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Learn By Example : Apache Storm

What if Twitter didn’t exist? What if when you searched a query on Google, it took hours or days to return results? Well, this is how old computers worked, a request would require running through all...

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Course Description

What if Twitter didn’t exist? What if when you searched a query on Google, it took hours or days to return results? Well, this is how old computers worked, a request would require running through all possible options, before it would return the best possible result. But, with the advancements in technology has reduced this number from days and hours to seconds. Apache Storm is a real-time proce...

What if Twitter didn’t exist? What if when you searched a query on Google, it took hours or days to return results? Well, this is how old computers worked, a request would require running through all possible options, before it would return the best possible result. But, with the advancements in technology has reduced this number from days and hours to seconds. Apache Storm is a real-time processing software that manages to do just that. It can process through data to find a particular trend or similar words in the queries. Storm allows developers to build powerful applications that are highly responsive and can find trends between topics on twitter, monitoring spikes in payment failures, and so on.

What you’ll learn
  • Building blocks of Storm Topology, including Spouts and Bolts
  • Running a Storm Topology in the local remote mode
  • Parallelizing data processing within a topology using different grouping strategies: Shuffle grouping, fields grouping, Direct grouping, All grouping, Custom Grouping
  • Managing reliability and fault-tolerance within Spouts and Bolts
  • Performing complex transformations on the fly using the Trident topology: Map, Filter, Windowing and Partitioning operations
  • Applying ML algorithms on the fly using libraries like Trident-ML and Storm-R.

Covering Topics

1
Section 1 : Start Here
2
Section 2 : Stream Processing with Storm
3
Section 3 : Implementing a Hello World Topology
4
Section 4 : Processing Data using Files
5
Section 5 : Running a Topology in the Remote Mode
6
Section 6 : Adding Parallelism to a Storm Topology
7
Section 7 : Building a Word Count Topology
8
Section 8 : Remote Procedure Calls Using Storm
9
Section 9 : Managing Reliability of Topologies
10
Section 10 : Integrating Storm with Different Sources/Sinks
11
Section 11 : Using the Storm Multilang Protocol
12
Section 12 : Complex Transformations using Trident

Curriculum

      Section 1 : Start Here
1
You This Course and Us
      Section 2 : Stream Processing with Storm
2
How does Twitter compute Trends? Preview
3
Improving Performance using Distributed Processing
4
Building blocks of Storm Topologies Preview
5
Adding Parallelism in a Storm Topology
6
Components of a Storm Cluster
      Section 3 : Implementing a Hello World Topology
7
A Simple Hello World Topology
8
Ex 1: Implementing a Spout Preview
9
Ex 1: Implementing a Bolt
10
Ex 1: Submitting the Topology
      Section 4 : Processing Data using Files
11
Ex 2: Reading Data from a File
12
Representing Data using Tuples Preview
13
Ex 3: Accessing data from Tuples
14
Ex 4: Writing Data to a File
      Section 5 : Running a Topology in the Remote Mode
15
Setting up a Storm Cluster
16
Ex 5: Submitting a topology to the Storm Cluster
      Section 6 : Adding Parallelism to a Storm Topology
17
Ex 6 : Shuffle Grouping
18
Ex 7: Fields Grouping
19
Ex 8: All Grouping
20
Ex 9: Custom Grouping
21
Ex 10: Direct Grouping
      Section 7 : Building a Word Count Topology
22
Ex 11: Building a Word Count Topology
      Section 8 : Remote Procedure Calls Using Storm
23
Ex 12: A Storm Topology for DRPC calls
      Section 9 : Managing Reliability of Topologies
24
Ex 13: Managing Failures in Spouts
      Section 10 : Integrating Storm with Different Sources/Sinks
25
Ex 14: Implementing a Twitter Spout
26
Ex 15: Using a HDFS Bolt
      Section 11 : Using the Storm Multilang Protocol
27
Ex 16: Building a Storm Topology using Python
      Section 12 : Complex Transformations using Trident
28
Ex 17: Building a basic Trident Topology
29
Ex 18: Implementing a Map Function
30
Ex 19: Implementing a Filter Function
31
Ex 20: Aggregating data
32
Ex 21: Understanding States
33
Ex 22: Windowing operations
34
Ex 23: Joining data streams
35
Ex 24: Building a Twitter Hashtag Extractor

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