IT systems and applications generate Terabytes and more of log data due to mobile devices, Internet of Things, social network user, and others. This session compares different frameworks and solutions for operational intelligence and log analytics, such as Splunk, TIBCO LogLogic and the open source “ELK stack” (ElasticSearch, Logstash, Kibana), and how these differ from Hadoop. A live demo will demonstrate how to analyze a transaction running through different applications such as a Java EE, integration middleware and analytics processes.
Target Audience: Architects, Developers, Project Leader, Decision Makers
Prerequisites: Basic knowledge in data processing, logging and integration helps
You will learn:
- Learn the different between log analytics and other big data processing tools such as Hadoop.
- Learn different open source frameworks and commercial tools for log analytics.
- Get a feeling of these tools by seeing a live demo analyzing logs of different applications.
IT systems and applications generate more and more 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 network and server monitoring/troubleshooting, security analysis, custom application monitoring and debugging, compliance standards, and others.
This session discusses how to solve the challenges of analyzing Terabytes and more of different log data to leverage the “digital business” – a term defined by Gartner and others to explain that IT is not just a tool to enable a business, but IT is the business.
The main part of the session compares different solutions for operational intelligence and log analytics to create “digital business”, such as Splunk, TIBCO LogLogic and the open source “ELK stack” (ElasticSearch, Logstash, Kibana).
A common use case will be demonstrated in a live demo: Monitoring, analyzing and correlating a complex E-Commerce transaction running through different custom applications such as a Java EE web application, an integration middleware and analytics processes.
The end of the session explains the distinction of the discussed solutions to Apache Hadoop, and how they can complement each other in a big data architecture.
Big Data is the rage in the marketplace with lots of potential to solve a myriad of customer and corporate issues. With this opportunity comes a new set of issues around security and privacy. The more data we have and want to use means more potential for the bad guys to attempt to harvest that same data for criminal activity. In this session, you will discover the latest strategies for monitoring Big Data usage and implementing controls to ensure your sensitive data is not exploited.
Target Audience: Big Data and Security audience
Prerequisites: Some knowledge of Big Data
You will learn:
- Learn about security & Privacy in Big Data
- Discover the latest strategies for monitoring Big Data usage
- Learn how to implement controls to ensure your data is not exploited