Log Analytics for Distributed Microservices
Take a look at Kai Wahner's presentation from OReillySACon in London, where he dives into how to handle log analytics for your scattered, distributed microservices.
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I had two sessions at O'Reilly Software Architecture Conference in London in October 2016. It was the first OReillySACon in London, and it was a well-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, were DevOps, serverless architectures, and big data analytics.
I want to share the slides 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 the millions of mobile devices, Internet of Things, social network users, and other new emerging technologies out there. 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
Published at DZone with permission of Kai Wähner, DZone MVB. See the original article here.
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