One of the biggest challenges with data lakes in general, and Hadoop in particular, is speed. How do you get real-time analytics performance out of a technology like Hadoop that was designed to trade off performance for scalability? While technologies like Hive, Presto, Parquet, ORC and others have delivered improvements, none of them provide near real-time, sub-second performance at scale, until Apache Druid. Druid, which is included as part of Cloudera HDP, has been widely used to deliver real-time performance for reporting and ad hoc analytics in data lake deployments.
Learn how companies have successfully accelerated Hadoop analytics using Apache Druid, and also moved towards real-time analytics using message buses like Kafka or Amazon Kinesis. This white paper explains why delivering real-time analytics on a data lake is so hard, approaches companies have taken to accelerate their data lakes, and how they used Druid with their existing technology to create end-to-end real-time analytics architectures.