Introduction to Log Analytics
With technologies such as Machine Learning and Deep Neural Networks (DNN), these technologies employ next-generation server infrastructure that spans immense Windows and Linux cluster environments. This article shows that log analytics plays a major role in managing real-time and log data. Additionally, for DNNs, these application stacks don’t only involve traditional system resources (CPUs, Memory) but also graphics processing units (GPUs). With a nontraditional infrastructure environment, the Microsoft Research Operations team needed a highly flexible, scalable, and Windows and Linux-compatible service to troubleshoot and determine causes across the full stack.
Log Analytics supports log search through billions of records, Streaming Analytics Stack metric collection, and rich custom visualizations across numerous sources. These out-of-the-box features, paired with the flexibility of available data sources, made Log Analytics a great option to produce visibility & insights by correlating across DNN clusters & components. The relevance of the log file can differ from one person to another. It may be possible that the particular log data can be beneficial for one user but irrelevant for another user. Therefore, log data can be lost inside the large cluster.
What is Log Data?
Before discussing the analytics of the log file, we should understand the log file. The log is data produced automatically by the system and stores the information about the events occurring inside the operating system. It stores the data in every period. The log data can be presented as a pivot table or file. The records are arranged according to the log file or table time. Every software application and system produces log files. Some examples of log files are transaction logs, event logs, audit logs, server logs, etc. Logs are usually application-specific. Therefore, log analytics is a much-needed task to extract valuable information from the log file.
| Log Name | Log Data Source | Information within the Log Data |
| Transaction Log | Database Management System | It consists of information about the uncommitted transactions, changes made by the rollback operations, and the changes not updated in the database. It is performed to retain the ACID (Atomicity, Consistency, Isolation, Durability) property at the time of crashes |
| Message Log | Internet Relay Chat (IRC) and Instant Messaging (IM) | In the case of IRC, it consists of server messages during the time interval the user is being connected to the channel. On the other hand, to enable the privacy of the user IM allows storing the messages in encrypted form as a message log. These logs require a password to decrypt and view. |
| Syslog | Network Devices include web servers, routers, switches, printers, etc. | Syslog messages provide the information by where, when, and why, i.e., address, Timestamp, and the log message. It contains two bits: facility (source of the message) and security (degree of the importance of the log message) |
| Server Log File | Web Servers | It is created automatically and contains information about the user in the form of three stages the IP-Address of the remote server, the timestamp and the document requested by the user |
| Audit Logs | Hadoop Distributed File System (HDFS) ANN Apache Spark. | It will record all the HDFS access activities taking place with the Hadoop platform |
| Daemon Logs | Docker | It details the interaction between containers, the Docker service, and the host machine. By combining these interactions, the cycle of the containers and disruption within the Docker service could be identified. |
| Pods | Kubernetes | It is a container collection that shares resources such as a single IP address and shared volumes. |
| Amazon CloudWatch Logs | Amazon Web Services (AWS) | It is used to monitor applications and systems using log data, examining their errors. It is also used to store and access the system's log data. |
| Swift Logs | OpenStack | These logs are sent to Syslog and managed by log level. They are used for monitoring the cluster, auditing records, extracting robust information about the server, and much more. |
What is the History of Log Analytics?
Since we started producing computer-generated records, we've been attempting to analyse them in mass. Devices, programmes, networks, and other entities emit recordings, which are then time-sequenced into logs. The need for log analytics is further supported by the fact that these logs are frequently not adequately documented or uniformly created across apps or devices.
Importance of Log Analytics
Indexing and crawling are two essential aspects. If the content does not include indexing and crawling, then the data update will not occur properly within time, and the chance of duplicate values will increase. However, with the use of log analytics, it will be possible to examine the issues of crawling and indexing data. This can be performed by analysing the time taken by Google to crawl the data and at what location Google is spending a significant amount of time.
In the case of large websites, it becomes difficult for the team to maintain a record of changes made on the site. Log Analytics allows updated changes to be retained over a regular period, thus helping to determine the quality of the website.
From a business point of view, Google's frequent crawling of the website is essential as it points towards the value of the products or services. Log analytics make it possible to examine how often Google views the page site. The changes made on the page site should be updated quickly to maintain the freshness of the content. Log Analytics can also determine this. Acquiring the accurate informative data automatically and measuring the system's security level.
Why do Organizations need Log Analytics?
Search Query Language
All datasets are thoroughly analysed with powerful log queries, which speed up threat detection and performance debugging. Discover the "unknowns" and provide your users with the tools to swiftly filter real-time insights and outcomes with an extensive operator library and intuitive search templates. Click here to know the relationship between SQL, NoSQL and NewSQL
Advanced Analytics
Machine learning is used in thorough monitoring and alerting to identify risks and solve issues with performance faster. Patented features like Log Compare, Log Reduce, Outlier Detection, and flexible query language make it easier to immediately identify the root cause of a security or operations problem. Click here to learn How to scale up Big Data Strategies with Advanced Analytics.
Complete Visibility
Unified logs, events, metrics, and traces make it easier and faster to interpret vast amounts of data. Pre-configured or out-of-the-box dashboards save time by making all of the stack's components visible. Special features like partitions and scheduled views allow users to gain visibility from the relevant dataset.
Real-time Insights
Log data can be visualized using rich data visualization on standard or custom dashboards. Machine learning-driven threat detection, integrated threat intelligence correlation, and deep search-based investigation can provide profound performance and security insights.
How to do Log Analytics?
The steps for the processing of Log Analytics are described below -- Collection and Cleaning of data
- Structuring of Data
- Analytics of Data
Data Cleansing
Firstly, Log data is collected from various sources. The collected information should be precise and informative, as the type of data received can affect performance. Therefore, the information should be collected from real users. Each type of Log contains a different kind of information.
After the collection of data, the data is represented in the form of a Relational Database Management System (RDMS). Each record is assigned a unique primary key, and the Entity-Relationship model is developed to interpret the conceptual schema of the data. Once the log data is arranged correctly, cleaning the data has to be performed. This is because there can be the possibility of corrupted log data. The reasons for the corruption of log data are given below -
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Crashing of the disk where log data is stored
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Applications are terminated abnormally
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Disturbance in the configuration of input/output
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Presence of the virus in the system and much more
Data Structuring
Log data is large as well as complex. Therefore, the presentation of log data directly affects their ability to correlate with the other data. An important aspect is that the log data can directly connect to the different log data so that the team members can interpret a deep understanding of the log data. The steps implemented for the structuring of log data are given below -
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Clarity about the usage of collected log data
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The same assets involve the data, so the log data values are consistent. This means that naming conventions can be used
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Correlation between the objects is automatically created due to the presence of nested files in the log data. It’s better to avoid nested files in the log data.
Data Analytics
The next step is to analyze the structured form of log data. This can be performed using various methods, such as Pattern Recognition, Normalization, Machine Learning Classification, Correlation Analytics, and more. Click here to learn about using Data Intelligence to Revolutionize Customer Experience.
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Knowledge Discovery and Data Mining
The volume of data in today's generation is increasing daily. Because of these circumstances, there is a great need to extract useful information from large data sets that can be used to make decisions. Knowledge Discovery and Data Mining are two distinct terms used to solve this problem.
Knowledge Discovery is a process for extracting useful information from the database. Data Mining is an algorithm for extracting patterns from data. Knowledge Discovery involves various steps such as Data Cleaning, Data Integration, Data Selection, Data Transformation, Data Mining, Pattern Evaluation, and Knowledge Presentation.
Knowledge discovery is a process that focuses on driving helpful information from the database, interpreting the data storage mechanism, implementing optimum algorithms, and visualizing results. This process emphasizes finding understandable patterns of evidence that can be used to grasp helpful information. Data Mining involves the extraction of patterns and fitting of the model.
The concept behind fitting the model is to ensure what information is inferred from its processing. It works on three aspects: model representation, estimation, and search. Some of the standard Data Mining techniques are Classification, Regression, and Clustering. 
Log Data Mining
After performing log analytics, the next step is to perform log mining. Log Mining is a technique that uses Data Mining to analyse logs. With the introduction of the data mining technique for log analytics, the quality of log data analytics has increased. This way, the analytics approach moves towards software and automated analytic systems. But, there are a few challenges to performing log analytics using data mining. These are -
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The daily volume of log data is increasing from megabytes to gigabytes or even petabytes. Therefore, advanced tools for log analytics are needed.
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The log data lacks essential information, so more efforts are needed to extract valuable data.
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Different log numbers are analyzed from various sources to delve deep into the knowledge. So, logs in various formats have to be analyzed.
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The presence of different logs creates the problem of data redundancy without identification. This leads to the question of synchronization between the sources of log data.
As shown in Fig, the process of log mining consists of three phases. Firstly, log data is collected from various sources like Syslog, Message log, etc. After receiving the log data, it is aggregated together using Log Collector. After aggregation, the second phase is started. In this, data cleaning is performed by removing irrelevant or corrupted data that can affect the accuracy of the process. After cleaning, log data is represented in the structured form of data (Integrated form) so that queries can be executed on them. After that, the transformation process is performed to convert into the required format for performing normalization and pattern analytics. Functional patterns are obtained by performing Pattern analytics. Various data mining techniques, such as Association Rules, Clustering, etc., are used to grasp the relevant information from the patterns. This information is used for decision-making and for alerting the organization of unusual behaviour in the design.
How do you design a Log Analytics Plan?
Your log analytics approach must consider the following: data import, transformation, and enrichment; indexing and sharding strategy; infrastructure design; and, ultimately, data lifecycle and archiving. The general procedures are as follows:
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You must first recognize data intake or motions. You need to determine an intake pathway.
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You must secondly configure data transformation for log lines or strings. Log analytics frequently deal with JSON; thus, the data must be suitably transformed and enhanced.
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Decide on an indexing and shards technique in the third place. Indexes must be created correctly.
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You must undertake some infrastructure planning to determine the type of instance you need and how many of them.
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Finally, you need a comprehensive data life cycle and archiving strategy to manage log size and cost.
Log Analytics Use Cases
Business Intelligence
Log data enables data-driven decision-making and provides insights into business processes.
Detecting and Troubleshooting Technical Problems
Log data can be used to find the root cause of technological problems, such as server breakdowns or network outages.
Security and Threat Intelligence
Security issues like malware infections or unauthorized access attempts can be found using log data.
Monitoring System Performance
Log data can be used to monitor CPU and memory consumption and spot possible problems before they become serious.
Protection and threat recognition.
Centralized Log Aggregation
Businesses gather and consolidate their logs from various systems and tools in one place. By removing potential data silos and redundant IT tools and relying instead on cloud principles to provide enhanced scalability and accessibility, organizations can increase operational efficiency.
Auditing and Compliance
Log data gives organizations a record of activities to audit, which aids in meeting regulatory and compliance obligations.
Customer Experience Enhancement
Log data keeps track of consumer interactions with a company's goods or services and identifies areas for improvement.
Intelligent Big data analytics for empowering enterprises to discover deep, quicker and actionable insights to improve operational efficiency. Checkout our Big Data Analytics Services and Solutions
How Can XenonStack Help You?
XenonStack Data Science Solutions provides a Platform for Data Scientists and Researchers to Build and deploy Machine Learning and Deep Learning Algorithms at a scale with automated On-Premises and Hybrid Cloud Infrastructure. Get Real-Time Insights into Machine Data with XenonStack Analytics Services, which monitors, aggregates, indexes, and analyzes all the log data from your infrastructure. Collect and correlate the data from multiple sources with brilliant Analytics using Machine Learning and Deep Learning.
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