DataHub Cloud is an event-driven AI & Data Context Platform that uses active metadata for real-time visibility across your entire data ecosystem. Unlike traditional data catalogs that provide outdated snapshots, DataHub Cloud instantly propagates changes, automatically enforces policies, and connects every data source across platforms with 100+ pre-built connectors.
Built on an open source foundation with a thriving community of 13,000+ members, DataHub gives you unmatched flexibility to customize and extend without vendor lock-in. DataHub Cloud is a modern metadata platform with REST and GraphQL APIs that optimize performance for complex queries, essential for AI-ready data management and ML lifecycle support.
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NeuBird AI is a Production Ops Platform for ITOps, SRE, and DevOps teams that brings agentic AI to production cloud environments. It continuously analyzes telemetry across Amazon CloudWatch, Azure Monitor, logs, metrics, traces, and changes to help teams prevent incidents, automate root cause analysis, and optimize cloud operations in real time.
Instead of relying on dashboards and manual investigation, NeuBird AI automatically detects degradation, reduces alert noise, and identifies root cause in minutes. It enables teams to move from reactive firefighting to proactive operations.
Built for production cloud and Kubernetes environments, NeuBird integrates with AWS, Azure and OpenShift services and existing observability and incident management tools with no rip and replace required.
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Sifflet
Automatically cover thousands of tables with ML-based anomaly detection and 50+ custom metrics. Comprehensive data and metadata monitoring. Exhaustive mapping of all dependencies between assets, from ingestion to BI. Enhanced productivity and collaboration between data engineers and data consumers. Sifflet seamlessly integrates into your data sources and preferred tools and can run on AWS, Google Cloud Platform, and Microsoft Azure. Keep an eye on the health of your data and alert the team when quality criteria aren’t met. Set up in a few clicks the fundamental coverage of all your tables. Configure the frequency of runs, their criticality, and even customized notifications at the same time. Leverage ML-based rules to detect any anomaly in your data. No need for an initial configuration. A unique model for each rule learns from historical data and from user feedback. Complement the automated rules with a library of 50+ templates that can be applied to any asset.
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Metaplane
Monitor your entire warehouse in 30 minutes. Identify downstream impact with automated warehouse-to-BI lineage. Trust takes seconds to lose and months to regain. Gain peace of mind with observability built for the modern data era. Code-based tests take hours to write and maintain, so it's hard to achieve the coverage you need. In Metaplane, you can add hundreds of tests within minutes. We support foundational tests (e.g. row counts, freshness, and schema drift), more complex tests (distribution drift, nullness shifts, enum changes), custom SQL, and everything in between. Manual thresholds take a long time to set and quickly go stale as your data changes. Our anomaly detection models learn from historical metadata to automatically detect outliers. Monitor what matters, all while accounting for seasonality, trends, and feedback from your team to minimize alert fatigue. Of course, you can override with manual thresholds, too.
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