Why Read This
Today's big data challenges require innovative approaches to extreme volumes, velocities, varieties, and variabilities of data for advanced analytics — and enterprises are increasingly turning to Hadoop to address those challenges. Developed as an Apache open source project and commercialized by a growing range of vendors, Hadoop defines a massively parallel cloud environment for advanced analytics against unstructured, semistructured, and structured data types. Some early adopters have deployed Hadoop as standalone cloud analytics infrastructure, while others use it as an adjunct to a traditional enterprise data warehouse (EDW). Application development and delivery (AD&D) professionals should consider Hadoop an immature but promising technology for addressing the most data-intensive analytics and application requirements. A key caveat is that the industry has developed Hadoop primarily for advanced analytics and skimped on the transactional computing features required by the most robust enterprise applications.