Glossary

LLM Observability is the comprehensive practice of monitoring, tracking, and analyzing the behavior, performance, and outputs of Large Language Models (LLMs) throughout their entire lifecycle from development to production. It provides real-time visibility into every layer of LLM-based systems, enabling organizations to understand not just what is happening with their AI models, but why specific behaviors occur, ensuring reliable, safe, and cost-effective AI operations.

Logstash is an open-source server-side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to your favorite "stash." As a core component of the ELK Stack (Elasticsearch, Logstash, and Kibana), Logstash serves as the data collection and log-parsing engine that helps organizations centralize, transform, and route data for analysis and storage.

Kibana is a powerful open-source data visualization and exploration platform designed to work seamlessly with Elasticsearch as part of the Elastic Stack. Originally developed by Elasticsearch (now Elastic) in 2013, Kibana has evolved into the leading solution for creating interactive dashboards, real-time data visualization, and comprehensive log analysis. As organizations increasingly generate massive volumes of structured and unstructured data across distributed systems, cloud platforms, and applications, Kibana serves as the visual interface that transforms raw Elasticsearch data into actionable insights through intuitive dashboards, advanced visualizations, and real-time monitoring capabilities that enable data-driven decision making across security, operations, business intelligence, and application performance monitoring use cases.

Filebeat is a lightweight log shipper designed to efficiently forward and centralize log data as part of the Elastic Stack ecosystem. Originally developed by Elastic, Filebeat belongs to the Beats family of data shippers and serves as a crucial component in modern log management pipelines. As organizations increasingly deploy distributed systems, microservices, and cloud-native applications that generate massive volumes of log data across multiple servers and containers, Filebeat provides a reliable, resource-efficient solution for collecting, processing, and forwarding log files to centralized destinations like Elasticsearch, Logstash, or other data processing systems. Unlike heavy-weight log collection tools, Filebeat is specifically designed to consume minimal system resources while maintaining high reliability and performance in production environments.

The ELK Stack is a powerful collection of three open-source tools—Elasticsearch, Logstash, and Kibana—designed to provide comprehensive log management, search, analysis, and visualization capabilities. Originally developed by Elastic, this stack has become the de facto standard for centralized logging, observability, and security information and event management (SIEM) across modern IT infrastructures. As organizations increasingly adopt microservices architectures, cloud-native deployments, and distributed systems, the ELK Stack provides essential capabilities for aggregating, processing, and analyzing the massive volumes of log data generated by applications, servers, and network devices to maintain operational visibility and troubleshoot complex issues.

An inverted index is a fundamental data structure used in information retrieval systems and search engines to enable fast full-text search capabilities. Unlike a regular index that maps document IDs to their content, an inverted index reverses this relationship by mapping each unique word or term to a list of documents containing that term. This "inversion" allows search engines to quickly identify which documents contain specific search terms without scanning through entire document collections. Inverted indexes are the backbone of modern search technologies, powering everything from web search engines like Google to database full-text search capabilities in systems like Apache Doris, ClickHouse, and Elasticsearch.

Semi-structured data is a form of data that sits between structured and unstructured data, containing some organizational properties without conforming to a rigid schema like traditional relational databases. This data format maintains partial organization through tags, metadata, and hierarchical structures while retaining flexibility for varied content representation. As organizations increasingly handle diverse data sources including web content, IoT device outputs, social media feeds, and API responses, semi-structured data has become fundamental to modern data management strategies. Unlike structured data that fits neatly into rows and columns, or unstructured data that lacks any organizational framework, semi-structured data provides a balance of flexibility and organization that enables efficient storage, processing, and analysis across distributed systems and cloud-native architectures.

OpenTelemetry is a 100% free and open-source observability framework designed to provide comprehensive telemetry data collection, processing, and export capabilities for modern distributed systems. Born as a merger of OpenTracing and OpenCensus projects in 2019, OpenTelemetry has become the industry standard for observability instrumentation under the Cloud Native Computing Foundation (CNCF). As organizations increasingly adopt microservices, containerized applications, and cloud-native architectures, OpenTelemetry addresses the critical need for unified observability across complex distributed systems by providing standardized APIs, SDKs, and tools for generating, collecting, and exporting traces, metrics, and logs without vendor lock-in.

Grafana is an open-source analytics and monitoring platform that provides comprehensive data visualization, dashboards, and alerting capabilities for observability across modern IT infrastructure. Originally developed by Torkel Ödegaard in 2014, Grafana has evolved into the leading solution for creating interactive dashboards that unify metrics, logs, traces, and other data sources into coherent visual narratives.

Apache Doris is an MPP-based real-time data warehouse known for its high query speed. For queries on large datasets, it returns results in sub-seconds. It supports both high-concurrency point queries and high-throughput complex analysis. It can be used for report analysis, ad-hoc queries, unified data warehouse, and data lake query acceleration. Based on Apache Doris, users can build applications for user behavior analysis, A/B testing platform, log analysis, user profile analysis, and e-commerce order analysis.

An analytics database is a specialized database management system optimized for Online Analytical Processing (OLAP), designed to handle complex queries, aggregations, and analytical workloads across large datasets. Unlike traditional transactional databases that focus on operational efficiency and data consistency, analytics databases prioritize query performance, data compression, and support for multidimensional analysis. Modern analytics databases leverage columnar storage, massively parallel processing (MPP) architectures, and vectorized execution engines to deliver sub-second response times on petabyte-scale datasets, making them essential for business intelligence, data science, and real-time decision-making applications.