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Using log analytics provides organizations with powerful and necessary capabilities for IT security. By analyzing log data, you can drive critical business outcomes, such as identifying security threats or opportunities to build new products. Log analytics also helps improve business efficiency, application, infrastructure, and uptime.

In the second edition of this report, data architects and IT infrastructure leads will learn how to get up to speed on log data, log analytics, and log management. Log data, the list of recorded events from software and hardware, typically includes the IP address, time of event, date of event, and more. You'll explore how proactively planned data storage and delivery extends enterprise IT capabilities critical to security analytics deployments.

  • Explore what log analytics is--and why log data is so vital
  • Learn how log analytics helps organizations achieve better business outcomes
  • Use log analytics to address specific business problems
  • Examine the current state of log analytics, including common issues
  • Make the right storage deployments for log analytics use cases
  • Understand how log analytics will evolve in the future

With this in-depth report, you'll be able to identify the points your organization needs to consider to achieve successful business outcomes from your log data.

Table of Contents

  1. 1. Log Analytics
    1. Capturing the Potential of Log Data
    2. Your Environment Has Too Many Log Sources to Count
    3. Treating Logs as Data Sources
    4. Standardizing Log Formatting
    5. The Log Analytics Pipeline
  2. 2. Log Analytics Use Cases
    1. Cybersecurity
    2. Speed Matters
    3. Identifying and Defeating Advanced Threats
    4. IT Operations
    5. Infrastructure Monitoring and Troubleshooting
    6. Industrial Automation
    7. Enabling Industry 4.0
  3. 3. Tools for Log Analytics
    1. Splunk
    2. Elastic (Formerly ELK) Stack
    3. Sumo Logic
    4. Apache Kafka
    5. Apache Spark
    6. Combining Log Analytics with Modern Developer Tools
    7. Deploying Log Analytic Tools
  4. 4. Topologies for Enterprise Storage Architecture
    1. Direct-Attached Storage (DAS)
    2. Virtualized Storage
    3. Physically Disaggregated Storage and Compute
  5. 5. The Role of Object Stores for Log Data
    1. The Trade-Offs of Indexing Log Data
  6. 6. Performance Implications of Storage Architecture
    1. Additional Considerations for Security Analytics
    2. Responding to and Detecting Threats in Real Time
    3. Analyzing the Threat Landscape
    4. Seamless Scalability
  7. 7. Enabling Log Data’s Strategic Value with a Unified Fast File and Object Platform
  8. 8. 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?
  9. 9. Conclusion
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