Using Splunk for machine-generated big data

When is big data useful? How can we find relevant big data? Certainly not by having to ask someone else to answer questions but by allowing as many people as possible to explore it for themselves.
The problem with most discussions of big data is that the emphasis is on one or two steps in the entire value chain.
Big data cannot be boiled down into a one-off project or a quarterly task. It is a new capability to be mastered and incorporated in ways that make sense.
Business intelligence (BI) solutions don’t live up to their potential when you can only answer questions planned months in advance. Asking new questions requires changes to the data model and system changes by experts who become a bottleneck. Emerging technologies such as Hadoop are, well, still emerging.
Splunk supports a new approach to big data: interactive exploration (as well supporting every other phase of the big data life cycle).
Splunk customers use our software every day to gain insights across their big data deployments. Splunk offers:

  • A simple and easy-to-use search language
  • A powerful search language that supports a complicated data processing pipeline
  • Support for batch and real-time processing
  • A mature environment for application development

Splunk can support an entire end-to-end big data program and get many people involved so that you can get results quickly. That’s why you should be using Splunk for your big data.