The delivery of real-time query makes Hadoop accessible to more users — and by orders of magnitude. Its significance goes well beyond delivering a database management system (DBMS) kind of query engine that other products have had for decades. Rather, Hadoop as a platform now supports a whole new paradigm of analytics. Real-time query is the catalyst for delivering a new level of self-service in analytics to a much broader audience. Interactive response and the accessibility of a structured query language (SQL) interface through open database connectivity/Java database connectivity (ODBC/JDBC) make the incremental discovery and enrichment of data possible for a greater and more varied audience of users than just data scientists. Hadoop can now reach an even wider array of users who are familiar with business intelligence tools such as Tableau and MicroStrategy. That incremental discovery and enrichment process has two other major implications. First, it dramatically shortens the time between collecting data from source applications and extracting some signal from that data’s background noise. Second, it becomes a self-enforcing exercise in crowdsourcing the process of refining meaning from the data. Both issues had previously represented major bottlenecks in the exploitation of traditional data warehouses.