Data quality isn't just a technical issue: It impacts an organization's compliance, operational efficiency, and customer satisfaction.
A discussion on Generative AI: Join industry experts as they talk about how GenAI has transformed the software development space.
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 accelerated Hadoop analytics using Druid, and also moved towards real-time analytics using message buses like Kafka or Amazon Kinesis. This 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.
