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Book Description

If enabled, logging captures almost every system process, event, or message in your software or hardware. But once you have all that data, what do you do with it? This report shows you how to use log analytics—the process of gathering, correlating, and analyzing that information—to drive critical business insights and outcomes.

Drawing on real-world use cases, Matt Gillespie outlines the opportunities for log analytics and the challenges you may face—along with approaches for meeting them. Data architects and IT and infrastructure leads will learn the mechanics of log analytics and key architectural considerations for data storage. The report also offers nine key guideposts that will help you plan and design your own solutions to obtain the full value from your log data.

  • Learn the current state of log analytics and common challenges
  • See how log analytics is helping organizations achieve better business outcomes in areas such as cybersecurity, IT operations, and industrial automation
  • Explore tools for log analytics, including Splunk, the Elastic stack, and Sumo Logic
  • Understand the role storage plays in ensuring successful outcomes

Table of Contents

  1. Understanding Log Analytics at Scale
    1. Capturing the Potential of Log Data
      1. Treating Logs as Data Sources
      2. The Log Analytics Pipeline
    2. Log Analytics Use Cases
      1. Cybersecurity
      2. IT Operations
      3. Industrial Automation
    3. Tools for Log Analytics
      1. Splunk
      2. Elastic (formerly ELK) Stack
      3. Sumo Logic
    4. Topologies for Enterprise Storage Architecture
      1. DAS
      2. Virtualized Storage
      3. Physically Disaggregated Storage and Compute
    5. The Role of Object Stores for Log Data
    6. The Trade-Offs of Indexing Log Data
    7. Performance Implications of Storage Architecture
    8. Enabling Log Data’s Strategic Value with Data Hub Architecture
    9. Nine Guideposts for Log Analytics Planning
      1. Guidepost 1: What Are the Trends for Ingest Rates?
      2. Guidepost 2: How Long Does Log Data Need to be Retained?
      3. Guidepost 3: How Will Regulatory Issues Affect Log Analytics?
      4. Guidepost 4: What Data Sources and Formats Are Involved?
      5. Guidepost 5: What Role Will Changing Business Realities Have?
      6. Guidepost 6: What Are the Ongoing Query Requirements?
      7. Guidepost 7: How Are Data-Management Challenges Addressed?
      8. Guidepost 8: How Are Data Transformations Handled?
      9. Guidepost 9: What About Data Protection and High Availability?
    10. Conclusion
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