With thatDot observability platform, you can keep content delivery networks at peak health
thatDot provides a unified observability platform that integrates logs, metrics, and traces from multiple sources. This holistic view allows for more effective monitoring and troubleshooting of content delivery networks (CDNs), reducing the time and effort required to diagnose and fix issues, and improving overall service reliability and performance.
Rich Contextual Insights and Root Cause Analysis
thatDot’s platform enriches observability data with contextual information, making it easier to perform root cause analysis. By correlating events across different layers of the content delivery stack (e.g., network, application, infrastructure), teams can pinpoint the exact source of issues more efficiently, leading to faster remediation and improved system reliability.
Advanced Anomaly Detection and Alerts
thatDot employs sophisticated anomaly detection algorithms that can identify unusual patterns or deviations in real-time. Coupled with customizable alerting mechanisms, this ensures that potential issues are flagged promptly, allowing for proactive management and rapid resolution of problems before they impact end-users.
Unified Dashboard for Troubleshooting
thatDot offers a unified dashboard where all logs, metrics, and traces from various sources are centralized. This integration simplifies the troubleshooting process by providing a single pane of glass, enabling teams to quickly visualize, assess, and remediate the problems and performance of the content delivery system.
Use Cases
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Real-time Blockchain Fraud Detection
The Problem Real-time linking of transactions, accounts, wallets, and blocks within and across blockchains is not possible with current solutions. Instead, the user must either rely on batch…
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Authentication Fraud
The Problem Metered attacks that generate low volume log-in attempts, from diverse IPs and across extended time frames, are designed to avoid the “3 strikes in 24 hours”…
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Financial Fraud Detection
The Problem Financial fraud detection requires monitoring billions of transactions, devices and users in real-time for suspect behaviors without false positives that alienate customers when service is denied…
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Video Observability for Root Cause Analysis
The Problem Real-time video observability that can solve Quality of Experience (QoE) issues while live broadcast events are still playing require the simultaneous monitoring of millions of data…
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Streaming Graph ETL
The Problem Most ETL tools use the batch processing paradigm to find high-value patterns in large volumes of data. Whether the specific business application is fraud detection, cyber…
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Log Analysis
The Problem Monitoring systems comprised of multiple services is typically done by monitoring each service individually using it’s logs, or on an end to end basis that lacks…
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Graph AI
The Problem Pick One. Recent AI research is generating a growing number of graph AI techniques that take advantage of graph data relationships, and the rich context it…
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Stateful Digital Twin
The Problem While digital twins and the emerging subcategory of asset graphs promise operators greater visibility into the relationships between IT assets and equipment under management, current approaches…
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Real-Time IoB Threat Hunting
The Problem Modern threat detection requires data – lots of data – typically from multiple sources. This brings with it a number of interesting data engineering challenges, especially…