What are the common patterns in real-time data processing?

Explore common patterns in real-time data processing and their use cases in modern data architecture.
Last updated
May 2, 2024

What are the Common Patterns in Real-time Data Processing?

Real-time data processing involves various patterns, each with unique characteristics and use cases. These patterns are designed to handle the continuous flow of data and provide immediate or near-real-time insights. Let's explore some of the most common ones.

1. Lambda Architecture

Lambda Architecture splits processing into two layers: the speed layer for real-time processing and the batch layer for comprehensive, accurate background processing. Data is ingested into both layers and processed separately, then combined in the serving layer to provide a complete view.

2. Kappa Architecture

Kappa Architecture is a simplified version of Lambda, where the batch layer is eliminated. All data flows through a single processing layer (real-time stream processing), and the output is continually updated. This reduces complexity but requires strong streaming capabilities.

3. Streaming Data Integration

Streaming Data Integration involves continuously capturing changes from data sources (like databases) and immediately streaming them to downstream systems. It is ideal for real-time analytics and operational dashboards where up-to-date data is critical for decision-making.

4. Complex Event Processing (CEP)

CEP uses pattern detection to identify complex sequences of events within multiple streams of event data. It allows real-time monitoring and responding to business situations as they occur.

5. Microservices-based Streaming

This approach uses a set of small, independent services that communicate through well-defined APIs. Each microservice can independently scale and process streams, leading to flexible deployments and resilience.

6. Event Sourcing

Event Sourcing stores events as a sequence of time-ordered, immutable logs, which can be replayed to reach the current state. This allows for historical state reconstruction, audit, and consistency.

7. Stream Processing Frameworks

Stream Processing Frameworks like Apache Kafka Streams, Apache Flink, or Apache Storm are used to process and manage continuous data streams. They are applicable for real-time data manipulation, including filtering, aggregating, and enriching data before it is stored or used in applications.

8. Stream-to-stream

Stream-to-stream is a type of real-time data processing that generates and processes input and output data streams continuously. It is often used in applications that require constant data flow and immediate processing.

9. Batch-to-stream

Batch-to-stream involves the conversion of any data accumulated (batch) in a data repository into a structure that can be processed in real time. This pattern is common in scenarios where batch data needs to be transformed for real-time analysis or reporting.

10. Polyglot Persistence

Polyglot Persistence involves using multiple data storage technologies to handle different data types within a system. For example, a relational database might be used for structured data, while a NoSQL database handles unstructured or semi-structured data.

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