TRANSACTIONAL DATA LAKES: A FRAMEWORK COMPARISON AND ANALYSIS
Keywords:
Data Analytics, Data Lake Frameworks, Data Management, Real-time Processing, Transactional Data LakesAbstract
This comprehensive article explores the evolution and implementation of transactional data lakes as a revolutionary solution to modern enterprise data management challenges. The article examines how organizations are adapting to handle exponentially growing data volumes while maintaining data integrity and enabling real-time analytics. It provides a detailed comparison of leading frameworks including Delta Lake, Apache Iceberg, and Apache Hudi, analyzing their distinct features and performance characteristics. The article also explores industry-specific applications across financial services, healthcare, e-commerce, and telecommunications sectors, highlighting how transactional data lakes are transforming operational efficiency and decision-making capabilities. Additionally, the article addresses implementation challenges, considerations for organizations adopting these technologies, and emerging trends in the field, particularly focusing on artificial intelligence integration, real-time processing capabilities, and cloud-native architectures.
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