Analyzing Log Data with Spark and Looker
Learn how to set up a modern pipeline that collects, processes, and analyzes high-volume, machine-generated data. This on-demand webinar discusses popular collection mechanisms, does a hands-on log-parsing example in Spark, and shows how to use Looker to get insights from event data.
Machines are constantly generating data. Unlocking the value of log files has historically lived in the realm of batch processing. However, emerging technologies have dramatically reduced the latency of event pipelines and have improved interactive analysis and querying.
In this webinar, Scott Hoover, Looker Data Scientist, and Daniel Mintz, Chief Data Evangelist at Looker, discuss the specifics of setting up a modern pipeline that collects, processes, and analyzes high-volume, machine-generated data.
Topics covered include:
- Popular collection mechanisms
- Hands-on log-parsing example in Spark
- How to utilize Looker to glean insights from event data
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