The business value of data degrades rapidly with time. A fraud signal detected in real-time can prevent a transaction; detected the next day, it's just evidence for a claims process. A customer behavior signal processed in real-time enables personalization; processed overnight, it's historical curiosity. This reality is driving a massive shift from batch to real-time analytics architectures.
The Lambda and Kappa Architectures
Two architectural patterns dominate real-time analytics design. Lambda architecture separates batch and streaming processing layers, combining them in a serving layer. It provides correctness and low latency but at the cost of operational complexity — two codebases to maintain, two systems to operate. Kappa architecture simplifies this by using a single streaming system for all processing, reprocessing historical data when logic changes.
Technology Building Blocks
Modern real-time analytics stacks typically include Apache Kafka or Pulsar for event streaming, Apache Flink or Spark Streaming for stream processing, Apache Pinot, Druid, or ClickHouse for real-time OLAP querying, and a feature store (Feast, Tecton) for ML feature serving. The right combination depends on latency requirements, query complexity, and team expertise.