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Storm Applied

  • Course Code: Big Data - Storm Applied
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 2 Days Audience: This course is geared for those who wants to use Apache Storm for the real-world tasks associated with processing and analyzing real-time data streams

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Storm Applied skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to use Apache Storm for the real-world tasks associated with processing and analyzing real-time data streams 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Storm Applied is an example-driven guide to processing and analyzing real-time data streams. This immediately useful course starts by teaching you how to design Storm solutions the right way. Then, it quickly dives into real-world case studies that show you how to scale a high-throughput stream processor, ensure smooth operation within a production cluster, and more. Along the way, you’ll learn to use Trident for stateful stream processing, along with other tools from the Storm ecosystem. 

Working in a hands-on learning environment, led by our Apache Storm expert instructor, students will learn about and explore: 

you learn how to think about designing Storm solutions the right way from day one.  

But it quickly dives into real-world case studies that will bring the novice up to speed with productionizing Storm. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Mapping real problems to Storm components 
  • Performance tuning and scaling 
  • Practical troubleshooting and debugging 
  • Exactly-once processing with Trident 

Audience & Pre-Requisites 

This course is geared for attendees who want to build a solid foundation of Storm essentials. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills.  
  • While prior experience with Storm is not assumed 
  • some experience with big data and real-time systems is helpful. 

Course Agenda / Topics 

  1. Introducing Stormfree 
  • What is big data? 
  • How Storm fits into the big data picture 
  • Why you’d want to use Storm 
  1. Core Storm conceptsfree 
  • Problem definition: GitHub commit count dashboard 
  • Basic Storm concepts 
  • Implementing a GitHub commit count dashboard in Storm 
  1. Topology design 
  • Approaching topology design 
  • Problem definition: a social heat map 
  • Precepts for mapping the solution to Storm 
  • Initial implementation of the design 
  • Scaling the topology 
  • Topology design paradigms 
  1. Creating robust topologies 
  • Requirements for reliability 
  • Problem definition: a credit card authorization system 
  • Basic implementation of the bolts 
  • Guaranteed message processing 
  • Replay semantics 
  1. Moving from local to remote topologiesfree 
  • The Storm cluster 
  • Fail-fast philosophy for fault tolerance within a Storm cluster 
  • Installing a Storm cluster 
  • Getting your topology to run on a Storm cluster 
  • The Storm UI and its role in the Storm cluster 
  1. Tuning in Storm 
  • Problem definition: Daily Deals! reborn 
  • Initial implementation 
  • Tuning: I wanna go fast 
  • Latency: when external systems take their time 
  • Storm’s metrics-collecting API 
  1. Resource contention 
  • Changing the number of worker processes running on a worker node 
  • Changing the amount of memory allocated to worker processes (JVMs) 
  • Figuring out which worker nodes/processes a topology is executing on 
  • Contention for worker processes in a Storm cluster 
  • Memory contention within a worker process (JVM) 
  • Memory contention on a worker node 
  • Worker node CPU contention 
  • Worker node I/O contention 
  1. Storm internals 
  • The commit count topology revisited 
  • Diving into the details of an executor 
  • Routing and tasks 
  • Knowing when Storm’s internal queues overflow 
  • Addressing internal Storm buffers overflowing 
  • Tweaking buffer sizes for performance gain 
  1. Trident 
  • What is Trident? 
  • Kafka and its role with Trident 
  • Problem definition: Internet radio 
  • Implementing the internet radio design as a Trident topology 
  • Accessing the persisted counts through DRPC 
  • Mapping Trident operations to Storm primitives 
  • Scaling a Trident topology 
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