Zander Matheson - Real-Time Revolution: Kickstarting Your Journey in Streaming Data | PyData Global

ByteWax is an open-source, Python-native stream processing framework built on a Rust engine that enables real-time data processing

Stream processing differs from batch processing by handling continuous, unbounded data as it arrives rather than processing defined sets of data

Real-time data processing means handling data as soon as it’s generated, with sub-second latency requirements depending on the use case

Key challenges in stream processing include:

• Handling time windows and late-arriving data
• Managing state across partitions
• Scaling processing across multiple workers
• Ensuring fault tolerance and recovery
• Maintaining data ordering

ByteWax features:

• Can run locally or scale on Kubernetes
• Supports various input sources (Kafka, WebSockets, HTTP streams)
• Provides windowing and aggregation capabilities
• Handles state management and recovery
• No vendor lock-in
• Works with the Python ecosystem

Stream processing operations include:

• Input connectors for data sources
• Map operations for transformations
• Window operators for time-based aggregations
• Fold operations for accumulating state
• Partitioning for parallel processing

Data flows in ByteWax are represented as directed acyclic graphs (DAGs) with nodes for each processing step

ByteWax can handle both streaming and batch processing scenarios, though it’s designed streaming-first

Common use cases include:

• IoT sensor data processing
• Social media analytics
• Manufacturing telemetry
• Financial market data
• Real-time monitoring

The framework supports running on various platforms from single Raspberry Pis to distributed cloud environments