What are the most effective patterns for streaming and real-time data engineering?

1 Lambda Architecture

Lambda architecture is a popular pattern that combines batch and stream processing to handle both historical and real-time data. In this pattern, you have three layers: the batch layer, the speed layer, and the serving layer. The batch layer stores and processes all the raw data using a batch framework, such as Hadoop or Spark. The speed layer ingests and processes the data streams using a stream framework, such as Kafka or Flink. The serving layer provides a unified view of the data by merging the results from the batch and speed layers. Lambda architecture can handle large volumes of data with high fault-tolerance and low latency, but it also introduces complexity and duplication in the data pipeline.

2 Kappa Architecture

Kappa architecture is a simpler alternative to lambda architecture that only uses stream processing to handle both historical and real-time data. In this pattern, you have two layers: the stream layer and the serving layer. The stream layer ingests and processes all the data streams using a stream framework, such as Kafka or Flink. The serving layer provides a view of the data by querying the stream layer. Kappa architecture can handle large volumes of data with high scalability and low latency, but it also requires a stream framework that can support reprocessing and state management.

3 Micro-batch Processing

Micro-batch processing is a hybrid pattern that splits the data streams into small batches and processes them using a batch framework, such as Spark or Hadoop. In this pattern, you have two layers: the micro-batch layer and the serving layer. The micro-batch layer ingests and processes the data streams in small batches using a batch framework. The serving layer provides a view of the data by querying the micro-batch layer. Micro-batch processing can handle large volumes of data with high throughput and low latency, but it also depends on the batch size and frequency to achieve the desired performance.

4 Event-driven Processing

Event-driven processing is a pattern that triggers actions or responses based on the occurrence of specific events in the data streams. In this pattern, you have two components: the event producer and the event consumer. The event producer generates and publishes the events to a message broker, such as Kafka or RabbitMQ. The event consumer subscribes to the events and performs the actions or responses based on the event type and content. Event-driven processing can handle large volumes of data with high responsiveness and flexibility, but it also requires a robust and scalable message broker and a clear definition of the event schema and logic.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?