Introduction In distributed messaging systems, delivery semantics define how messages are guaranteed to be delivered and processed between producers and consumers. Two critical semantics are exactly-once and at-least-once, each balancing reliability, performance, and complexity in event-driven architectures. These semantics are…

Introduction to Data Pipelines Data pipelines represent structured workflows in distributed systems that facilitate the ingestion, transformation, and delivery of data from diverse sources to target destinations. These pipelines are fundamental for enabling data-driven decision-making, supporting real-time analytics, and ensuring…

Introduction In streaming systems, backpressure refers to the buildup of data when the rate of incoming events exceeds the processing capacity of downstream components, potentially leading to resource exhaustion, increased latency, or system failures. Backpressure handling is a critical mechanism…

Introduction Publish-Subscribe (Pub/Sub) systems are a cornerstone of event-driven architectures, enabling asynchronous communication between producers (who publish messages) and consumers (who subscribe to messages) through a broker. These systems decouple components, facilitating scalability, fault tolerance, and real-time data processing in…

Introduction Event-Driven Architecture (EDA) is a design paradigm in distributed systems that emphasizes the production, detection, consumption, and reaction to events—discrete occurrences or changes in system state. Unlike traditional request-response models, where components interact synchronously, EDA promotes asynchronous communication through…

Introduction JSON Web Tokens (JWTs) are a widely adopted standard for secure authentication and authorization in distributed systems. As a compact, self-contained mechanism, JWTs enable stateless, scalable, and secure communication between parties by encoding user identity and permissions in a…

Introduction In distributed systems design, data processing paradigms play a pivotal role in determining how efficiently and effectively a system handles varying workloads. Two fundamental approaches are batch processing and stream processing. Batch processing involves collecting data into groups (batches)…

Introduction Apache Kafka serves as a foundational tool in distributed systems for managing high-volume data flows with efficiency and reliability. Its architecture, centered on durable logs, partitioned topics, and scalable brokers, enables it to support two primary paradigms: event streaming,…

Introduction Apache Kafka is a distributed event streaming platform designed for high-throughput, low-latency, and fault-tolerant data processing. Its distributed architecture enables it to scale horizontally, handle failures gracefully, and maintain data consistency across multiple nodes. This detailed analysis explores Kafka’s…