Real-Time vs Batch Data Processing
Data processing architectures can be broadly categorised as batch (processing data in large groups at scheduled intervals) or real-time/streaming (processing data as it arrives, continuously). Choosing the right approach depends on your latency requirements, data volumes, and business use cases.
Batch Processing
Batch processing runs at scheduled intervals — hourly, daily, or weekly. Data accumulates, then is processed as a batch. Characteristics:
- Simpler to implement and debug — process is deterministic and repeatable
- Higher latency — data is only processed at the next scheduled run
- Efficient for large volumes — batch jobs can use compute resources optimally
- Suitable for: overnight report generation, daily data warehouse refreshes, monthly billing runs, bulk email sends
Real-Time / Streaming Processing
Streaming architectures process data continuously as events occur. Technologies: Apache Kafka, AWS Kinesis, Google Pub/Sub, Apache Flink, Spark Streaming. Characteristics:
- Low latency — data processed within seconds or milliseconds
- More complex infrastructure and programming model
- Suitable for: fraud detection, live dashboards, real-time notifications, personalisation, IoT data processing
The Lambda Architecture
Many systems combine both approaches — the Lambda architecture processes data in real-time for immediate results (speed layer) and in batch for accurate historical analysis (batch layer). Complexity has led to the Kappa architecture (streaming only) gaining popularity for systems that can tolerate streaming semantics.
Our Recommendation
Start with batch processing unless you have specific real-time requirements. Batch is simpler, cheaper, and easier to debug. Add real-time streaming where business requirements genuinely demand low latency.