Cluster computing frameworks such as Hadoop or Spark are tremendously beneficial in processing and deriving insights from data. However, long query latencies make these frameworks sub-optimal choices to power interactive applications. Organizations frequently rely on dedicated query layers, such as relational databases and key/value stores, for faster query latencies, but these technologies suffer many drawbacks for analytic use cases. In this session, we discuss using Druid for analytics and why the architecture is well suited to power analytic applications.
User-facing applications are replacing traditional reporting interfaces as the preferred means for organizations to derive value from their datasets. In order to provide an interactive user experience, user interactions with analytic applications must complete in an order of milliseconds. To meet these needs, organizations often struggle with selecting a proper serving layer. Many serving layers are selected because of their general popularity without understanding the possible architecture limitations.
Druid is an analytics data store designed for analytic (OLAP) queries on event data. It draws inspiration from Google’s Dremel, Google’s PowerDrill, and search infrastructure. Many enterprises are switching to Druid for analytics, and we will cover why the technology is a good fit for its intended use cases.