Revolutionizing Big Data: Scalable Pipelines and the Power of Data Lakehouse Architecture

Abstract

This paper studies how the Data Lakehouse architecture has the potential to change data analysis because it combines the most useful elements of data lakes and data warehouses into a single, scalable and cost-effective system. This looks at parts of the Lakehouse system, including open storage standards, extra data layers and engines that use ACID principles and points out why it is important to have scalable data pipelines. A comparison of warehouses, lakes and lake houses proves that lake houses are better equipped to handle different types of data tasks. By showing how finance, healthcare and retail use data lake houses to do complex analytics and machine learning with large data, this paper demonstrates how these systems enable organizations to avoid the common limits faced with traditional infrastructure. It also explains the tools and technologies involved in making Lakehouse work—for instance, Apache Spark, Delta Lake, Apache Airflow and Databricks and it looks at topics for further study, like live data, AI-friendly orchestration and data settings that are both safe and easy to use together. All of these insights explain how data pipelines and Lakehouse systems play an important role in the future of big data.