Why we need Hadoop alternatives

Apache Hadoop, initially developed by Yahoo! engineers to manage large-scale data cost-effectively, has become limited in addressing the demands of modern enterprise data due to its early-2000s design, which includes challenges such as high-latency processing and the small file problem. While Hadoop was revolutionary in introducing distributed storage and processing using modules like HDFS and MapReduce, its complexities and limitations have led to the development of alternatives for more flexible, scalable data solutions. Cloud-based services such as Amazon EMR and Microsoft Azure’s HDInsight provide Hadoop capabilities without on-premises constraints, yet they don't fully overcome Hadoop's inherent issues. Solutions like Apache Spark and Apache Flink offer in-memory data processing and support for non-HDFS sources, addressing some of Hadoop's shortcomings. Trino, a modern distributed SQL query engine, represents a significant shift by supporting massively parallel processing and enabling SQL queries across a federated data architecture, integrating seamlessly with cloud storage solutions and providing a more comprehensive data management approach. Starburst’s implementation of Trino enhances this capability, allowing organizations to unify diverse data sources into a single access point using SQL, effectively transforming enterprise data architectures beyond being mere Hadoop alternatives into open data lakehouses that integrate seamlessly with existing business intelligence tools.