Acceldata
Acceldata is an agentic data management platform that helps enterprises monitor, govern, and optimize data across cloud, lakehouse, and hybrid environments. The platform combines AI-powered agents with data observability to proactively detect issues, trace root causes, and automate remediation workflows. Key products include ADM (Agentic Data Management), ADOC (Acceldata Data Observability Cloud), Pulse for Hadoop environments, and Agent Studio for building custom AI agents. It supports integrations with Snowflake, Databricks, AWS, GCP, Azure, and Hadoop.
APIs
Acceldata Data Observability Cloud API
The ADOC API provides programmatic access to data observability features including alerts, data quality rules, pipeline monitoring, data lineage, users, groups, roles, and permi...
Capabilities
Features
AI-powered agents that proactively detect issues, trace root causes, and automate data quality remediation workflows
Multi-variate anomaly detection, column-level profiling, and proactive monitoring across all data platforms
End-to-end data lineage visualization with schema change management and column-level impact analysis
Real-time SLA monitoring, bottleneck identification, and root cause analysis for data pipelines
Visibility into data spending, budget optimization, chargebacks, and cost forecasting across cloud environments
Natural language interface with contextual memory for querying data quality and observability insights
Low-code environment for building and deploying custom AI agents for data management workflows
Bring Your Own Large Language Model for enterprise-controlled AI inference within data operations
Exabyte-scale, AI-aware processing engine supporting cloud hyperscalers and on-premises deployments
Use Cases
Continuously monitor and automatically remediate data quality issues across cloud and hybrid environments
Validate data completeness, consistency, and accuracy during cloud migration projects
Ensure data pipelines produce clean, reliable, and AI-ready datasets for training and inference
Identify and reduce wasteful data pipeline and infrastructure costs with granular usage analytics
Automatically detect and resolve discrepancies between source and target systems across platforms
Track data lineage and access patterns to support regulatory compliance and data governance programs
Integrations
Native integration for monitoring Snowflake data quality, query performance, and cost optimization
Integration for observing Databricks lakehouse pipelines, job health, and data quality
Support for AWS data services including S3, Redshift, Glue, EMR, and Athena
Integration with GCP data services including BigQuery, Dataflow, and Cloud Storage
Integration with Azure Synapse, Azure Data Factory, and Azure Data Lake Storage
Dedicated Pulse product for Hadoop ecosystem monitoring including HDFS, YARN, Hive, and Spark