com/2021/08/17/data-observability-startup-monte-carlo-raises-60m/

Web

August 17, 2021

Monte Carlo raises $25M for its data observability service

Alex Wilhelm

https://techcrunch.com/2021/02/09/monte-carlo-raises-25m-for-its-data-observability-service/?guccounter=1

Web

February 9, 2021

Monte Carlo: Using Data Observability to Improve Data Reliability

Hashmap

https://medium.com/hashmapinc/monte-carlo-using-data-observability-to-improve-data-reliability-5cf15cbb6701

Web

June 23, 2021

References

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