Big Data Engineering Patterns for Real-Time Analytics: A Comprehensive Framework

Abstract

The modern information environment has radically changed with the accelerating expansion of high-speed data streams of Internet of Things devices, digital interactions, and financial systems. The classic batch processing processes become more and more deficient in the current enterprise demands that require the ability to make instant decisions in milliseconds after the data is generated. Although Lambda architecture is meant to provide a tradeoff between fault tolerance and low-latency processing by having separation of batch and speed layers, it induces significant complexity in operation by having two sets of codebase maintenance and code duplication. The Kappa architecture is a conceptual simplification that assumes all data are continuous streams and removes an architectural duplication of Lambda architectures, in addition to making substantial complexity and resource reduction infrastructural. Stateful computations with many operators can be executed on modern stream computing infrastructures that provide advanced state management schemes to sustain stateful computations such as temporal joins, pattern matching, and long-running aggregations that tolerate operator failure. Layered storage architectures make the distinction between hot, warm, and cold layers of storage data through the use of access patterns and latency requirements, and cloud services use advanced tiering strategies where data can be automatically moved across the storage classes. Column-based storage systems are more effective in analytical queries with a better compression ratio and lower input-output needs, and multi-level caching solutions greatly minimize unnecessary calculations. The event-driven architectures encourage loose coupling among the system components by using event production and consumption patterns that change the flow of information radically, in contrast to the normal request-response systems. The integration of integrated stream processing, efficient state management, and optimized storage architecture provisions allows organizations to satisfy their scalability requirements and enhance maintainability, resource utilization, and democratize real-time analytical performance across a wide range of organizational environments.