Log Management Explainers:
Log Analytics: A Practical Guide
What is Log Analytics?
Log analysis is the process of collecting, parsing, and analyzing log data from various sources in order to identify patterns, trends, and issues.This can include everything from web server logs and application logs to network and security logs. The importance of log analytics lies in the fact that it allows organizations to gain valuable insights into the performance and security of their systems.
One of the main benefits of log analytics is the ability to quickly identify and troubleshoot problems within an IT environment. By analyzing log data in real-time, organizations can identify potential issues and take steps to address them before they become major problems. This can save a lot of time and resources that would otherwise be spent on manually tracking down and fixing issues.
Another key benefit of log analytics is improved security. By analyzing log data, organizations can identify potential security threats and take steps to prevent them. This can include detecting and blocking malicious activities such as hacking attempts, or identifying and addressing vulnerabilities in the IT environment.
This is part of a series of articles about log management.
How Log Analytics Works
Log analytics typically involves four main steps: data collection and classification, data storage, pattern recognition, and correlation analysis.
In the first step, data is collected from various sources within an IT environment, such as web server logs, application logs, and network logs. The next step is classification, which involves grouping the data into different categories based on certain characteristics. For three examples, data can be classified according to the type of event it represents, or the source of the data, or the time at which it was generated – ideally all of these and more.
This data is then stored in a centralized repository, such as a database or data warehouse. Once the data is stored, it can be analyzed using pattern recognition algorithms. These algorithms look for patterns and trends within the data, such as specific sequences of events or anomalies in the data. This can help to identify potential issues or opportunities within the IT environment.
Finally, the data is analyzed using correlation analysis, which involves looking for relationships and connections between different events and data points. This can help to identify cause-and-effect relationships, or to spot potential issues that may require further investigation.
What Are the Use Cases For Log Analytics?
Log analytics has many different use cases, but some of the main ones include:
- Application deployment verification: Organizations can use log analytics to verify that new applications have been deployed successfully, and to identify any issues or errors that may have occurred during the deployment process. This can help to ensure that applications are running smoothly and efficiently, and to identify any potential problems before they impact users.
- Fault isolation: Log analytics can help organizations to quickly identify and isolate faults within their IT environment. By analyzing log data in real-time, organizations can pinpoint the source of an issue and take steps to fix it, without having to manually search through large volumes of data.
- Peak performance analysis: Log analytics can be used to analyze the performance of an IT environment over time, and to identify periods of peak performance or peak usage. This can help organizations to identify potential bottlenecks or other performance issues, and to take steps to improve the overall performance of their systems.
- Forensics: Log analytics can be used for forensic purposes, such as investigating security breaches or other incidents. By analyzing log data, organizations can identify the source and scope of a security breach, and take steps to prevent similar incidents from happening in the future.
- Improved software quality: Log analytics can help organizations to improve the quality of their software. By analyzing log data, organizations can identify issues and errors in their software, and take steps to fix them before they impact users. This can help to ensure that software is running smoothly and efficiently, and to improve the overall user experience.
How To Do Log Analysis
Log analysis typically involves several steps, one of which is data cleansing. Data cleansing is the process of cleaning up and preparing log data for analysis. It involves a number of different tasks, including:
- Removing irrelevant or duplicate data: Log data often contains irrelevant or duplicate information that is not useful for analysis. Data cleansing involves identifying and removing this data to make the log data more useful and manageable.
- Normalizing the data: Log data may be generated by different systems and applications, and may be in different formats. Data cleansing involves normalizing the data, so that it is in a consistent format and can be easily analyzed.
- Transforming the data: Log data may need to be transformed in order to make it more useful for analysis. This can include tasks such as converting timestamps to a standard format, or calculating derived values such as averages or totals.
- Validating the data: Data cleansing also involves validating the log data to ensure that it is accurate and complete. This can include checking for missing values, or verifying that data values fall within certain ranges.
After the data cleansing step, the next step in log analysis is data structuring. Data structuring involves organizing the log data into a structured format that is more suitable for analysis. This can include tasks such as:
- Identifying the fields and attributes of the log data: Log data typically contains a number of different fields and attributes, such as timestamps, user IDs, and event types. Data structuring involves identifying and defining these fields and attributes, so that they can be easily analyzed. It is most efficient to do this when parsing the log at ingest, but different solutions have different methods.
- Creating a schema for the log data: Once the fields and attributes of the log data have been identified, a schema can be created to define how the data is structured. This can include defining the data types for each field, as well as any relationships or dependencies between the fields.
- Loading the data into a data store: After the data has been structured, it is typically loaded into a data store such as a database or data warehouse. This allows the data to be easily accessed and queried for analysis.
- Indexing the data: Data structuring also involves creating indexes on the log data, which can help to improve the performance of analysis queries. Indexes allow the data to be quickly and efficiently searched, sorted, and grouped based on different criteria.
After the data cleansing and data structuring steps, the next step in log analysis is data analysis. Data analysis involves using various techniques and tools to analyze the log data, in order to gain insights and identify patterns and trends. This can include tasks such as:
- Querying the data: Data analysis often involves running queries against the log data in order to extract specific subsets of data for further analysis. This can include queries that filter the data based on certain criteria, or that aggregate the data in order to calculate statistics or other summary information.
- Visualizing the data: Data analysis often involves using visualizations to represent the log data in a more intuitive and easily understandable way. This can include charts, graphs, and other types of visualizations that help to highlight key trends and patterns in the data.
- Identifying anomalies and trends: Data analysis also involves looking for anomalies and trends in the log data. This can include identifying unusual patterns or spikes in the data, or detecting changes in the data over time.
- Correlation analysis: Data analysis may also involve looking for relationships and correlations between different events and data points. This can help to identify cause-and-effect relationships, or to spot potential issues that may require further investigation.
Learn more: Read our guide to log management tools
Log Analysis Best Practices
Some important log analysis best practices include:
- Collecting log data from all relevant sources: In order to effectively analyze log data, it is important to collect logs from all relevant sources within the IT environment. This can include web server logs, application logs, network logs, and security logs, among others.
- Storing log data in a centralized repository: In order to make log data easily accessible for analysis, it is important to store it in a centralized repository such as a database or data warehouse. This allows the data to be easily queried and analyzed.
- Normalizing and structuring the log data: In order to make log data more useful for analysis, it is important to normalize and structure the data. This involves cleaning up and organizing the data, and converting it into a consistent and structured format.
- Using the right tools and techniques for analysis: In order to effectively analyze log data, it is important to use the right tools and techniques. This can include specialized log analysis software, as well as various data analysis techniques such as pattern recognition and correlation analysis.
- Regularly reviewing and updating the log analysis process: In order to ensure that log analysis is effective and efficient, it is important to regularly review and update the log analysis process. This can include identifying areas for improvement, and implementing new tools or techniques as needed.
Security Log Analytics with Exabeam
Exabeam Security Log Management represents the Exabeam entry point to ingest, parse, store, and search security data in one place, providing a fast, modern search and dashboarding experience across your security log data.
Exabeam Security Log Management delivers affordable log management at scale without requiring advanced programming, query-building skills or lengthy deployment cycles. Collectors gather data from on-premises or cloud data sources using a single interface. Log Stream parses each raw log into a security event as data travels from the source, identifies named fields, and normalizes them using a standard format (CIM) for accelerated analysis with added security context that helps map user credentials to IPs and devices.
- Multiple transport methods: API, agent, syslog, Cribl, SIEM data lake
- 381+ products in 56 product categories; including
- 34 cloud-delivered security products
- 11 SaaS productivity applications
- 21 cloud infrastructure solutions
- Combining to represent over 9,300 pre-built log parsers
An essential capability of Exabeam Security Log Management is Search, a simple drop-down wizard selection process helping even new analysts create complex queries – fast.. From Search, you can create powerful visualizations from your parsed log data quickly, building a dashboard in minutes from 14 different pre-built chart types. Or if the query is repeated often, you can share with your team or use it to create a correlation rule with automated responses via email or API.
Learn more: Exabeam for Security Log Management