Comparison Of Log Analytics for Distributed Microservices – Open Source Frameworks, SaaS and Enterprise Products

Log Analytics is the right framework or tool to monitor for Distributed Microservices. Comparison of Open source, SaaS and Enteprrise Products. Plus relation to big data components such as Apache Hadoop / Spark.

I had two sessions at O’Reilly Software Architecture Conference in London in October 2016. It is the first #OReillySACon in London. A very good organized conference with plenty of great speakers and sessions. I can really recommend this conference and its siblings in other cities such as San Francisco or New York if you want to learn about good software architectures and new concepts, best practices and technologies. Some of the hot topics this year besides microservices are DevOps, serverless architectures and big data analytics.

I want to share the slide of my session about comparing open source frameworks, SaaS and Enterprise products regarding log analytics for distributed microservices:

Monitoring Distributed Microservices with Log Analytics

IT systems and applications generate more and more distributed machine data due to millions of mobile devices, Internet of Things, social network users, and other new emerging technologies. However, organizations experience challenges when monitoring and managing their IT systems and technology infrastructure. They struggle with distributed Microservices and Cloud architectures, custom application monitoring and debugging, network and server monitoring / troubleshooting, security analysis, compliance standards, and others.

This session discusses how to solve the challenges of monitoring and analyzing Terabytes and more of different distributed machine data to leverage the “digital business”. The main part of the session compares different open source frameworks and SaaS cloud solutions for Log Management and operational intelligence, such as Graylog , the “ELK stack”, Papertrail, Splunk or TIBCO LogLogic). A live demo will demonstrate how to monitor and analyze distributed Microservices and sensor data from the “Internet of Things”.

The session also explains the distinction of the discussed solutions to other big data components such as Apache Hadoop, Data Warehouse or Machine Learning and its application to real time processing, and how they can complement each other in a big data architecture.

The session concludes with an outlook to the new, advanced concept of IT Operations Analytics (ITOA).

Slide Deck from O’Reilly Software Architecture Conference

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