Elementary Data Analysis
What is Elementary Data?
Elementary builds a control plane for data and AI reliability. The product combines observability, data quality, governance, discovery, and incident response for modern data teams. The company says more than 1,500 data teams and customers including Elastic, RGA, Now Pensions, StubHub International, Fiverr, and Scalapay use its platform.
Location
Tel Aviv-Yafo, Israel
Employees
11-50
Founded
2021
Revenue
$5M
Business Model
Predictable pricing model designed to scale and support optimal coverage.
Company Stage
Seed
Investors
- Cowboy Ventures
- TLV Partners
- Y Combinator
YC Batch
W22
Product Features & Capabilities
- dbt-native data observability, data quality tests, anomaly detection, data lineage, incident management, alerts, data health scores, performance tracking, cost tracking, data catalog.
Pricing
- Starter Plan:
- 2 Seats, 1 Env (+$150/m per seat, up to 3)
- Unlimited monitored tables
- End-to-end column-level lineage
- Essentials Plan:
- 8 Seats, 2 Envs (+ discounted additional seats)
- Advanced alerts
- Triggered syncs for real-time monitoring
- Enterprise Plan:
- 20+ Seats, 4+ Envs (+ discounted additional seats)
- Custom contracts and SLA
- Advanced security (OKTA, SAML)
Market Focus
Elementary Data primarily focuses on the data observability market, specifically providing tools for data quality and reliability aimed at users of dbt (data build tool).
Competitors Overview
Elementary Data operates in the data observability tools market, which focuses on ensuring data quality and reliability for data teams. Its main competitors include:
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Monte Carlo: Known for its strong focus on data reliability and observability, Monte Carlo offers features like data lineage and anomaly detection. It is often praised for its user-friendly interface and robust integration capabilities.
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Datadog: A comprehensive monitoring and analytics platform that provides observability across applications and infrastructure. Datadog's advantage lies in its extensive integrations and real-time monitoring capabilities, making it suitable for large-scale environments.
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Dynatrace: Offers advanced observability solutions with AI-driven insights. Its strengths include automated monitoring and root cause analysis, which can significantly reduce troubleshooting time for data teams.
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Acceldata: Focuses on data observability and quality, providing tools for data lineage, anomaly detection, and incident management. Acceldata is noted for its strong analytics capabilities and proactive monitoring features.
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Great Expectations: An open-source tool that emphasizes data quality and testing. It allows teams to define expectations for their data and automatically validate them, which can be a significant advantage for teams looking for customizable solutions.
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IBM Watson: Offers a suite of data observability tools as part of its broader AI and analytics offerings. IBM's advantage is its extensive resources and integration with enterprise-level solutions.
Notable differences include the focus areas of each competitor, such as Monte Carlo's emphasis on data reliability, Datadog's comprehensive monitoring capabilities, and Dynatrace's AI-driven insights. Each competitor has unique strengths that may appeal to different segments of the market.
Company Pricing
- Starter Plan:
- 2 Seats, 1 Env (+$150/m per seat, up to 3)
- Unlimited monitored tables
- End-to-end column-level lineage
- Essentials Plan:
- 8 Seats, 2 Envs (+ discounted additional seats)
- Advanced alerts
- Triggered syncs for real-time monitoring
- Enterprise Plan:
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