Acceldata
From Data Observability to Agentic Data Management
Description
Acceldata provides an advanced data platform transitioning traditional data observability into Agentic Data Management (ADM). This approach moves beyond siloed tools for data quality, governance, and catalogs by employing AI agents that deeply understand data context, detect anomalies, and initiate precise corrective actions automatically. The platform aims to manage increasing data volumes and complexity across multi-cloud and on-premises environments, ensuring continuous reliability and preventing costly issues like downtime and governance gaps.
The Acceldata platform features include AI Agents, an exabyte-scale xLake Reasoning Engine, a natural language interface called The Business Notebook, and an Agent Studio for custom agent development. It offers comprehensive observability across data quality, pipelines, infrastructure, users, and costs, coupled with capabilities like data reconciliation and FinOps management. Acceldata supports major data platforms such as Snowflake, Databricks, AWS, Hadoop, GCP, and Azure, catering to various industries including financial services, manufacturing, and retail by improving data reliability and optimizing operational costs.
Key Features
- AI Agents: Understand data context, detect anomalies, and take precise corrective actions.
- Agentic Data Management (ADM): Unifies data quality, governance, and catalogs with autonomous actions.
- xLake Reasoning Engine: Exabyte-scale, AI-aware data processing engine for diverse environments.
- The Business Notebook: Natural language interface with contextual memory for interaction and explanation.
- Agent Studio: Build and deploy custom AI agents.
- Comprehensive Data Observability: Covers quality, pipelines, infrastructure, users, and costs.
- Data Quality & Reliability: Features anomaly detection, profiling, freshness checks, schema drift monitoring, and automated classification.
- Cost Optimization: Provides cost metrics, trends, query analysis, dashboards, alerts, chargeback/showback.
- Data Reconciliation: Efficiently verifies large datasets across systems.
- AI Copilot: Assists with various data management tasks.
- Multi-Platform Support: Integrates with Snowflake, Databricks, AWS, Hadoop, GCP, Azure.
Use Cases
- Improving enterprise data quality and reliability.
- Optimizing cloud data costs and implementing FinOps.
- Managing complex, large-scale data environments.
- Ensuring data integrity during cloud migrations.
- Automating data anomaly detection and resolution.
- Facilitating collaboration between business and data teams.
- Enabling AI and LLM initiatives with trusted data.
- Replacing disparate or DIY data management tools.
Frequently Asked Questions
Does my data leave the premises?
No, Acceldata observes your data inline so it is highly scalable and secure.
What types of data can you manage?
Acceldata observes structured, unstructured and streaming datasets.
Do you provide recommendations?
Yes, Acceldata provides recommendations on data quality rules to use based on the data context and several others.
Do you support on-prem and cloud data?
Yes, Acceldata can observe data that’s on premises and in the cloud, making it suitable for cloud migration initiatives.
Can I customize data quality rules?
Yes, you can customize data quality rules and specify exactly where they apply.
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