Batch vs Stream processing

There are two popular methods to process data: Batch processing and Stream processing. Let’s understand the main differences between the two.

In Batch processing, we collect data over time and processes it in large chunks at set intervals. This approach shines when you need to analyze historical data, generate periodic reports, or perform large-scale data migrations. It's more cost-effective and simpler to implement since you do not need constant processing power.

Stream processing, on the other hand, handles data in real-time as it arrives. Think of monitoring live traffic or detecting fraud in banking transactions. It's ideal when immediate insights are needed. Stream processing requires more complex infrastructure and higher costs to maintain continuous processing capabilities.

Batch processing often uses tools like Apache Spark or Apache Airflow, while stream processing relies on platforms like Apache Kafka or RabbitMQ.

Akhil Singh Chauhan

Creator