Designing High-Throughput Data Pipelines: A Performance-Centric Architectural Framework for Low-Latency Analytics in Distributed Cloud Environments

These days, using analytics in real-time with big datasets is vital to the design of smart systems and methods for making decisions. This work aims to provide a framework for efficient analytical processing, focusing on minimizing latency in distributed cloud environments. Because the amount of data produced by IoT devices, social media, transactional systems, and sensor networks is constantly rising, a capable and scalable system is needed to quickly handle and review this data. According to this research, existing data pipeline systems struggle with data ingestion delays, restricted use of different processors, slow database connections, and inability to use resources well. To solve these problems, we present a new architectural model that uses micro batching, asynchronous functioning, edge computing, and smart load distribution. The methodology has five layers to accomplish this, starting with data intake, processing streaming events, storage, and real-time data analytics. It is scalable and fault-tolerant with containers deployed using Kubernetes. Comparisons are made between traditional and new architectures on both real and simulated data using AWS, Azure, and GCP cloud services. Assessing a performance means looking at how fast the system works, its response time, how it uses resources, and how much it costs. The experiments show that the framework can reduce total latency by 45% and increase data throughput by 60% when measured against typical systems. This document features a thorough review of current literature, a well-structured design of the system, suggestions for building it, a look at how it performs, and directions for future research. Integrating Apache Kafka, Apache Flink, and TensorFlow Extended, the proposed framework allows businesses to build fast and agile data analytics platforms in the cloud