Advanced Techniques in Data Ingestion and Pipelining for Scalable Big Data Platforms: A Comprehensive Review

Abstract

In the era of big data, effective data ingestion and pipelining are critical for organizations to harness the power of vast and diverse data sources, such as IoT devices, social media, and transactional systems. Data ingestion refers to the process of systematically collecting and importing data for processing, while data pipelining ensures the continuous flow of this data through various transformation stages, preparing it for analysis. As data volumes grow, platforms like Hadoop, with its HDFS (Hadoop Distributed File System) and MapReduce, have become essential in managing large-scale datasets by providing distributed storage and parallel processing capabilities. Additionally, tools such as Apache Kafka and Apache Flume facilitate both batch and real-time data ingestion, ensuring flexibility in handling different types of data streams. However, challenges remain, especially with the increasing complexity of datasets and the growing need for real-time processing. Emerging technologies such as cloud-native ingestion frameworks, edge computing, and AI-enhanced pipelines offer solutions to these challenges by improving scalability, performance, and cost efficiency. Machine learning is also playing an evolving role in optimizing pipeline workflows, enabling more intelligent and adaptive data processing. This paper reviews key technologies for data ingestion and pipelining, discusses their limitations, and explores cutting-edge trends aimed at enhancing the efficiency of big data platforms. These advancements promise to reshape how organizations handle big data, offering improved agility and operational effectiveness. In addition to established technologies, the rise of cloud-native architectures and AI-driven solutions is revolutionizing data ingestion and pipelining. Cloud platforms offer scalable, on-demand resources, while AI and machine learning enhance automation, anomaly detection, and predictive analytics within pipelines.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 9 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.