NetkaView Logger (NLG) vs. Dash0

Netka System is a Thailand-based leader in RegTech, OpsTech, and ESG Tech solutions, offering AI-powered platforms that enhance IT security, compliance, and operational efficiency. Its flagship product, NetkaView Logger (NLG), consolidates all technology logs into a single, centralized platform to support SIEM, SOAR, and observability workflows. By integrating threat intelligence and deep packet inspection (DPI), NLG equips organizations with the ability to detect, investigate, and mitigate threats in real time. Beyond security, the platform provides a 360° view of IT operations, enabling businesses to optimize performance, identify compliance gaps, and ensure regulatory adherence. Netka’s solutions are designed with flexibility in mind, offering both cloud-based services for scalability and appliance-based deployments for organizations needing stability and control. Compliance is a major focus, with NLG helping businesses align with Thailand’s Computer-related Crime Act 2017, PDPA regulations, and international standards like ISO/IEC 27001 and ISO/IEC 27701. The system also supports automated reporting, simplifying the process of passing audits and meeting legal obligations. With AI-driven analytics, IT teams can identify patterns, predict risks, and streamline decision-making. Trusted by enterprises across industries, Netka has established itself as a key partner for digital transformation in Southeast Asia. Ultimately, Netka empowers businesses to stay compliant, resilient, and secure in a rapidly evolving cyber landscape.

Engineering teams that adopt OpenTelemetry often hit the same wall: instrumentation is standardized, but the backend receiving it is not. Dash0 was built to close that gap. Every signal, whether a trace, a log record, a metric, or the resource emitting it, is stored against OpenTelemetry semantic conventions and correlated automatically. A request that ran long can be examined next to the log lines it produced and the pod it ran on, with no manual joins and no hopping between products. Ingestion happens through a standard OTLP endpoint. Nothing proprietary gets deployed, and existing instrumentation keeps working untouched. Because the wire format is open, data can be redirected to a different destination later without changes to application code. Prometheus users are treated as first-class. Full PromQL is supported, existing recording and alerting rules carry over, and Grafana dashboard definitions import directly. Cluster-level collection is handled by a dedicated Kubernetes operator covering workloads, nodes, and control plane components. Visualization runs on Perses, with dashboard, check, and alert definitions expressed declaratively and kept under version control. Investigations start broad and get narrow: heatmaps expose the shape of a latency distribution, then filters on high-cardinality attributes isolate the affected requests. Machine learning is applied to telemetry during processing rather than surfaced as a chatbot. Log AI assigns severity to records that arrive without it, discovers recurring patterns, and clusters similar entries, turning noisy third-party output into something queryable. For failing requests, the SIFT methodology structures the path from symptom to root cause. Consumption stays transparent throughout. Teams can identify which services, attributes, and log volumes are responsible for their bill and reduce them at the source, before the invoice arrives.