XpoLog
Intelligent Log Management Automation

Various software applications and systems within an organization automatically produce log files, which, in a computing context, represent the timely documentation of events associated with a particular system.

The massive volumes of log data created by these applications and information systems, if managed and utilized efficiently, proves beneficial to businesses, creating a huge positive impact in terms of health, data protection, performance, and more.

Thus, effective log management tools must offer capabilities that go beyond simply collecting, aggregating, and storing log data.

Merely indexing all the logs from an organization’s IT infrastructure that comprises servers, network systems, as well as home-grown and third-party applications, mostly results in thousands of search results for a single log query.

An effective log management tool must not only be able to provide precise search results for a specific log data record but also offer a wider perspective into events occurred throughout the entire environment across various systems and applications.

In a comprehensive interview with CIO Applications, XpoLog’s CEO, Haim Koschitzky sheds light on how the company’s log analysis platform helps organizations optimize their business applications. New York-based XpoLog offers a log analysis platform that ticks all these boxes.

XpoLog’s platform comprises a management tool to comprehensively collect, index, monitor, and analyze log data along with providing valuable insights and pinpointing errors by using machine learning.

In addition, XpoLog’s platform also features tools for viewing, parsing, and reporting logs, as well as the ability to understand specific data patterns automatically.

What led to the inception of XpoLog?

We recognized that with the constant advancements in technology and the proliferation of cloud usage, applications, and software would increasingly generate massive volumes of machine and log data.At that time, there were hardly any technologies available in the market to keep up with these large volumes of data, and hence, a solution that could collect, index, search, and automatically analyze log data was the need of the hour.

Consequently, we developed a log analysis platform to provide organizations with the best way to search information and gain insights from the log data collected, and in turn, benefit their development, operation, and application support teams.

To shorten the development lifecycles while frequently delivering updates using DevOps, these teams also require a continuous delivery of new app-based tools, software, and services, which we guarantee with our technology In the DevOps space, we offer elastic data search capabilities and partner with organizations running on private as well as public clouds including Amazon AWS, Azure, and more.

Over the years, we have significantly grown as a company, collaborating with technology delivery services and cloud providers, as well as leading global banks, government institutions, and insurance companies.

How has the log management market evolved over the years? How has XpoLog adapted to these changes?

The application management industry is constantly evolving to keep up with the continuous delivery of software and applications in the modern DevOps environment. In addition, fragmentation of the software delivery process and the availability of microservices have resulted in a higher rate of changes in the industry. As the volumes of log data generated by these applications continue to boom, organizations are also starting to utilize log management for security purposes, which includes investigating malicious use of software, performing forensics on log data, and more. In order to track configuration changes and immediately troubleshoot them, organizations need a log management solution that consolidates and analyzes data, quickly identifies problems and risks, and further mitigates them.

Although organizations use multitudes of tools to analyze logs, most of the analytical work involved is manual, which results in failure to gain valuable insights from log data and creating investigative reports of problem areas. As a result, we began applying machine learning and semantic processing to the log data, thus effectively pinpointing problems. By automatically suggesting users where those problems occur and navigating them to specific areas in their codes and log data that they need to take a lot to eliminate anomalies, we are saving programmers’ as well as analysts’ time. This automated approach toward log management to efficiently understand issues and quickly act on them benefits our clients significantly.

Could you elaborate more on the features of your technology, and how it helps organizations identify and solve problems that might negatively affect their business?

We add a layer of human intellect to our log management capabilities, which we call augmented intelligence, thus bolstering the quality and precision of the insights that an AI-based algorithmic tool provides. We can inject these insights into the search engine to provide more relevant and contextual results along with shortening the time taken to identify and fix problems. We also provide out-of-the-box solutions to automatically monitor the data streaming into a system, analyzing it, and tracing patterns to find the source of problems and mitigate them immediately.

Various large, as well as small organizations, find our technology useful in understanding and monitoring not only logs but also the data that is being migrated to the cloud. A large insurance company is currently using our log analysis platform to track changes occurring within their application, create reports, and swiftly tackle the risk areas. Several companies from the DevOps space are also relying on our technology to generate timely reports on application delivery and performance as well as to troubleshoot faster and more effectively. To help these organizations better understand log data and assist their migration to the cloud or hybrid environments, we are working on various innovations. Our objective is to ensure that both developers and application teams investigate log data thoroughly and stay ahead of the curve with advanced analytical capabilities.