Building a Modern Data Lake for Enterprise Analytics

  • Authors

    • Yuga Sai Kiran Reddy Sudini Director Of Engineering and operations at Public Partnerships LLC, USA. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2024PII7P2X

    Published 09-10-2024

  • Enterprise Data Lake, Enterprise Analytics, Databricks, Power BI, Data Integration, Data Engineering, Big Data Analytics, Data Governance, Data Quality, Data Transformation, Business Intelligence, Data Visualization, Scalable Analytics, Cloud Data Platform

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

  • Abstract

    Enterprises increasingly depend on data-driven decision-making, yet fragmented data sources, heterogeneous formats, inconsistent data structures, and limited accessibility can significantly reduce the value of organizational data. Modern data lake architectures provide a scalable approach for consolidating structured, semi-structured, and unstructured data while supporting advanced analytics and business intelligence. This paper presents a modern enterprise data lake ecosystem integrating Databricks and Power BI to establish a unified platform for data ingestion, transformation, analytics, visualization, and reporting. The proposed architecture enables data from diverse enterprise sources to be ingested into a centralized data environment, processed through scalable data engineering workflows, and transformed into reliable analytical datasets. Databricks provides distributed data processing, transformation, and analytical capabilities, while Power BI enables interactive dashboards, visualization, and business-oriented reporting. The framework incorporates data integration, data quality management, governance, scalable processing, and semantic modeling to improve the consistency and accessibility of enterprise information. By establishing an integrated analytics ecosystem, the proposed approach reduces data fragmentation and supports more efficient information discovery, reporting, and analytical decision-making. The study highlights how cloud-oriented data lake architectures can provide a flexible and scalable foundation for enterprise analytics while improving data availability, processing efficiency, and organizational insight generation.

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