REVOLUTIONIZING DATA INGESTION PIPELINES THROUGH MACHINE LEARNING: A PARADIGM SHIFT IN AUTOMATED DATA PROCESSING AND INTEGRATION

Abstract

In today’s world, companies are drowning in data, but the process of turning that raw information into something useful is still messy and slow. Traditional ways of getting data from one place to another what we call "data ingestion" often involve a lot of manual work, struggle with changing data formats, and can’t always handle the unpredictable flow of information. This is where machine learning can truly make a difference. In this paper, we look at how specific machine learning models can help make data ingestion smoother and faster. For example, supervised learning models like decision trees can automatically clean up messy data and remove duplicates without needing human oversight. Then, we have unsupervised learning techniques like clustering algorithms (think K-means) that can take data from different sources and group it together in a meaningful way, making the whole process of combining data much easier. To keep things running smoothly, anomaly detection models like isolation forests can catch unusual or incorrect data before it causes problems, ensuring that only high-quality information flows through. Recurrent neural networks (RNNs) and time-series forecasting models can even predict future data loads, allowing systems to scale up or down as needed, so there’s no waste or slowdowns during peak times. And when data structures change, transfer learning can help systems adapt automatically, without the need for constant manual adjustments. By using these machine learning tools, businesses can drastically cut down the time and effort needed to process data, allowing them to focus on what really matters: getting insights and making smarter decisions. This paper explores how these technologies can transform data ingestion into a faster, more reliable, and less hands-on process for today’s data-driven world