Acceldata Accelerates Autonomous Data and AI

The emergence of artificial intelligence (AI) agents capable of automating enterprise decision-making has placed greater focus on the need for trusted and reliable data. As I recently explained, the guardrails provided by agent harnesses ensure the output of agents and large language models (LLMs) is grounded by enterprise data. The accuracy and trustworthiness of the content are dependent on the quality and reliability of the underlying data, however. Software providers with a track record of ensuring data is fit for human consumption, such as Acceldata, are updating their products to ensure they can keep up with the performance and scalability requirements of AI agents.

Acceldata was founded in 2018 by former executives and engineers of Apache Hadoop-specialist Hortonworks. The founders identified an opportunity to help organizations address scaling and performance issues for data initiatives by developing a product to monitor and manage the reliability of data pipelines and data infrastructure. Acceldata was rated an Innovative Provider and an Overall Leader in ISG’s 2025 Buyers Guide for Data Observability, as well as a Capability and Product Experience Leader. Acceldata has expanded its platform capabilities and addressable market in recent years, with investment in research and development to add agentic data engineering and AI observability functionality, as well as the expansion of its operations into Europe. Acceldata’s growth has been fueled by almost $100 million in funding, including a $50 million Series C round provided in 2023 by March Capital, Sanabil Investments, Industry Ventures and Insight Partners.

Awareness of the importance of data observability has risen in recent years as enterprises have focused on the need to validate the quality of data flowing through data pipelines used to support business intelligence (BI) and AI use cases. I assert that through 2028, more than two-thirds of enterprises will invest in initiatives to improve trust in data through the adoption of data observability tools that detect, resolve and prevent data reliability issues. While traditional data quality software helps users identify and resolve issues with the quality of data generated by transformation and integration pipelines, data observability software automates the detection and identification of the causes of data quality problems. Data observability involves monitoring key attributes of the data—including freshness, distribution, volume, schema and lineage—as well as the integrity of the infrastructure used to transport and process the data. By identifying the causes of data quality issues, data observability software potentially enables users to accelerate resolution and limit their impact.

Acceldata’s focus on data infrastructure scaling and performance issues remains a differentiator among data observability specialists, which tend to focus primarily on the data pipeline itself. The company’s Autonomous Data & AI Platform is built on a foundation of multiple open-source projects—known as OS Foundry—that address data governance, data processing and data orchestration. Key components include Apache Ranger for catalog-based governance, Apache YuniKorn for resource management and workflow scheduling, Apache Airflow for pipeline orchestration and Apache NiFi for data ingestion and routing. Acceldata’s OS Foundry also provides multiple options for data processing (with support for Apache Spark, Apache Flink and Trino), table formats (Apache Iceberg, Apache Hudi and Delta Lake) and storage formats (Parquet, ORC and Avro). In May, the company refreshed its platform with the general availability of the Autonomous Data & AI Platform and its new xLake architecture, which is designed to address requirements for AI initiatives that span hybrid on-premises and cloud environments.

The xLake architecture comprises a Kubernetes-based Data Plane that runs in a customer’s own virtual private cloud environment as well as an Acceldata-managed Control Plane. The various components that make up the OS Foundry are delivered via xGovern for catalog-based data governance, xRoute for workload scheduling and xObserve for monitoring data, AI and the underlying compute resources. In addition to the company’s established data observability functionality for data profiling, data quality, data reconciliation and data lineage, Acceldata has recently enhanced xObserve with AI observability capabilities, including data privacy and access controls, prompt evaluation and monitoring, agent and model tracing and production monitoring. The xLake architecture is enabled by xReasoning, a reasoning engine designed to manage the data estate and automate the resolution of data and infrastructure issues based on a combination of semantic metadata and contextual memory that enables the understanding of business ownership and impact.

Data quality is a primary challenge for enterprises scaling investments in AI initiatives. It is second only to demonstrating value and return on investment, according to participants in ISG’s Market Lens Data and AI Program Study. Acceldata is differentiated by the breadth of capabilities offered by its platform, including not only data observability but also infrastructure observability as well as governance, scheduling and reasoning-based automation. The addition of AI observability is a natural extension and should appeal to enterprises as they recognize the need to take a more holistic view of data and AI to address the requirements of agentic decision-making. I recommend that enterprises evaluating data and AI observability software products to improve the reliability of AI and data infrastructure and trust in AI and data initiatives include Acceldata in their assessments. 

Regards,

Matt Aslett