Created on 11-15-2018 08:05 PM - edited 08-17-2019 05:49 AM
Objective of this article is to demonstrate how to rapidly deploy a demo Druid & LLAP cluster preloaded with 20 years (nearly 113 million records) of airline data ready for analytics using CloudBreak on any IaaS. Entire deployment is UI driven without the need for a large overhead of administration. All artifacts mentioned in this article are publicly available for reuse to try on your own
Time series is an incredible capability highly leveraged within the IoT space. Current solution sets offer non scalable & expensive or distributed processing engines lacking low latency OLAP speeds. Druid is an OLAP time series engine backed by a Lambda architecture. Druid out of the box SQL capabilities are severely limited and without join support. Layering HiveQL over Druid brings the best of both worlds. Hive 3 also offers HiveQL over Kafka essentially making Hive a true SQL federation engine. With Druid’s native integration with Kafka, streaming data from Kafka directly into Druid while executing real time SQL queries via HiveQL offers a comprehensive Time Series solution for the IoT space.
To begin the demonstration, launch a CloudBreak deployer instance on any IaaS or on prem VM. Quick start makes this super simple. Launching CloudBreak deployer is well documented here.
Once the CloudBreak deployer is up, add your Azure, AWS, GCP, or OpenStack credentials within the CloudBreak UI. This will allow deployment of the same cluster on any IaaS.
Here I demonstrated how to rapidly launch a Druid/LLAP cluster preloaded with airline data using CloudBreak. Enjoy Druid, it's crazy fast. HiveQL makes Druid easy to work with. CloudBreak makes the deployment super quick.