Become an analytics and Big Data Specialist and Data Scientist every company wants. Learn both Hadoop and Big Data technologies
This training is jointly organized by BITM & Business Accelerate BD Ltd.
Training will be held in Business Accelerate BD Ltd.
COURSE CURRICULUM
Section: 1
Introduction.
1. Introduction
Section: 2
Big Data at a Glance
2. What is data?
3. What is Big Data?
4. Data Sources of Big Data part 1
5. Data Sources of Big Data part 2
6. Traditional Analytics vs Big Data Analytics.
7. Big Data Customers-Many Industrial Domains.
Section: 3
Big Data Attributes Challenges.
8. Volume.
9. Variety.
10. Velocity.
11. Veracity.
Section: 4
Getting Started with Hadoop.
12. Hadoop History.
13. Hadoop Concepts.
14. Hadoop Ecosystem.
15. Hadoop Core Components.
16. Hadoop Distributions.
Section: 5
HDFS architecture and Concepts.
17. HDFS-Blocks-File Splits.
18. HDFS Write Operation.
19. HDFS-Hadoop-2.x-Architecture-part 1.
20. HDFS-Hadoop-2.x-Architecture-part 2.
Section: 6
Understanding MapReduce.
21. MapReduce Components.
22. Understand MapReduce Flow.
23. Client Communication.
24. NEED OF YARN.
25. HDFS Architecture.
26. NodeManager
27. Hadoop Cluster Modes.
28. Secondary Namenode.
Session: 7
Programming Hadoop with Hive and Hbase.
29. Understanding Hive
30. Understanding Hbase
31. Using relational data stores with Hadoop
32. Using non-relational data stores with Hadoop
Session: 8
Programming Hadoop with Pig
33. Understanding Pig
34. Programming Hadoop with Pig
Session: 9
Programming Hadoop with Spark
35. Spark Basics
36. Spark Libraries
37. Spark Streaming
38. Using Spark
39. Analyzing Streams of Data
Session:10
Understanding Hadoop Libraries and Workflows and Connectors
40. Introducing Oozie
41. Building a workflow with Oozie
42. Introducing Sqoop
43. Importing data with Sqoop
44. Introducing Flume
45. Introducing ZooKeeper
46. Using ZooKeeper to coordinate workflows
47. Introducing Impala
48. Using Impala
49. Introducing Mahout
50. Introducing Storm
Session: 11
Data Visualization and Real world System
51. Visualizing Hadoop Output with Tools
52. Designing Real-World Systems
Section: 12
Hadoop Installation and Configuration.
53. Hadoop 2.7.3 Installation-part1
54. Hadoop 2.7.3 Installation-part2
55. Hadoop 2.7.3 Configuration Files.
56. Hadoop Basic Commands.
Section: 13
Project 1: Customer Support Analysis Using MapReduce
57. Overview
58. Implementation of Scenario -1 in MapReduce
59. Implementation of Scenario -1 in MapReduce -Continuous...
60. Implementation of Scenario -2 in MapReduce
61. Implementation of Scenario -3 in MapReduce
Section: 14
Project 2- Video Data Analysis Using MapReduce.
62. Problem Statement.
63. Understanding Problem Statement
64. Data Preparation and Understanding
65. Basics of Big Data
66. MapReduce
67. MapReduce-Programming To Process the Scenario for Analysis part-2
68. MapReduce-Top 10 Highest Rated Videos
69. MapReduce-Top 10 Most Viewed Videos
70. MapReduce-How many people Age less 18 years uploaded Videos
71. MapReduce-Different Scenarios for Videos Analysis
Theory | Mastering Hadoop and related tools with real-time data processing using Spark,Pig,Hive,Impala | 19 Hrs |
Practical | Customer Support Analysis Using MapReduce, Video Data Analysis Using MapReduce. | 20 Hrs |
Project #1 | Customer Complaints Analysis | 3 Hrs |
Project #2 | Analyze Loan Dataset | 3 Hrs |
Project #3 | Enhance Customer Experience Management | 3 Hrs |