5 रिपॉजिटरी
Integrations specifically for queuing outgoing messages through Apache Kafka for non-blocking delivery.
Distinct from Message Queue Integrations: Distinct from Message Queue Integrations: specifically targets Kafka-based queuing for message delivery, not general message broker connectivity.
Explore 5 awesome GitHub repositories matching data & databases · Kafka. Refine with filters or upvote what's useful.
goim
Queues outgoing messages through Kafka for non-blocking delivery, fan-out, and aggregated room delivery.
JavaTutorial is a specialized knowledge base and set of study guides focused on backend engineering, the Java ecosystem, distributed systems, and database internals. It serves as a technical reference for engineers, providing structured learning paths and curated content designed for Java backend developer interview preparation. The resource distinguishes itself through deep-dive analyses of internal mechanics, including JVM memory management, garbage collection algorithms, and the internal architecture of the Spring Framework. It provides detailed studies on database internals specifically f
Covers configuration of cluster operations and broker internals to implement reliable messaging queues.
Materialize is a streaming SQL database that continuously ingests live data from sources such as Kafka, Redpanda, PostgreSQL, and MySQL, and incrementally maintains materialized views. It provides a PostgreSQL-compatible query engine that accepts standard SQL over the PostgreSQL wire protocol, enabling any existing SQL client or BI tool to query real-time data. The system also includes a Model Context Protocol (MCP) server that exposes live materialized view data to AI agents, providing fresh context without polling. Materialize distinguishes itself through its ability to offer configurable c
Configures starting offsets for Kafka topic consumption in streaming SQL sources.
Kafkacat is a suite of command-line utilities for interacting with Apache Kafka clusters. It provides a non-JVM binary for producing and consuming messages, inspecting cluster metadata, and debugging the Kafka protocol via the terminal. The tool functions as a producer and consumer capable of pushing data from files or standard input and reading messages from specific topics and partitions. It includes a metadata inspector to retrieve cluster state and partition configurations in plain text or JSON, as well as a protocol debugger for inspecting message offsets, timestamps, and binary payloads
Allows querying specific message positions by timestamp to consume data from a precise point in time.
Burrow is a monitoring and alerting service for Kafka clusters. It tracks consumer group lag by evaluating committed offsets and identifying the gap between produced and consumed messages. The system calculates processing delays using a sliding-window offset evaluation and monitors overall cluster health. When consumer group conditions meet predefined thresholds, it triggers notifications to external services via email or webhooks. The service provides HTTP endpoints for querying broker information and consumer group status, allowing external tools to poll for system health and performance m
Fetches current offset positions directly from Kafka brokers to determine the gap between production and consumption.