Category: DNS and Big Data

The deployment of machine learning models for DNS telemetry analysis in large-scale environments demands not only high predictive accuracy but also robustness, reproducibility, and operational resilience. Whether these models are used to detect domain generation algorithms (DGAs), identify DNS tunneling attempts, classify domains by category, or monitor behavioral anomalies, ensuring that each change to a…

As DNS over HTTPS (DoH) and DNS over TLS (DoT) become increasingly prevalent across public networks and endpoint applications, enterprise environments are facing a paradigm shift in how DNS activity can be observed, controlled, and analyzed. These encrypted DNS protocols are designed to enhance user privacy and security by preventing eavesdropping and tampering of DNS…

Authoritative DNS servers are the cornerstone of internet name resolution, responsible for maintaining definitive records that map domain names to IP addresses and other critical metadata. In dynamic environments where records change frequently—due to service scaling, infrastructure updates, security reconfigurations, or TTL expirations—it is essential for operational visibility, auditing, and replication integrity to monitor changes…

As DNS becomes an increasingly integral part of security, performance, and observability tooling across large-scale networks, the data captured within DNS logs grows in both volume and sensitivity. DNS logs, while appearing innocuous at first glance, can contain inadvertent leaks of personally identifiable information (PII). This includes usernames, email addresses, device hostnames tied to individuals,…

Presto, a distributed SQL query engine designed for large-scale interactive analytics, has become a foundational tool in modern data infrastructure, especially where heterogeneous data sources and petabyte-scale telemetry intersect. In the context of DNS analysis, where log tables often reach billions of rows per day and queries must support security detection, performance monitoring, and forensic…

In the rapidly evolving landscape of big data, DNS logs represent a trove of untapped insights, offering a real-time, granular view into the network behaviors of users, applications, and systems. As organizations become increasingly reliant on network-driven services, the volume of DNS queries generated grows exponentially. Harnessing this data effectively for analytics purposes requires the…

The volume of DNS query data generated by modern networks is staggering, with enterprises and service providers handling billions of queries each day. Each of these queries—whether it’s a user visiting a website, a device checking for updates, or malware trying to contact a command-and-control server—contains critical metadata that can be used for operational insight,…

As the volume of DNS logs continues to surge in enterprise and service provider environments, the need for efficient storage and fast analytical access becomes increasingly critical. DNS logs are generated continuously, with billions of records accumulating daily in large networks. Each log entry includes multiple fields such as timestamps, query types, domain names, source…

The explosion of DNS traffic in modern digital infrastructure has brought both unprecedented visibility into internet activity and a massive analytical challenge. DNS logs are fundamentally relational—each query ties together an origin IP address, a resolver, a domain name, and often an authoritative name server. These interconnections form an implicit graph structure, and at large…

As digital networks expand and internet-connected devices proliferate, DNS has become one of the most voluminous and consistently active data sources in enterprise and cloud environments. Every web request, software update, email connection, or IoT ping typically initiates with one or more DNS queries. This results in immense data generation—DNS logs can quickly accumulate into…