Category: DNS and Big Data

In the evolving landscape of network observability and cybersecurity analytics, Domain Name System (DNS) telemetry has emerged as a foundational data stream. Its ubiquity, regularity, and low payload cost make it ideal for understanding real-time system behavior, threat actor patterns, and long-term infrastructure trends. However, the scale of this telemetry—frequently measured in billions of events…

In the realm of modern data architecture, DNS telemetry has evolved into a cornerstone for a broad spectrum of analytical and security applications. From monitoring network health and detecting anomalies to tracing malware activity and supporting regulatory investigations, DNS logs provide a uniquely rich view of system and user behavior. As this data is centralized…

As Software-as-a-Service (SaaS) platforms continue to scale horizontally across a diverse customer base, the need for observability and security visibility at the DNS layer has taken on new urgency. DNS telemetry serves as a crucial foundation for detecting tenant-specific issues, enforcing isolation boundaries, and identifying shared infrastructure threats. In multi-tenant SaaS environments, however, the challenges…

As the internet continues to expand at an unprecedented rate, the volume of DNS traffic generated globally has surged to petabyte-scale levels. Every query sent to resolve a domain name leaves a digital footprint, and when aggregated, these DNS records form a rich dataset that holds vast potential for insights into user behavior, threat detection,…

The Domain Name System serves as one of the foundational pillars of internet communication, translating human-readable domain names into IP addresses that machines understand. Given its essential role, DNS infrastructure must be resilient, responsive, and scalable, especially in environments that handle billions of queries daily. Sudden traffic spikes—whether from benign events like software updates or…

As organizations increasingly migrate their data infrastructure to cloud-native platforms, the need to extract maximum value from operational data becomes paramount. One of the richest yet underutilized sources of data is DNS traffic, which provides deep visibility into network behavior, application usage, user intent, and potential security threats. However, raw DNS query logs, while voluminous…

In the modern landscape of big data analytics, DNS logs are an invaluable resource, often serving as a foundational dataset for everything from threat detection and anomaly tracking to network usage analysis and performance optimization. However, the sheer volume of DNS log data generated by large-scale infrastructures presents a significant challenge in terms of storage…

As enterprise networks scale to support millions of users and billions of transactions daily, the volume of DNS logs generated becomes staggering. These logs, which capture every DNS query and response across an organization’s infrastructure, serve as a rich source of metadata for network security, compliance auditing, traffic analysis, and threat hunting. However, the challenge…

As enterprise networks grow more complex and the threat landscape becomes increasingly sophisticated, the foundational assumption that internal traffic can be inherently trusted is rapidly becoming obsolete. This paradigm shift has led to the widespread adoption of zero-trust architectures, which dictate that no user, device, or service—regardless of its location—should be trusted by default. Within…

As organizations increasingly rely on DNS telemetry for threat detection, compliance audits, and forensic investigations, the need for accurate, consistent, and queryable historical DNS data becomes paramount. DNS logs, by their nature, are high-velocity, append-heavy datasets, making them well-suited for big data processing frameworks. However, challenges arise when attempting to maintain reliable, auditable tables of…