⚡ Lambda & Kappa Architecture

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Overview

Lambda and Kappa architectures solve the challenge of processing both batch and streaming data for real-time analytics.

Lambda Architecture

Data Flow: Batch + Speed + Serving Layers

Batch Layer: Historical data processing (Spark, MapReduce)
Speed Layer: Real-time stream processing (Storm, Flink)
Serving Layer: Merge batch + real-time views (HBase, Cassandra)

Components

  • Data Ingestion: Kafka, Kinesis, Event Hubs
  • Batch Processing: Spark, Hadoop MapReduce
  • Stream Processing: Storm, Flink, Spark Streaming
  • Storage: HDFS, S3, HBase, Cassandra
  • Query Layer: Druid, ElasticSearch

Patterns

  • Dual Processing batch + stream
  • Immutable Data append-only
  • View Merging combine results
  • Fault Tolerance recompute from source

Kappa Architecture

Data Flow: Stream-Only Processing

Stream Processing: All data treated as streams (Kafka + Flink)
Reprocessing: Replay historical data as streams
Storage: Stream-native storage (Kafka, Pulsar)

Components

  • Stream Platform: Kafka, Pulsar, Kinesis
  • Stream Processing: Flink, Kafka Streams
  • State Management: RocksDB, Redis
  • Storage: Kafka (log retention), S3
  • Query Layer: Materialized views

Patterns

  • Stream-First everything is a stream
  • Replay Capability reprocess history
  • Single Codebase unified logic
  • Event Sourcing immutable events

Architecture Comparison

Aspect Lambda Architecture Kappa Architecture
Complexity High (dual systems) Lower (single system)
Latency Mixed (batch + real-time) Consistent low latency
Throughput High (batch optimized) Good (stream optimized)
Reprocessing Batch layer handles Stream replay required
Code Maintenance Dual codebases Single codebase
Best For Mixed workloads Stream-native use cases

Use Cases

Lambda Architecture

  • E-commerce: Product recommendations (batch ML + real-time clicks)
  • Finance: Risk analytics (historical + live trading)
  • IoT: Sensor analytics (batch trends + real-time alerts)

Kappa Architecture

  • Social Media: Real-time feed generation
  • Gaming: Live leaderboards and events
  • Fraud Detection: Real-time transaction scoring

Modern Evolution

Current Trends
  • Unified Engines: Spark Structured Streaming, Flink SQL
  • Table Formats: Delta Lake, Iceberg enable unified batch/stream
  • Cloud Native: Managed services reduce operational complexity
  • Event-Driven: Microservices adopt Kappa patterns

Implementation Considerations

Lambda Challenges
  • ⚠️ Dual codebase maintenance
  • ⚠️ Data consistency between layers
  • ⚠️ Complex operational overhead
Kappa Challenges
  • ⚠️ Stream processing complexity
  • ⚠️ State management at scale
  • ⚠️ Replay performance for large datasets
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