Log analytics analyzes log data from various sources to understand and improve the performance and security of application and infrastructure environments.

Log analytics use cases include:

1. Centralized log aggregation: Organizations collect and aggregate all of their logs from disparate systems and tools into a single location. With centralized logging tools, organizations can improve operational efficiency by eliminating potential data silos and duplicative IT tools, instead relying on cloud principles to offer increased scalability and accessibility.

2. Identifying and troubleshooting technical issues: Log data helps identify the root cause of technical issues, such as server crashes or network outages.

3. Monitoring system performance: Log data can monitor system performance, such as CPU and memory usage, and identify potential issues before they become critical.

4. Security and threat detection: Log data helps identify security threats, such as malware infections or unauthorized access attempts.

5. Auditing and compliance: Log data helps organizations meet regulatory and compliance requirements by providing a record of activities to audit.

6. Customer experience optimization: Log data records how customers interact with an organization’s products or services, and identify opportunities for improvement.

7. Business intelligence and data analysis: Log data provides insights into business operations and to make data-driven decisions.

Log management and log analytics are related, but they refer to different aspects of working with log data. Log management refers to the processes and tools used to collect, store and manage log data.

Log analytics refers to the process of analyzing log data to extract insights and generate useful information. The goal of log analytics is to use log data to improve the efficiency and effectiveness of an organization, identify and troubleshoot problems, and monitor the health and performance of systems.

Benefits to using log analytics include:

1. Improved performance: By analyzing log data organizations can identify and resolve technical issues faster, improving overall system performance.

2. Enhanced security: Log analytics help organizations detect security threats and breaches, allowing them to take action to prevent or mitigate these events.

3. Better decision-making: Log data offers valuable insights into business operations and customer behavior, enabling organizations to make data-driven decisions.

4. Compliance: Log analytics help organizations meet regulatory and compliance requirements by providing a record of activities to be audited.

5. Cost savings: Identifying and resolving technical issues quickly helps organizations reduce downtime and minimize resolution costs.

6. Improved customer experience: Log data helps understand how customers interact with your products or services and identifies opportunities for improvement

Sumo Logic has three different types of data collectors:

1. Hosted Collectors reside in the cloud for seamless connection to cloud services.

2. Installed Collectors are installed on a local machine.

3. OpenTelemetry Distribution is an installed agent for all data built entirely with opentelemetry-collector-builder, providing a single unified agent to send logs, metrics, traces and metadata to Sumo Logic.

DevSecOps teams need to collect wide swaths of data across the user experience and threat environments. By logging and analyzing both security and observability data, you can better detect and remediate a host of problems, such as performance issues, vulnerabilities, and security breaches, resulting in a higher-quality experience.

With Sumo Logic you can collect logs using hundreds of out-of-the-box integrations, including:

1. Cloud providers AWS, Azure and GCP

2. Containers, such as Kubernetes and Docker

3. Database servers Oracle, MongoDB, etc.

4. Web-based servers like Apache and NGINX

5. Security applications, including Okta and Zscaler

6. Productivity tools, such as Salesforce, Jira and Zoom