Data Engineering in the Era of Real-Time Analytics: Tools, Techniques, and Architectural Patterns

Authors: Ritesh Kumar

DOI: https://doi.org/10.5281/zenodo.15086602

Short DOI: https://doi.org/g89tfv

Country: United States

Full-text Research PDF File:   View   |   Download

Abstract: The rapid evolution of real-time analytics has fundamentally reshaped modern data engineering practices. As organizations increasingly prioritize low-latency data processing, there is a notable shift from batch-oriented architectures to streaming-based approaches that enable continuous data ingestion, transformation, and analysis. This paper examines the key tools, techniques, and architectural patterns that facilitate real-time data processing at scale. A comparative analysis of Lambda and Kappa architectures is presented, highlighting their design principles, performance trade-offs, and implementation challenges. Additionally, the study evaluates critical data streaming frameworks, including Apache Kafka, Apache Flink, and Apache Pulsar, and their role in managing high-throughput, fault-tolerant data pipelines. The discussion extends to cloud-native data warehouses, such as Google BigQuery, Snowflake, and Amazon Redshift, emphasizing their adaptability for real-time analytics workloads. Furthermore, the paper explores essential considerations such as data consistency, fault tolerance, event-driven scalability, and AI-driven observability in real-time architectures. By leveraging data pipeline automation and AI-powered monitoring, organizations can enhance performance optimization and anomaly detection in large-scale analytics systems. Through this analysis, the paper provides a comprehensive perspective on architectural best practices, emerging challenges, and the evolving landscape of real-time data engineering.

Keywords: Real-Time Analytics, Data Engineering, Streaming Architectures, Lambda Architecture, Kappa Architecture, Apache Kafka, Apache Flink, Apache Pulsar, Cloud Data Warehousing, Google BigQuery, Snowflake, Amazon Redshift, Event-Driven Processing, Data Pipeline Automation, Fault Tolerance, AI-Driven Observability, Low-Latency Data Processing, Scalable Analytics, Distributed Systems, Stream Processing

Paper Id: 232282

Published On: 2023-08-23

Published In: Volume 11, Issue 4, July-August 2023

Share this