Modernizing Legacy Big Data Platforms Using Cloud-Native Lakehouse Architectures

Abstract

The capacity to scale up the infrastructure, intricacy of the infrastructure, utilization of resources, governance of data, and compatibility with new analytics are just a few issues with the old models of the Hadoop-based big data systems of the past. This research paper suggests the implementation of cloud-based lakehouse architecture to upgrade the outdated big data systems implemented on Hadoop that has been integrated into the existing data and analytics without affecting the existing data resources. The suggested solution will result in a scaled and expandable data management space in cloud object storing methods, Apache Spark, Databricks, distributed data processing, an open table format, and centralized governance. A measured mode of modernization, which includes an assessment of the workload, data migration, transformation of the layers of processing, the addition of metadata, governance, and optimization of the workload, is planned. It also allows the storage and compute capabilities to be independently scaled as well as having the ability of running batch processing, streaming analytics, machine learning, and business intelligence on the same platform. The framework adds controls on data quality and metadata, security policies, lineage Garnier, cost-sensitive deployment of resources, to the functionality of the traditional Hadoop deployments. Architectural considerations show that the proposed lakehouse model has the ability to ease the management of the infrastructure, enhance resource elasticity, reduce platform fragmentation, and allow various analytical workloads. The paper demonstrates how the ideas of the lakehouse that are cloud native provide an organized guideline to organizations that are planning on upgrading their past big data infrastructures without sacrificing the interoperability, governance and operational continuity.