Stream Processing

Apache Pulsar and RocksDB logos on a dark background representing exactly-once stateful streaming.

Implementing Exactly-Once Stateful Streaming with Apache Pulsar and RocksDB

A deep dive into combining Apache Pulsar’s messaging backbone with RocksDB’s embedded state engine to achieve exactly-once semantics in stateful stream processing.

The Overlooked ETL Topic: Handling Late Arriving Data in Event-Driven Pipelines

Late data is inevitable in event-driven systems. This post shows practical patterns—watermarks, allowed lateness, and log-based reprocessing—to keep your ETL pipelines accurate and reliable.

Data flowing through a unified pipeline architecture

Mastering Apache Beam for Unified Batch and Stream Processing

Apache Beam offers a powerful unified programming model for batch and stream processing. This post explores its core concepts, architecture, and practical implementation patterns.

Diagram of Apache Flink checkpoint flow between job managers and task managers

Architecting Exactly-Once Semantics with Apache Flink Checkpoints

A deep dive into Flink’s checkpointing mechanism, fault-tolerant state management, and practical patterns for building reliable streaming pipelines.

Apache Pulsar Functions architecture diagram showing data flow

Mastering Apache Pulsar Functions for Event-Driven Microservices at Scale

Discover how Apache Pulsar Functions enable developers to process streaming data and build event-driven microservices at scale without the overhead of external frameworks.