A Comprehensive Cloud Data Lakehouse Adoption Strategy for Scalable Enterprise Analytics
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V3I4P111Keywords:
Cloud Data Lakehouse, Enterprise Analytics, Cloud-Native Architecture, Machine Learning Enablement, Feature Store, MLOps, FinOps, Cost OptimizationAbstract
Cloud data lakehouse systems are becoming the heart of the new era of enterprise analytics but most organizations do not have a well-defined roadmap to adopt them at scale. The data lakehouse adoption strategy as introduced in this paper provides an overview of an all-inclusive cloud data lakehouse adoption strategy that incorporates both the architectural underpinnings, enterprise integration patterns, governance, as well as FinOps in a single actionable framework. Then position the lakehouse in the history of data warehouses and data lakes highlighting such fundamental concepts as storage-compute isolation, table formats that support the ACID standard, integrated batch and streaming pipelines, and multi-modal queries. Based on this, Suggest reference architecture, based on the use of cloud-native services to enable ingestion, metadata management, scalable analytics and machine learning. The adoption plan is conceptualized using a progressive plan called Foundation, Expansion, and Optimization in accord with business outputs, maturity of data products, and the organizational operating models. How enterprise integration, ingestion modernization and ELT-based patterns can lead to less duplication and faster time to insight, and a single governance model, fine-grained access control and IAM policies ensure security and regulatory compliance. Lastly, also describe best cost optimization including storage tiering and workload-aware autoscaling, which incorporates FinOps disciplines into everyday operational platform activities. The future work directions are presented at the end of the paper, and it proposes the avenue of empirical evaluation, making the proposed strategy a viable blueprint of businesses intending to convert the old analytics estates into scalable and cloud-native lakehouses
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