This guide explains Elasticsearch cluster architecture, write and read workflows, bulk and async ingestion, data streams, high availability via shard allocation awareness and cross-cluster replication, data skew detection and remediation, and performance tuning for writes, reads, and cluster configuration.
This article explains the origins, concepts, and use‑cases of Event Stream Processing (ESP) and Complex Event Processing (CEP), contrasting their handling of ordered event streams versus unordered event clouds, and discusses how both technologies have evolved and are applied in modern real‑time analytics.
Data streams, akin to endless rivers, enable continuous, real-time processing of diverse sources such as IoT telemetry, web logs, and e-commerce events, offering advantages over batch processing, while presenting challenges like scalability and fault tolerance, and are supported by tools like Kinesis, Kafka, Flink, and Storm.
This article explains what data streams are, how they differ from arrays, the types of streams in Node.js, demonstrates Java Stream operations, and introduces popular streaming platforms like Apache Kafka and Amazon Kinesis, highlighting their core features and real‑time processing capabilities.
This article explains the Unix philosophy of keeping things simple, detailing how tools should do one job well and how data streams, redirection, and pipelines combine commands to create powerful yet straightforward command‑line workflows.
