Digital transformation drives need for monitoring
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Rodeny McCarter, Newgistics VP of Infrastructure, presents The Newgistics Digital Transformation Journey. Access the full presentation recordings for GalaxZ17 here: http://ow.ly/WyBu30cakk0
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Digital transformation drives need for monitoring
- 1. Rodney McCarter Vice President of Infrastructure Engineering and Operations
- 3. 3 Newgistics is… a seamless fusion of digital commerce and physical logistics With Newgistics, commerce is effortless.
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- 5. 5 We drive growth By efficiently connecting the digital and physical worlds.
- 6. 6 So, how does digital commerce and logistics relate to Zenoss? To answer that question, let’s discuss our digital transformation to… 1. Deploy a scalable application architecture 2. Increase value to customers with big data 3. Go from idea to build to deploy faster 4. Run IT like a business 5. Have an highly-available and cost effective infrastructure And use that to drive the business case for a monitoring platform.
- 7. 7 Application Architecture: Microservices & Containers Microservices • Select the best tools/technology for the job • Less expensive to change • Quicker, more frequent releases • Improved resilience • Scale • Elimination of the spaghetti coded monolith Microservice Benefits • Flexibility • Elasticity/Scalability • Efficiency • Portability • Consistency • Security Container Benefits • Evolution of Software Fortress Architecture • Autonomous • Based on real-world business domains • Technology choices can vary between services • Technology agnostic interfaces • REST APIs • Queues • Services typically have their own data stores Return Product Service Service
- 8. 8 Making the Business Case for Monitoring… Can monitoring be customized to my unique application environment? How do I determine the relationship between the various components of the architecture?
- 9. 9 Big Data Architecture 15 Minutes Later… Elastic Compute Capability ContinuousLoad(replicate) Data Warehouse (“Refined Data”) Continuous • Cleanse • Transform • Validate • Aggregate Data Lake (“Raw Data”) External Data Sources Partner Feeds Internal Applications Data Consumption / BI Structured Data Other Data
- 10. 10 Making the Business Case for Monitoring… Can monitoring be customized to my unique application environment? How do I determine the relationship between the various components of the architecture? What bottlenecks are slowing down the processing of data and thus the accuracy of data reports and visualizations?
- 11. 11 Continuous Integration / Deployment via DevOps
- 12. 12 Making the Business Case for Monitoring… Can monitoring be customized to my unique application environment? How do I determine the relationship between the various components of the architecture? What bottlenecks are slowing down the processing of data and thus the accuracy of data reports and visualizations? How do I automatically know when new releases are deployed?
- 13. 13 Mature IT Operations Change & Release Processes Knowledge Management Metrics Tracking & Reporting
- 14. 14 Making the Business Case for Monitoring… Can monitoring be customized to my unique application environment? How do I determine the relationship between the various components of the architecture? What bottlenecks are slowing down the processing of data and thus the accuracy of data reports and visualizations? How do I automatically know when new releases are deployed? How can service tickets be automatically created when issues occur? How can I measure, track, and report on uptime and service level agreements? How do I get to root cause faster?
- 15. Infrastructure Internal environment is highly available. Transition appropriate workloads to the cloud. Leverage SaaS providers for non- differentiated services. Basic Rules • Understand the requirements • Know the business goals • Examine the constraints
- 16. 16 Making the Business Case for Monitoring… Can monitoring be customized to my unique application environment? How do I determine the relationship between the various components of the architecture? What bottlenecks are slowing down the processing of data and thus the accuracy of data reports? How do I automatically know when new releases are deployed? How can service tickets be automatically created when issues occur? How can I measure, track, and report on uptime and service level agreements? How do I get to root cause faster? How do we maintain visibility in a highly elastic and dynamic environment? Where do I have faults or weaknesses that will impact service availability?
- 17. 17 And that’s why we chose Zenoss. How do we sift through our ocean of alerts? How do I automatically generate service tickets from events? How can I involve the right teams in a timely manner to address issues? Where are the opportunities to scale up or down? How can I improve uptime and meet client SLAs? CUSTOMER & BUSINESS SERVICES How can I execute faster to match desired cadence? ALERTS & ISSUE RESOLUTIONINFRASTRUCTURE MANAGEMENT OPERATIONS & MAINTENANCE How can I automatically monitor new releases and updates? When will my equipment reach performance capacity? What assets are most likely to put my services at risk? How do I increase transparency across my IT environment? How should we monitor newer technologies? How can I track changes in my dynamic environment?
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Editor's Notes
- Microservices Microservices are small, autonomous services that work together Domain boundaries are based on real-world business domains Technology choices can vary between services Consumed through technology agnostic interfaces REST – Synchronous communications Queues – Asynchronous communication They have their own data stores Data can be extracted or pushed to a reporting Data Warehouse Microservice benefits Allows each service to choose the best tools/technology for the job Makes change less expensive Quicker, more frequent releases Less downtime and improved resilience through isolation of failure Can scale individual services Reduction/elimination of the spaghetti coded monolith that evolves due to boundary erosion and tight coupling Containers Wrap a piece of software with everything needed to run: code, runtime, tools, system libraries. Lightweight – multiple (potentially hundreds/thousands of) containers run under a single OS instance with namespace isolation Designed to start up Fast Requires strong Devops including Container Orchestration (Kubernetes, Swarm, Mesos) Container Benefits Flexibility - can run in multiple cloud environments Elasticity/Scalability - Spin up or down capabilities in microseconds Efficiency – Increase server consolidation/efficiency Portability – Greater application portability Consistency – Container description is consistent between environments Security – Better security through isolation (debatable)
- NetHAWK Cloud is a high availability, highly scalable data processing architecture is critical for our plans over the next 10 to 15 years. SCALE: If you consider that we have terabytes of data today, and that we are only processing 20 to 25 events per package, imagine what happens as the Internet of Things makes sensor technology cheap, allowing us to capture package detail using GPS capabilities every step of the way as they move through our networks. Imagine the insights this can provide into network bottlenecks and capacity constraints, and the real-time ability this enables to sense and respond to conditions on the ground. Imagine also that moving from 25 events per package to thousands of events per package means leapfrogging “petabyte scale” and potentially jumping directly to “Exabyte scale”. Close the COMPETITIVE GAP: NetHAWK Cloud will help us close the gap between our capabilities and where some of our competitors (e.g. Amazon) are already at.
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