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Acceldata

Industry	Financial Services Retail and Consumer Packaged Goods Life Sciences Manufacturing
Founded	August 1st, 2018
Founder	Rohit Choudhary (Founder) Ashwin Rajeeva (Co-Founder) Gaurav Nagar (Co-Founder) Raghu Mitra (Co-Founder)
Headquarters	Campbell, California , United States
Products	Agentic Data Management Platform (ADM) Acceldata Data Observability Cloud (ADOC) Acceldata Pulse (Data Observability for Hadoop) Open Data Platform (Hadoop)
Services	Agentic Data Management (ADM) Data Observability Data Quality ) Data Governance
Number of employees	200 - 300 Employees (2025)
Website	https://www.acceldata.io/

Acceldata is an enterprise software company (B2B SaaS Company) that provides an agentic data management platform and data observability solutions. The company was founded in 2018 by Rohit Choudhary, Ashwin Rajeeva, and Gaurav Nagar and Raghu Mitra in San Jose, California. Acceldata began as a provider of data observability solutions, created to help organizations monitor and improve the reliability and performance of large-scale data systems^[1]. Its early focus addressed challenges faced by enterprises in managing the quality and availability of data across complex environments.

Building on its foundation in data observability, Acceldata developed an agentic data management platform that applies artificial intelligence agents or data agents to autonomously detect, analyze, and resolve issues across the data lifecycle^[2]. This system combines data observability, data governance, and optimization into a unified approach that allows data environments to self-monitor, self-correct, and adapt over time. By shifting from manual, reactive operations to more automated processes, the company’s technology supports scalable and efficient data management across cloud, hybrid, and on-premises systems.

Acceldata was founded on August 1, 2018, in San Jose, California, by Rohit Choudhary, Ashwin Rajeeva, and Gaurav Nagar, and Raghu Mitra. The company was created to address challenges in monitoring and managing data pipelines, and its founders introduced the term “data observability” in 2018 to describe this approach.

In August 2022, Acceldata announced the general availability of the Data Observability Cloud (DOC), a platform designed to provide enterprises with tools to monitor and ensure reliability of their data systems^[3]. That year, the company also introduced integrations for Snowflake and Databricks, made its services available through the AWS Marketplace, and released an Open Data Platform as a free, supported open-source option for enterprises.

Between late 2022 and early 2023, Acceldata raised a $50 million Series C funding round, which brought its total funding to over $100 million^[4]. In 2023, the company acquired Bewgle, a firm specializing in artificial intelligence and large language models, to extend its capabilities in AI-driven data management^[5]. Acceldata also recorded growth in enterprise adoption during this period and received recognition such as InfoWorld’s 2023 Technology of the Year Award^[6].

In 2024, Acceldata was named a Data Quality Leader in the G2 Fall 2024 Report based on customer feedback^[7].

In 2025, the company introduced its Agentic Data Management platform^[8]. The platform applies AI agents to autonomously detect, analyze, and address issues across the data lifecycle, bringing together observability, governance, and optimization into a unified system. The launch marked an expansion of Acceldata’s focus from monitoring data systems to enabling enterprises to manage and adapt their data environments through automated, agent-driven processes^[9].

Agentic Data Management Platform (ADM): A platform that uses AI agents to automatically detect and resolve issues in enterprise data systems. It combines observability, governance, and optimization into one framework, allowing organizations to manage data across cloud, hybrid, and on-premises environments with less manual effort^[10].

Acceldata Data Observability Cloud (ADOC): A cloud-based system that gives enterprises visibility into the health and performance of their data pipelines and infrastructure. It supports monitoring of data quality, reliability, and costs, helping teams prevent failures and manage resources effectively.

Acceldata Pulse: A product designed for Hadoop environments that offers monitoring and performance insights for large data clusters. It helps organizations track resource use, identify bottlenecks, and maintain reliability while planning for modernization or migration.

Open Data Platform (ODP): An open-source distribution^[11] that provides a flexible foundation for enterprises using Hadoop and related technologies. It supports on-premises, cloud, and hybrid deployments and is built to integrate with Acceldata’s observability tools for operational visibility.

Acceldata develops software that helps organizations keep track of their data and make sure it is reliable, accurate, and cost-efficient. This approach is known as data observability and agentic data management. In simple terms, the platform acts like a monitoring and management system for data, making sure it is clean, trustworthy, and ready to be used for analytics and artificial intelligence.

The software uses automation and machine learning (a type of artificial intelligence that learns from patterns in data) to spot problems such as errors, missing information, or unusual trends. It can also trace how data moves between systems, identify where an issue started, and suggest how to fix it. A feature called xLake allows the system to combine information from different sources and answer questions in natural language, so users can interact with it more easily.

The platform can work with different types of AI models, including large language models (LLMs) like those used in modern AI applications. It can run in the cloud (hosted by providers such as AWS or Google Cloud), in private company data centers, or in a mix of both (hybrid).

To ensure safety, Acceldata includes common security practices such as password protection and multi-factor authentication (logging in with more than just a password), encryption (scrambling data so only authorized users can read it), and audit logs (records of who accessed or changed data). It also allows organizations to set rules about who can access specific data and tools.

The software offers two ways to run checks on data. In PushDown mode, the system uses the computing power of platforms like Snowflake or BigQuery to run its checks. In ScaleOut mode, it uses a distributed system like Apache Spark, which allows large-scale data processing across many computers in parallel.

Acceldata connects to a range of data platforms, processing engines, orchestration tools, and collaboration systems. The following list is representative rather than exhaustive.

Cloud platforms

• Amazon Web Services, Microsoft Azure, Google Cloud Platform

Data warehouses and lakehouses

• Snowflake, Databricks, Google BigQuery, Amazon Redshift, Azure Synapse, Azure SQL Data Warehouse

Data lakes and storage

• Amazon S3, Azure Data Lake Storage Gen2, Google Cloud Storage, Azure Blob Storage, Apache HDFS, Delta and Parquet files

Processing and query engines

• Apache Spark, Presto, Trino, Apache Hive, Apache Impala, ClickHouse, Azure Databricks

Streaming and messaging

• Apache Kafka, AWS Kinesis

Orchestration and scheduling

• Apache Airflow, Amazon Managed Workflows for Apache Airflow, Autosys

Relational databases

• Amazon Aurora MySQL and PostgreSQL, Amazon RDS MySQL, MariaDB, PostgreSQL, Microsoft SQL Server, Oracle, IBM Db2, SAP HANA, Teradata, Azure SQL, Azure Cosmos DB for PostgreSQL

NoSQL and Hadoop ecosystem

• Apache HBase, Cassandra, MongoDB, MapR connectors

File transfer and secrets

• SFTP connector, AWS Secrets Manager, Microsoft Azure Key Vault, Azure Service Principal

Identity and access

• Okta, OpenID

Business intelligence and analytics

• Power BI, Tableau, Looker

Collaboration and incident management

• Slack, Microsoft Teams, Google Hangouts, Email notifications, Jira, ServiceNow, Opsgenie, PagerDuty

The platform can be deployed in cloud, on-premises, or hybrid settings. It provides a central control plane for user management, policy administration, metrics collection, and alerting. Integrations are added through connectors and APIs so that the software can observe and analyze data across heterogeneous environments.