Kafka Monitoring Guide: Key Metrics, Consumer Lag, and Alerting
A practical Kafka monitoring guide covering key broker, producer, and consumer metrics, consumer lag, fetch latency, alerting, and the signals that keep clusters healthy.
Production patterns for AI agents, RAG pipelines, data infrastructure, and MLOps. No theory-only posts — every article comes from a real deployment.
A practical Kafka monitoring guide covering key broker, producer, and consumer metrics, consumer lag, fetch latency, alerting, and the signals that keep clusters healthy.
Master Kafka benchmarking! Explore methodologies, key metrics, & tools (Kafka Perf, Trogdor, OMBF) for peak performance analysis. Boost your Kafka cluster!
A practical Kafka performance tuning guide with 15 tips beyond the basics, covering producer, broker, consumer, and infrastructure optimization.
A practical Apache Kafka performance tuning guide covering producer settings, `buffer.memory`, broker threads, consumer tuning, and low-latency throughput tradeoffs.
A practical Kafka EOS guide covering delivery semantics, idempotent producers, transactions, `read_committed`, and how to avoid data loss or duplicate processing.
Learn how Kafka Schema Registry handles schema evolution, schema IDs, compatibility rules, Avro or Protobuf serialization, and safer producer-consumer contracts.
A practical ksqlDB tutorial and Kafka Streams guide covering `CREATE STREAM`, windowed aggregations, joins, and real-time clickstream processing.
A practical Kafka Connect guide for ingesting data from databases, files, and APIs, with source connector examples, configuration patterns, and production best practices.
Learn Apache Kafka core concepts: events, topics, partitions, brokers, producers, consumers & KRaft. Essential guide with Python examples for beginners.
Kafka producer and consumer best practices for `acks`, idempotence, retries, offsets, commits, partitioning, and error handling in production streaming systems.
Learn Kafka topic and partition strategy for scalability, consumer parallelism, ordering guarantees, throughput planning, and long-term cluster design.
Five practical ways teams still use logistic regression, and why this classic model remains valuable even in a deep learning era.