• Lesson.No : 17
  • 00:01:17

  • 4.5. Apache Zookeeper Hands-On - Getting Started

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Course Lessons

  1. 1- 1. Course Overview
  2. 2- 2.1. Big Data Introduction Data Variety
  3. 3- 2.2. Big Data Introduction Distributed Systems
  4. 4- 2.3. Big Data Introduction What is Big Data?
  5. 5- 2.4. Big Data Introduction Why Do We Need Big Data Now?
  6. 6- 2.5 Big Data Introduction Big Data Applications - Recommendations
  7. 7- 2.6 Big Data Introduction Big Data Applications - A/B Testing
  8. 8- 2.7 Big Data Introduction Big Data Customers
  9. 9- 2.8. Big Data Introduction Big Data Solutions
  10. 10- 2.9. Big Data Introduction What Is Apache Hadoop?
  11. 11- 2.11. Big Data Introduction Overview Of Apache Hadoop Ecosystem
  12. 12- 3.1. Lab Overview Introduction
  13. 13- 4.1. Apache Zookeeper Race Condition
  14. 14- 4.2. Apache Zookeeper Deadlock
  15. 15- 4.3. Apache Zookeeper Coordination
  16. 16- 4.4. Apache Zookeeper Introduction
  17. 17- 4.5. Apache Zookeeper Hands-On - Getting Started
  18. 18- 4.6. Apache Zookeeper Data Model
  19. 19- 4.7. Apache Zookeeper Znode Types
  20. 20- 4.8. Apache Zookeeper Hands-On - Znodes
  21. 21- 4.8.1. Ambari Zookeeper - Establishing Server Connection
  22. 22- 4.9. Apache Zookeeper Architecture
  23. 23- 4.11. Apache Zookeeper Election & Majority
  24. 24- 4.12. Apache Zookeeper Sessions
  25. 25- 4.13. Apache Zookeeper Application
  26. 26- 5.1. HDFS Why HDFS?
  27. 27- 5.2. HDFS NameNode & DataNodes
  28. 28- 5.3. HDFS Design & Limitations
  29. 29- 5.4. HDFS Replication
  30. 30- 5.5. HDFS File Reading
  31. 31- 5.6. HDFS Namenode Backup & Failover
  32. 32- 5.7. HDFS Hands-On with Ambari
  33. 33- 5.8. HDFS Hands-On with Hue
  34. 34- 6.1. YARN Why YARN?
  35. 35- 6.2. YARN Evolution From MapReduce 1.0
  36. 36- 6.3. YARN Architecture
  37. 37- 7.1. Mapreduce Basics Understanding Sorting
  38. 38- 7.2. Mapreduce Basics Overview
  39. 39- 7.3. Mapreduce Basics Thinking In MR - Programatic & SQL
  40. 40- 7.4. Mapreduce Basics Thinking In MR - Unix Pipeline
  41. 41- 7.5. Mapreduce Basics Thinking In MR - External Sort
  42. 42- 8.1. Mapreduce Programming Writing MapReduce Code Using Java
  43. 43- 8.2. Mapreduce Programming Building MapReduce Project Using Apache Ant
  44. 44- 8.3. Mapreduce Programming Writing MapReduce Code Using Eclipse
  45. 45- 8.4. Mapreduce Programming Writing MapReduce Code Using Eclipse (Windows)
  46. 46- 8.5. Mapreduce Programming Run MapReduce Jobs Using Hadoop Streaming
  47. 47- 9.1. Pig Introduction
  48. 48- 9.2. Pig Execution Modes
  49. 49- 9.3. Pig Data Types
  50. 50- 9.4. Pig Relational Operators - Load, Store And Dump
  51. 51- 9.5. Pig Lazy Evaluation
  52. 52- 10.1. Hive Introduction
  53. 53- 10.2. Hive Data Types
  54. 54- 10.3. Hive Getting Started - Hands-On
  55. 55- 10.4. Hive Tables
  56. 56- 10.5. Hive Managed Tables - Hands-On
  57. 57- 10.6. Hive External Tables - Hands On
  58. 58- 10.7. Hive Select And Aggregation Queries
  59. 59- 10.8. Hive Saving Data
  60. 60- 10.9. Hive DDL - Alter Table
  61. 61- 11.1. Sqoop Introduction
  62. 62- 11.2. Sqoop Sqoop Import - MySQL To HDFS
  63. 63- 11.3. Sqoop Sqoop Import - MySQL To Hive
  64. 64- 12.1. Flume Introduction
  65. 65- 12.2. Flume Agents
  66. 66- 12.3. Flume Sources & Delivery Reliability
  67. 67- 12.4. Flume Hands-On Demo On CloudxLab
  68. 68- 12.5. Flume Summary
  69. 69- 13.1. Oozie Introduction
  70. 70- 13.2. Oozie Running Sqoop Action Using Oozie From Hue
  71. 71- 14.1. NoSQL Scaling Out / Up
  72. 72- 14.2. NoSQL ACID Properties And RDBMS Story
  73. 73- 14.3. NoSQL Types Of NoSQL Stores
  74. 74- 14.4. NoSQL CAP Theorem
  75. 75- 15.1. HBase Introduction
  76. 76- 15.2. HBase Architecture
  77. 77- 15.4. HBase Data Model
  78. 78- 15.5. HBase Design Guidelines
  79. 79- 15.6. HBase Data Model Example
  80. 80- 15.7. HBase Hands On With Hue
  81. 81- 15.8. HBase Hands-On With Console
  82. 82- 15.9. HBase Data Location
  83. 83- 15.11. HBase Bloom Filter
  84. 84- 15.12. HBase REST
  85. 85- 15.3. HBase Architecture - Regions