The Coolest Data Observability Companies Of The 2026 Big Data 100
Part 5 of CRN’s Big Data 100 takes a look at the vendors solution providers should know in the data observability space.
Keen Observations
Data teams within businesses and organizations strive to provide internal business users and external customers with analytical insights. But those efforts fall short when poor quality data is fed into analytic systems and processes—garbage in, garbage out, goes the old saying.
The problem has become more acute in recent years as the wave of artificial intelligence applications and agents now being developed and put into production is creating even greater demands for huge volumes of high-quality data.
Data observability tools are focused on the data itself and an organization’s data operations (DataOps) – the flow of data from its source to the end-consumer of analytical results. They monitor and manage the quality and reliability of the data itself, as well as the performance of data pipelines and data infrastructure, and assist with investigating and remediating data-related problems.
Such efforts are critical for maintaining high-quality data for internal operations, data engineering projects, and for building and operating data products and services.
As part of the CRN 2026 Big Data 100, we’ve put together the following list of data observability software companies—from well-established vendors to those in startup mode—that solution providers should be familiar with.
Note: The observability space within the IT industry includes many players who provide observability systems for a range of IT management, application performance management and cybersecurity tasks. This being the Big Data 100 list, we have focused here on the companies that market software for data observability, considered a key component of an organization’s data governance and data management operations.
This week CRN is running the 2026 Big Data 100 list in a series of slide shows, organized by technology category, spotlighting vendors of business analytics software, database systems, data warehouse and data lake systems, data management and integration software, data observability tools, and big data systems and cloud platforms.
Some vendors have big data product portfolios that span multiple technology categories. They appear in the slideshow for the technology segment in which they are most prominent.
Acceldata
Top Executive: Rohit Choudhary, Founder and CEO
Acceldata’s data and AI observability technology provides data, AI and Hadoop observability capabilities that monitor, detect and resolve issues across data and AI pipelines. The tools are used for data profiling, data reconciliation and anomaly detection tasks, and setting and enforcing data quality policies.
Other Acceldata products span data warehousing, agentic data engineering, agentic data management, agentic runtime, and Hadoop modernization.
In May Acceldata announced the general availability of its Autonomous Data & AI Platform, which the company said enables enterprises to autonomously run data analytics and AI agents “with trust” across cloud, on-premises, hybrid and sovereign environments.
Also in May, Acceldata partnered with ServiceNow to integrate Acceldata’s software with the ServiceNow Data Catalog to provide data quality and reliability scores for data feeding the ServiceNow Workflow Data Fabric.
Bigeye
Top Executive: Eleanor Treharne-Jones, CEO
Bigeye’s flagship product, the Bigeye Data Observability Platform, offers a series of functional modules that provide data observability, data governance, data lineage, data sensitivity and metadata management capabilities.
In June 2025 the company launched the Bigeye AI Trust Platform, a new framework that applies the company’s data observability expertise to AI agent usage. And in December the company unveiled AI Guardian, part of the Bigeye AI Trust Platform, which gives enterprises more control over AI data use.
Just this month Bigeye launched Agent Trust Hub, which provides a central place to understand what AI agents are doing with business data by connecting agent activity to data quality, classification, lineage, usage, governance, ownership, policy and cost signals.
Splunk, a Cisco Systems company
Top Executive: Chuck Robbins, CEO
Data observability was one of the key applications for Splunk’s big data platform prior to Cisco Systems’ acquisition of Splunk in March 2024 for $28 billion.
Since then Cisco, which operates Splunk as a subsidiary, has worked to leverage Splunk’s data analytics and observability capabilities alongside Cisco’s networking and security technology portfolios.
In April Cisco struck a deal to buy AI observability specialist Galileo Technologies. Cisco is adding the acquired technology to the Splunk Observability portfolio to boost its AI agent observability and protection capabilities.
Cribl
Top Executive: Clint Sharp, Co-Founder and CEO
Cribl’s products, including Crible Stream, Cribl Edge, Cribl Search, Cribl Guard and Cribl Lakehouse, are designed to collect and manage telemetry or machine-generated data for data observability tasks—largely for IT management and security purposes.
Today the company is positioning itself as “the AI platform for telemetry,” emphasizing the need to manage and analyze telemetry data for both people and AI agents.
In February the company said it surpassed $300 million in annual recurring revenue in 2025.
Grafana Labs
Top Executive: Raj Dutt, Co-Founder and CEO
Grafana Labs is the company behind the popular open-source Grafana full-stack observability platform for analyzing, querying and visualizing machine (metric, log and trace) data from virtually any source.
The flagship visualization and dashboarding tool connects to more than 100 data sources including the open-source Prometheus monitoring and alerting toolkit. Other Grafana Labs products include Mimir, Loki, Tempo, Allow and Grafana k6.
In March Grafana Labs signed a strategic collaboration agreement with Amazon Web Services in a bid to accelerate the use of Grafana Cloud on AWS.
In February the company said that its annual recurring revenue in its fiscal 2026 (ended Jan. 31) surpassed $400 million and supported more than 7,000 customers worldwide.
Hydrolix
Top Executive: Marty Kagan, Co-Founder and CEO
Hydrolix develops a real-time data lake platform used to power log data-intensive applications including observability, security and media performance.
The platform’s capabilities combine streaming data ingestion and real-time queries for working with massive volumes of log data.
Hydrolix, which has strategic partnerships with AWS and Akamai, among others, raised $80 million in August 2025 in a Series C funding round led by QED Investors.
Imply
Top Executive: Fangjin Yang, Co-Founder and CEO
Imply offers a high-performance observability warehouse and a real-time data analytics database for ingesting, managing and searching log, metric and trace data.
Imply Lumi is an observability data warehouse or “data layer” that plugs into observability platforms for storing and serving up telemetry data for search and analysis. It provides proprietary data compression and indexing capabilities and federated query support.
Imply Polaris is a fully managed database-as-a-service, build on the open-source Apache Druid real-time database, for managing observability data and building observability applications.
In January Imply and Cribl teamed up to offer a joint solution that combines Imply Lumi at the data query and storage layer with Cribl Stream operating at the data ingest and routing layer.
Monte Carlo
Top Executive: Barr Moses, Co-Founder and CEO
Monte Carlo touts its data and AI observability platform as bringing software engineering standards of reliability to data pipelines and AI models. The company’s system continuously tracks data health across the entire “data stack” and is integrated with data warehouse and data lake systems and business intelligence tools.
The Monte Carlo platform provides autonomous observability capabilities for monitoring, troubleshooting and improving the performance of AI agents in production. Poor quality data and bad data retrieval context can lead to “hallucinated outputs, silent behavioral drift and runaway token costs,” according to the company.
In September 2025 Monte Carlo launched Agent Observability for detecting, triaging and resolving AI reliability issues in production. In February of this year the company announced the general availability of Operations Agent, an in-platform agent that assists data teams with their data workflow monitoring tasks.
Sumo Logic
Top Executive: Mark Ties, CEO
Sumo Logic’s cloud-native Intelligent Operations Platform is used to collect, monitor and analyze machine data, particularly operational and security data, for application performance and cloud security, security operations and SIEM tasks.
In May Sumo Logic announced that its platform had been integrated with the Claude Compliance API, providing compliance and security teams with visibility into Claude usage and helping them maintain security, transparency and accountability.
Unravel Data
Top Executive: Kunal Agarwal, CEO
Unravel Data offers an AI-powered data platform optimization and FinOps tool that organizations use to manage and troubleshoot their data operations and reduce the costs of managing complex data environments.
The company’s platform provides full-stack visibility into data jobs spanning batch processing, data streaming, model training and generative AI applications. The technology autonomously optimizes datasets and data workloads, detects issues with data processes and applies fixes.
The Unravel Data system offers optimization capabilities for Snowflake, Databricks, Cloudera and Google Cloud Big Query platforms.
In May the company unveiled Arvix AI, an agentic engine that autonomously tunes and optimizes every optimization action within Unravel. The new offering, built upon a decade of data platform telemetry, continuously analyzes data workloads, rewrites code, right-sizes data infrastructure, eliminates storage waste, and validates every change before applying it, according to the company.