Huge Demand for Log Analysis Service Industry

Huge Demand for Log Analysis Service Industry

In the management and intelligence of computer logs, log analysis (or systems and network log analysis) is an art and science that seeks to make sense of computer-generated records (also known as log or audit trail records). Creating such records is known as data logging. Logs are issued by network devices, operating systems, applications, and all types of intelligent or programmable devices. A chronological sequence of messages often includes a log. Logs can be directed to files and saved to disk or sent as a network stream to a log collector.

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Some of the key players of Log Analysis Service Industry:

Splunk, Graylog, Datadog, Microsoft, Google, Sumo Logic, IBM, Scalyr, Coralogix, Apache, SolarWinds, DTSTACK, Alibaba Cloud

Log messages usually need to be interpreted in relation to the internal status of their source (e.g. application) and announce security or operational events (e.g. a user login or a system error). Logs are often created by software developers to help debug the operation of an application or understand how users interact with a system such as a search engine. The syntax and semantics of data in log messages are usually application or manufacturer specific. The terminology can also vary. For example, a user's authentication to an application can be described as a login, login, user connection, or authentication event. Therefore, log analysis must interpret messages in the context of an application, provider, system, or configuration in order to make useful comparisons with messages from different log sources.

The format or content of log messages may not always be fully documented. One job of the protocol analyst is to get the system to return the entire message area in order to understand the entire domain from which the messages are to be interpreted. A log analyst can map different terminology from different log sources to a unified, normalized terminology so that reports and statistics can be derived from a heterogeneous environment. For example, log messages from Windows, Unix, network firewalls and databases can be combined into a "normalized" report for the auditor. Different systems can signal different message priorities with a different vocabulary, e.g. B. "Error" and "Warning" versus "Error", "Warning" and "Critical".

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