Advanced Data Management: Trino, Hadoop, and AWS for a Robust Lakehouse

As organizations increasingly migrate away from Apache Hadoop due to its performance limitations and architectural complexity, many are adopting modern data lakehouse architectures on platforms like Amazon Web Services (AWS) to improve scalability, cost-effectiveness, and performance. The transition from Hadoop involves leveraging AWS services such as S3 for data storage, Glue for ETL processes, and EMR for managing Hadoop infrastructures, while new tools like Apache Spark and Trino offer enhanced data processing and query capabilities. Modern file and table formats, including Parquet, Avro, Iceberg, and Delta Lake, accelerate query performance and support ACID transactions, making them well-suited for handling semi-structured and unstructured data from streaming sources. Enterprise solutions like Starburst extend the capabilities of open-source tools, providing federated data access, governance, and security features that facilitate compliance with international data regulations. Case studies illustrate how organizations like global investment banks and Israel's Bank Hapoalim have utilized these technologies to achieve efficient data management and rapid decision-making, ultimately streamlining their data architectures and enhancing their data-driven cultures.