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Streaming in Data Engineering

Overview

Streaming enables real‑time processing of data as it arrives, allowing organizations to react instantly to events, anomalies, and user interactions. Unlike batch processing, streaming pipelines handle unbounded datasets with low latency.

Common Architectures

  • Publish‑Subscribe (Kafka, Pulsar)
  • Lambda Architecture (batch + speed layer)
  • Kappa Architecture (single stream processing layer)
  • Event‑Sourcing & CQRS

Tools & Frameworks

CategoryTechnology
Message BrokersApache Kafka, Azure Event Hubs, RabbitMQ
Stream ProcessingApache Flink, Spark Structured Streaming, Azure Stream Analytics, Kafka Streams
StorageDelta Lake, Apache Iceberg, Azure Data Lake Storage

Best Practices

  1. Design for idempotency and exactly‑once semantics.
  2. Implement graceful back‑pressure handling.
  3. Use schema evolution (Avro/Protobuf) for forward compatibility.
  4. Monitor latency, throughput, and error rates continuously.
  5. Separate concerns: ingestion, processing, and storage.

Code Sample (Kafka + Flink)

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.kafka.common.serialization.StringDeserializer;
import java.util.Properties;

public class StreamingJob {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        Properties props = new Properties();
        props.setProperty("bootstrap.servers", "localhost:9092");
        props.setProperty("group.id", "flink-consumer");
        FlinkKafkaConsumer<String> consumer = new FlinkKafkaConsumer<>(
            "events",
            new org.apache.flink.streaming.util.serialization.SimpleStringSchema(),
            props);
        consumer.setStartFromLatest();
        env.addSource(consumer)
           .map(value -> value.toUpperCase())
           .print();
        env.execute("Kafka to Flink Streaming");
    }
}

Run the job and observe real‑time transformations of incoming Kafka messages.

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