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What is Log Analytics

What is Log Analytics?

Log files are computer-generated messages that record events, processes, and other information during operation. Log analytics is the general discipline of analyzing log files to identify patterns that can assist in troubleshooting, performance predictions, maintenance, and improvements. 

In the context of IT operations, log files are generally used to understand the processes and events that occurred during a hardware failure, security breach, or other type of incident. A log analytics tool may be used to reveal system-wide trends to help prevent future incidents from occurring.

The Benefits of Log Analytics for Your Data Storage Infrastructure

Common benefits of log analytics include:

  • Greater insight into the security and health of your storage array
  • A standardized way to troubleshoot and perform root cause analysis
  • The ability to identify storage bottlenecks to prevent future downtimes

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Why Choose Pure Storage to Power Your Log Analytics Pipeline?

Log files have to be stored somewhere safe, secure, and preferably on premises. A typical log analytics pipeline must draw unstructured data from multiple sources across your network before any valuable insights can be extracted. 

Pure Storage® FlashBlade® is an all-flash scale-out  network-attached storage (NAS) solution capable of bringing a Modern Data Experience™ to your log analytics pipeline. FlashBlade benefits include:

  • Massively parallel and scalable file and object storage
  • A simplified and unified data hub for consolidating your log files
  • Enhanced data mobility across your storage network
  • The speed and efficiency of native NVMe performance
  • Enterprise-scale time to insight with Elasticsearch®

Elasticsearch is an open-source NoSQL datastore based on the Apache Lucene search engine. Traditionally Elasticsearch would be deployed on distributed direct-attached storage (DDAS) and NAS devices. FlashBlade eliminates the need for multiple storage layers in a typical Elasticsearch workload by consolidating all data silos into a unified hub. For your log analytics pipeline, that means faster time to insight at a lower total cost of ownership (TCO).

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