BIG DATA HADOOP DEVELOPER CERTIFICATION

BIG DATA HADOOP DEVELOPER CERTIFICATION

547,500.00

 

 

Big Data Hadoop developer certification is an ideal training package for every aspiring professional who wants to make a career in Big Data Analytics using Hadoop Framework. The course equips the participant to work on the Hadoop environment with ease and get hands-on experience with various modules such as YARN, Flume, Oozie, Mahout, & Chukwa

Description

KEY FEATURES

  • 40 Hours of Lab Exercises with Proprietary VM
  • 40 Hours of instructor led Virtual classroom Training
  • 25 Hours of High Quality E-learning Content
  • 13 Chapter-end Quizzes
  • 2 Big Data & Hadoop Simulation Exams
  • 60 Hours Of Real Time Industry-based Projects
  • 30 PDUs Offered
  • 5 Projects with 11 Unique Data Sets Included
  • Java Essentials for Hadoop Included
  • Downloadable eBook Included
  • Packed with Latest & Advanced Modules like Yarn, Flume, Oozie, Mahout, & Chukwa
  • Industry Specific Projects on Top 3 Sectors – Retail, Telecom, & Insurance
  • Additional Free Online Course: Certified Data Scientist – R Language
  • Big Data and Hadoop Developer Certificate

BIG DATA HADOOP DEVELOPER CERTIFICATION

Big Data Hadoop developer certification is an ideal training package for every aspiring professional who wants to make a career in Big Data Analytics using Hadoop Framework. The course equips the participant to work on the Hadoop environment with ease and get hands-on experience with various modules such as YARN, Flume, Oozie, Mahout, & Chukwa

Big Data is a collection of large and complex data sets that cannot be processed using regular database management tools or processing applications. A lot of challenges such as capture, curation, storage, search, sharing, analysis, and visualization can be encountered while handling Big Data. On the other hand, the Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

 

BIG DATA HADOOP DEVELOPER CERTIFICATION COURSE OBJECTIVES

At the end of JK Michaels training in Big Data & Hadoop, participants will be able to:

  • Master the concepts of Hadoop framework and its deployment in a cluster environment
  • Learn to write complex MapReduce programs in both MRv1 & MRv2 (Yarn)
  • Learn high-level scripting frameworks Pig & Hive and perform data analytics using high level scripting language under Pig & Hive
  • Have a good understanding of Ecosystem and its advance components like Flume, Apache Oozie workflow scheduler etc.
  • Understand advance concepts Hadoop 2.0 : Hbase, Zookeeper, and Sqoop
  • Get hands-on experience in different configurations of Hadoop cluster, its optimization & troubleshooting
  • Understand Hadoop Architecture by understanding Hadoop Distribution File System operations principles (vHDFS 1.0 & vHDFS 2.0)
  • Understand advance concepts of parallel processing in MapReduce1.0 (or MRv1) & MapReduce2.0 (or MRv2)
  • Process Big Data sets (around 3.5 Billion Data Points covering in 5 Projects) with high efficiency and can derive a logical conclusion, which is helpful in live industrial scenarios

Course Outlines

COURSE OUTLINES

Getting Started with Big data and Hadoop Developer

Lesson 00 – Course Introduction8:22

  • 1 WelcomePreview1:8
  • 2 Course IntroductionPreview1:17
  • 3 Course Objectives1:27
  • 4 Course Overview1:48
  • 5 Course Overview(contd.)1:54
  • 6 Value to Professionals1:50
  • 7 Lessons Covered1:9
  • 8 Thank You

 

Lesson 01 – Introduction to Big Data and Hadoop29:44

  • 1 Introduction to Big Data and Hadoop1:18
  • 2 Objectives1:17
  • 3 Data Explosion2:4
  • 4 Types of Data1:43
  • 5 Need for Big Data2:19
  • 6 Data – The Most Valuable Resource1:35
  • 7 Big Data and Its Sources1:43
  • 8 Three Characteristics of Big Data0:0
  • 9 Characteristics of Big Data Technology2:31
  • 10 Appeal of Big Data Technology1:38
  • 11 Leveraging Multiple Sources of Data0:0
  • 12 Traditional IT Analytics Approach2:1
  • 13 Big Data Technology – Platform for Discovery and Exploration1:54
  • 14 Big Data Technology – Capabilities1:26
  • 15 Big Data – Use Cases1:43
  • 16 Handling Limitations of Big Data1:37
  • 17 Introduction to Hadoop1:45
  • 18 History and Milestones of Hadoop2:27
  • 19 Organizations Using Hadoop1:28
  • 20 Quiz0:0
  • 21 Summary1:48
  • 22 Thank You

 

Lesson 02 – Getting Started with Hadoop update26:52

  • 1 Getting Started with Hadoop1:19
  • 2 Objectives1:29
  • 3 VMware Player – Introduction1:38
  • 4 VMware Player – Hardware Requirements1:37
  • 5 Steps to Install VMware Player1:58
  • 6 Install VMware Player – Step 11:22
  • 7 Install VMware Player – Step 21:20
  • 8 Install VMware Player – Step 31:26
  • 9 Install VMware Player – Step 41:21
  • 10 Install VMware Player – Step 51:19
  • 11 Install VMware Player – Step 61:17
  • 12 Install VMware Player – Step 71:16
  • 13 Install VMware Player – Step 81:16
  • 14 Install VMware Player – Step 91:51
  • 15 Steps to Create a VM in VMware Player1:49
  • 16 Create a VM in a VMware Player – Step 11:17
  • 17 Create a VM in a VMware Player – Step 21:12
  • 18 Create a VM in a VMware Player – Step 31:15
  • 19 Create a VM in a VMware Player – Step 41:14
  • 20 Create a VM in a VMware Player – Step 51:16
  • 21 Create a VM in a VMware Player – Step 61:15
  • 22 Open a VM in VMware Player – Step11:22
  • 23 Open a VM in VMware Player – Step21:20
  • 24 Oracle VirtualBox to Open a VM1:35
  • 25 Open a VM using Oracle VirtualBox – Step 11:26
  • 26 Open a VM using Oracle VirtualBox – Step 21:13
  • 27 Open a VM using Oracle VirtualBox – Step 31:14
  • 28 Open a VM using Oracle VirtualBox – Step 41:11
  • 29 Business Scenario1:57
  • 30 Demo0:0
  • 31 Demo Summary1:12
  • 32 Summary1:32
  • 33 Thank You

 

Lesson 03 – Hadoop Architecture44:44

  • 1 Hadoop Architecture1:17
  • 2 Objectives1:28
  • 3 Key Terms1:34
  • 4 Hadoop Cluster Using Commodity Hardware1:42
  • 5 Hadoop Configuration0:0
  • 6 Hadoop Core Services1:30
  • 7 Apache Hadoop Core Components1:26
  • 8 Hadoop Core Components – HDFS1:57
  • 9 Hadoop Core Components – MapReduce1:41
  • 10 Regular File System vs. HDFS1:48
  • 11 HDFS – Characteristics2:40
  • 12 HDFS – Key Features1:55
  • 13 HDFS Architecture2:4
  • 14 HDFS – Operation Principle3:25
  • 15 HDFS2:11
  • 16 File System Namespace1:31
  • 17 NameNode Operation3:20
  • 18 Data Block Split1:57
  • 19 Benefits of Data Block Approach1:17
  • 20 HDFS – Block Replication Architecture1:57
  • 21 Replication Method1:47
  • 22 Data Replication Topology1:27
  • 23 Data Replication Representation2:7
  • 24 HDFS Access1:31
  • 25 Business Scenario1:28
  • 26 Demo0:0
  • 27 Demo Summary1:11
  • 28 Quiz0:0
  • 29 Summary1:36
  • 30 Thank You

 

Lesson 04 – Hadoop Deployment52:44

  • 1 Hadoop Deployment1:21
  • 2 Objectives1:25
  • 3 Ubuntu Server – Introduction1:49
  • 4 Installation of Ubuntu Server 12.041:50
  • 5 Business Scenario1:56
  • 6 Demo 10:0
  • 7 Demo Summary1:46
  • 8 Hadoop Installation – Prerequisites1:24
  • 9 Hadoop Installation2:48
  • 10 Hadoop Installation – Step 11:32
  • 11 Hadoop Installation – Step 22:6
  • 12 Hadoop Installation – Step 31:22
  • 13 Hadoop Installation – Step 41:23
  • 14 Hadoop Installation – Step 51:14
  • 15 Hadoop Installation – Step 61:17
  • 16 Hadoop Installation – Step 71:21
  • 17 Hadoop Installation – Step 7 (contd.)1:21
  • 18 Hadoop Installation – Step 81:27
  • 19 Hadoop Installation – Step 8 (contd.)1:15
  • 20 Hadoop Installation – Step 8 (contd.)1:13
  • 21 Hadoop Installation – Step 91:23
  • 22 Hadoop Installation – Step 9 (contd.)1:35
  • 23 Hadoop Installation – Step 101:20
  • 24 Hadoop Installation – Step 10 (contd.)1:20
  • 25 Hadoop Installation – Step 111:36
  • 26 Hadoop Installation – Step 121:38
  • 27 Hadoop Installation – Step 12 (contd.)1:31
  • 28 Demo 20:0
  • 29 Demo Summary2:16
  • 30 Hadoop Multi-Node Installation – Prerequisites1:26
  • 31 Steps for Hadoop Multi-Node Installation1:43
  • 32 Hadoop Multi-Node Installation – Steps 1 and 21:18
  • 33 Hadoop Multi-Node Installation – Step 31:23
  • 34 Hadoop Multi-Node Installation – Step 3 (contd.)1:43
  • 35 Hadoop Multi-Node Installation – Step 41:52
  • 36 Hadoop Multi-Node Installation – Step 4( contd.)1:20
  • 37 Hadoop Multi-Node Installation – Step 4 (contd.)1:28
  • 38 Single-Node Cluster vs. Multi-Node Cluster1:43
  • 39 Demo 30:0
  • 40 Demo Summary1:16
  • 41 Demo 40:0
  • 42 Demo Summary3:1
  • 43 Demo 50:0
  • 44 Demo Summary3:2
  • 45 Quiz0:0
  • 46 Summary2:2
  • 47 Thank You

 

Lesson 05 – Introduction to MapReduce56:0

  • 1 Introduction to MapReduce1:18
  • 2 Objectives1:20
  • 3 MapReduce – Introduction2:3
  • 4 MapReduce – Analogy2:1
  • 5 MapReduce – Analogy (contd.)1:49
  • 6 MapReduce – Example2:54
  • 7 Map Execution0:0
  • 8 Map Execution – Distributed Two Node Environment1:0
  • 9 MapReduce Essentials2:13
  • 10 MapReduce Jobs2:6
  • 11 MapReduce Engine1:49
  • 12 MapReduce and Associated Tasks1:59
  • 13 MapReduce Association with HDFS1:39
  • 14 Hadoop Job Work Interaction0:0
  • 15 Characteristics of MapReduce1:51
  • 16 Real-Time Uses of MapReduce1:56
  • 17 Prerequisites for Hadoop Installation in Ubuntu Desktop 12.041:21
  • 18 Steps to Install Hadoop1:52
  • 19 Business Scenario2:4
  • 20 Set up Environment for MapReduce Development1:25
  • 21 Small Data and Big Data1:35
  • 22 Uploading Small Data and Big Data1:30
  • 23 Demo 10:0
  • 24 Demo Summary1:12
  • 25 Build MapReduce Program1:52
  • 26 Hadoop MapReduce Requirements1:59
  • 27 Hadoop MapReduce – Features1:49
  • 28 Hadoop MapReduce – Processes1:44
  • 29 Steps of Hadoop MapReduce2:16
  • 30 MapReduce – Responsibilities1:53
  • 31 MapReduce Java Programming in Eclipse1:25
  • 32 Create a New Project: Step 11:24
  • 33 Create a New Project: Step 21:11
  • 34 Create a New Project: Step 31:12
  • 35 Create a New Project: Step 41:13
  • 36 Create a New Project: Step 51:20
  • 37 Demo 20:0
  • 38 Demo Summary1:12
  • 39 Demo 30:0
  • 40 Demo Summary1:23
  • 41 Checking Hadoop Environment for MapReduce1:39
  • 42 Demo 40:0
  • 43 Demo Summary1:15
  • 44 Demo 50:0
  • 45 Demo Summary1:36
  • 46 Demo 60:0
  • 47 Demo Summary1:54
  • 48 MapReduce v 2.01:8
  • 49 Quiz0:0
  • 50 Summary1:32
  • 51 Thank You

 

Lesson 06 – Advanced HDFS and MapReduce41:12

  • 1 Advanced HDFS and MapReduce1:20
  • 2 Objectives1:24
  • 3 Advanced HDFS – Introduction1:47
  • 4 HDFS Benchmarking1:32
  • 5 HDFS Benchmarking (contd.)1:19
  • 6 Setting Up HDFS Block Size1:45
  • 7 Setting Up HDFS Block Size – Step 11:13
  • 8 Setting Up HDFS Block Size – Step 21:40
  • 9 Decommissioning a DataNode1:49
  • 10 Decommissioning a DataNode – Step 11:14
  • 11 Decommissioning a DataNode – Step 21:14
  • 12 Decommissioning a DataNode – Step 3 and 41:21
  • 13 Business Scenario1:34
  • 14 Demo 10:0
  • 15 Demo Summary1:30
  • 16 Advanced MapReduce1:48
  • 17 Interfaces0:0
  • 18 Data Types in Hadoop2:19
  • 19 InputFormats in MapReduce2:19
  • 20 OutputFormats in MapReduce2:44
  • 21 Distributed Cache2:6
  • 22 Using Distributed Cache – Step 11:20
  • 23 Using Distributed Cache – Step 21:16
  • 24 Using Distributed Cache – Step 31:24
  • 25 Joins in MapReduce0:0
  • 26 Reduce Side Join1:38
  • 27 Reduce Side Join (contd.)1:36
  • 28 Replicated Join1:30
  • 29 Replicated Join (contd.)1:41
  • 30 Composite Join1:34
  • 31 Composite Join (contd.)1:28
  • 32 Cartesian Product1:38
  • 33 Cartesian Product (contd.)1:25
  • 34 Demo 20:0
  • 35 Demo Summary1:48
  • 36 Quiz0:0
  • 37 Summary1:44
  • 38 Thank You

 

Lesson 07 – Pig46:28

  • 1 Pig1:16
  • 2 Objectives1:21
  • 3 Challenges Of MapReduce Development Using Java1:54
  • 4 Introduction To Pig1:36
  • 5 Components Of Pig1:51
  • 6 How Pig Works1:47
  • 7 Data Model1:40
  • 8 Data Model (contd.)2:37
  • 9 Nested Data Model1:28
  • 10 Pig Execution Modes1:29
  • 11 Pig Interactive Modes1:30
  • 12 Salient Features1:32
  • 13 Pig vs. SQL1:55
  • 14 Pig vs. SQL – Example2:12
  • 15 Installing Pig Engine1:24
  • 16 Steps To Installing Pig Engine1:32
  • 17 Installing Pig Engine – Step 11:16
  • 18 Installing Pig Engine – Step 21:33
  • 19 Installing Pig Engine – Step 31:20
  • 20 Installing Pig Engine – Step 41:9
  • 21 Installing Pig Engine – Step 51:10
  • 22 Run A Sample Program To Test Pig1:36
  • 23 Getting Datasets For Pig Development1:22
  • 24 Prerequisites To Set The Environment For Pig Latin1:21
  • 25 Prerequisites To Set The Environment For Pig Latin – Step 11:17
  • 26 Prerequisites To Set The Environment For Pig Latin – Step 21:12
  • 27 Prerequisites To Set The Environment For Pig Latin – Step 31:13
  • 28 Loading And Storing Methods – Step 11:32
  • 29 Loading and Storing Methods – Step 21:24
  • 30 Script Interpretation1:44
  • 31 Filtering and Transforming1:28
  • 32 Grouping and Sorting1:21
  • 33 Combining and Splitting1:22
  • 34 Pig Commands2:8
  • 35 Business Scenario1:52
  • 36 Demo 10:0
  • 37 Demo Summary1:23
  • 38 Demo 20:0
  • 39 Demo Summary1:26
  • 40 Demo 30:0
  • 41 Demo Summary1:26
  • 42 Demo 40:0
  • 43 Demo Summary1:25
  • 44 Demo 50:0
  • 45 Demo Summary1:41
  • 46 Demo 60:0
  • 47 Demo Summary1:16
  • 48 Quiz0:0
  • 49 Summary1:38
  • 50 Thank You

 

Lesson 08 – Hive49:44

  • 1 Hive1:14
  • 2 Objectives1:23
  • 3 Need for Additional Data Warehousing System1:47
  • 4 Hive – Introduction1:46
  • 5 Hive – Characteristics2:7
  • 6 System Architecture and Components of Hive1:20
  • 7 Metastore1:29
  • 8 Metastore Configuration1:34
  • 9 Driver1:26
  • 10 Query Compiler1:21
  • 11 Query Optimizer1:30
  • 12 Execution Engine1:21
  • 13 Hive Server1:32
  • 14 Client Components1:41
  • 15 Basics of The Hive Query Language1:37
  • 16 Data Model – Tables1:37
  • 17 Data Model – External Tables2:4
  • 18 Data Types in Hive0:0
  • 19 Data Model – Partitions1:42
  • 20 Serialization and Deserialization2:5
  • 21 Hive File Formats1:34
  • 22 Hive Query Language – Select1:26
  • 23 Hive Query Language – JOIN and INSERT1:13
  • 24 Hive Installation – Step 11:25
  • 25 Hive Installation – Step 21:20
  • 26 Hive Installation – Step 31:18
  • 27 Hive Installation – Step 41:23
  • 28 Running Hive1:25
  • 29 Programming in Hive1:20
  • 30 Programming in Hive (contd.)1:17
  • 31 Programming in Hive (contd.)1:31
  • 32 Programming in Hive (contd.)1:16
  • 33 Programming in Hive (contd.)1:11
  • 34 Programming in Hive (contd.)1:15
  • 35 Programming in Hive (contd.)1:11
  • 36 Programming in Hive (contd.)1:13
  • 37 Programming in Hive (contd.)1:13
  • 38 Hive Query Language – Extensibility1:21
  • 39 User-Defined Function1:41
  • 40 Built-In Functions1:26
  • 41 Other Functions in Hive2:24
  • 42 MapReduce Scripts1:56
  • 43 UDF and UDAF vs. MapReduce Scripts1:29
  • 44 Business Scenario1:41
  • 45 Demo 10:0
  • 46 Demo Summary1:29
  • 47 Demo 20:0
  • 48 Demo Summary1:17
  • 49 Demo 30:0
  • 50 Demo Summary1:16
  • 51 Demo 40:0
  • 52 Demo Summary1:29
  • 53 Quiz0:0
  • 54 Summary1:42
  • 55 Thank You

 

Lesson 09 – HBase32:42

  • 1 HBase1:14
  • 2 Objectives1:24
  • 3 HBase – Introduction2:8
  • 4 Characteristics of HBase1:37
  • 5 Companies Using HBase1:13
  • 6 HBase Architecture1:50
  • 7 HBase Architecture (contd.)1:48
  • 8 Storage Model of HBase1:59
  • 9 Row Distribution of Data between RegionServers1:25
  • 10 Data Storage in HBase1:41
  • 11 Data Model1:0
  • 12 When to Use HBase1:38
  • 13 HBase vs. RDBMS1:59
  • 14 Installation of HBase1:41
  • 15 Installation of HBase – Step 11:13
  • 16 Installation of HBase – Steps 2 and 31:23
  • 17 Installation of HBase – Steps 4 and 51:23
  • 18 Installation of HBase – Steps 6 and 71:19
  • 19 Installation of HBase – Step 81:13
  • 20 Configuration of HBase1:9
  • 21 Configuration of HBase – Step 11:19
  • 22 Configuration of HBase – Step 21:16
  • 23 Configuration of HBase – Steps 3 and 41:23
  • 24 Business Scenario1:25
  • 25 Demo0:0
  • 26 Demo Summary1:47
  • 27 Connecting to HBase1:46
  • 28 HBase Shell Commands1:28
  • 29 HBase Shell Commands (contd.)1:27
  • 30 Quiz0:0
  • 31 Summary1:37
  • 32 Thank you

 

Lesson 10 – Commercial Distribution of Hadoop25:22

  • 1 Commercial Distribution of Hadoop1:18
  • 2 Objectives1:27
  • 3 Cloudera – Introduction1:39
  • 4 Cloudera CDH1:59
  • 5 Downloading The Cloudera QuickStart Virtual Machine1:22
  • 6 Starting The Cloudera VM1:47
  • 7 Starting The Cloudera VM – Steps 1 and 21:21
  • 8 Starting The Cloudera VM – Steps 3 and 41:20
  • 9 Starting The Cloudera VM – Step 51:25
  • 10 Starting The Cloudera VM – Step 61:15
  • 11 Logging Into Hue1:23
  • 12 Logging Into Hue (contd.)1:21
  • 13 Logging Into Hue (contd.)1:22
  • 14 Cloudera Manager1:30
  • 15 Logging Into Cloudera Manager0:0
  • 16 Business Scenario1:50
  • 17 Demo 10:0
  • 18 Demo Summary1:23
  • 19 Demo 20:0
  • 20 Demo Summary1:35
  • 21 Hortonworks Data Platform1:38
  • 22 MapR Data Platform1:40
  • 23 Pivotal HD1:55
  • 24 IBM InfoSphere BigInsights1:44
  • 25 IBM InfoSphere BigInsights (contd.)0:0
  • 26 Quiz0:0
  • 27 Summary1:49
  • 28 Thank You

 

Lesson 11 – ZooKeeper Sqoop and Flume57:54

  • 1 ZooKeeper, Sqoop and Flume1:18
  • 2 Objectives1:30
  • 3 Introduction to ZooKeeper1:21
  • 4 Features of ZooKeeper2:3
  • 5 Challenges Faced in Distributed Applications1:39
  • 6 Coordination2:3
  • 7 Goals of ZooKeeper1:39
  • 8 Uses of ZooKeeper1:41
  • 9 ZooKeeper Entities1:50
  • 10 ZooKeeper Data Model1:40
  • 11 ZooKeeper Services1:35
  • 12 ZooKeeper Services (contd.)1:49
  • 13 Client API Functions2:3
  • 14 Recipe 1: Cluster Management1:46
  • 15 Recipe 2: Leader Election1:41
  • 16 Recipe 3: Distributed Exclusive Lock1:54
  • 17 Business Scenario1:35
  • 18 Demo 10:0
  • 19 Demo Summary1:16
  • 20 Why Sqoop2:13
  • 21 Why Sqoop (contd.)1:56
  • 22 Benefits of Sqoop1:38
  • 23 Sqoop Processing1:37
  • 24 Sqoop Under The Hood1:30
  • 25 Importing Data Using Sqoop1:22
  • 26 Sqoop Import – Process0:0
  • 27 Sqoop Import – Process (contd.)1:55
  • 28 Importing Data To Hive0:0
  • 29 Importing Data To HBase1:30
  • 30 Importing Data To HBase (contd.)0:0
  • 31 Exporting Data From Hadoop Using Sqoop1:13
  • 32 Exporting Data From Hadoop Using Sqoop (contd.)1:37
  • 33 Sqoop Connectors1:48
  • 34 Sample Sqoop Commands1:0
  • 35 Business Scenario1:41
  • 36 Demo 20:0
  • 37 Demo Summary1:48
  • 38 Demo 30:0
  • 39 Demo Summary1:36
  • 40 Why Flume1:30
  • 41 Apache Flume – Introduction1:32
  • 42 Flume Model1:36
  • 43 Flume – Goals1:42
  • 44 Scalability In Flume1:33
  • 45 Flume – Sample Use Cases1:34
  • 46 Business Scenario1:28
  • 47 Demo 40:0
  • 48 Demo Summary1:37
  • 49 Quiz0:0
  • 50 Summary2:1
  • 51 Thank You

 

Lesson 12 – Ecosystem and its Components24:38

  • 1 Ecosystem and Its Components1:19
  • 2 Objectives1:18
  • 3 Apache Hadoop Ecosystem0:0
  • 4 Apache Oozie1:45
  • 5 Apache Oozie Workflow1:51
  • 6 Apache Oozie Workflow (contd.)1:47
  • 7 Introduction to Mahout0:0
  • 8 Why Mahout1:30
  • 9 Features of Mahout1:35
  • 10 Usage of Mahout1:27
  • 11 Usage of Mahout (contd.)1:32
  • 12 Apache Cassandra1:51
  • 13 Why Apache Cassandra1:38
  • 14 Apache Spark2:17
  • 15 Apache Spark Tools2:9
  • 16 Key Concepts Related to Apache Spark1:0
  • 17 Apache Spark – Example1:12
  • 18 Hadoop Integration1:38
  • 19 Quiz0:0
  • 20 Summary1:51
  • 21 Thank You

 

Lesson 13 – Hadoop Administration, Troubleshooting, and Security39:58

  • 1 Hadoop Administration, Troubleshooting and Security1:19
  • 2 Objectives1:23
  • 3 Typical Hadoop Core Cluster1:33
  • 4 Load Balancer1:30
  • 5 Commands Used in Hadoop Programming1:55
  • 6 Different Configuration Files of Hadoop Cluster2:7
  • 7 Properties of hadoop default.xml2:8
  • 8 Different Configurations for Hadoop Cluster1:42
  • 9 Different Configurations for Hadoop Cluster (contd.)2:32
  • 10 Port Numbers for Individual Hadoop Services2:31
  • 11 Performance Monitoring1:42
  • 12 Performance Tuning1:25
  • 13 Parameters of Performance Tuning2:27
  • 14 Troubleshooting and Log Observation1:43
  • 15 Apache Ambari1:31
  • 16 Key Features of Apache Ambari1:47
  • 17 Business Scenario1:0
  • 18 Demo 10:0
  • 19 Demo Summary1:24
  • 20 Demo 20:0
  • 21 Demo Summary1:45
  • 22 Hadoop Security – Kerberos1:58
  • 23 Kerberos – Authentication Mechanism0:0
  • 24 Kerberos Configuration2:8
  • 25 Data Confidentiality2:7
  • 26 Quiz0:0
  • 27 Summary1:35
  • 28 Thank You

Why is the certification most sought-after?

  • Why is the certification most sought-after?

As the Big Data buzz is getting bigger with volume, variety, and velocity, certified hadoopers equipped with the right skills to process the Big Data through Hadoop are becoming the ‘most wanted’ employees in Fortune 500 companies. This incredible surge has greatly increased the career scope for certified hadoopers in comparison to their non-certified peers. This certification is one among the most sought-after certifications for the reasons given below:

  • According to Gartner – “Big Data & Analytics is one of the top 10 strategic technologies for businesses and there would be 4.4 Million Big Data jobs by 2015”
  • Top companies like Microsoft, Software AG, IBM, Oracle, HP, SAP, EMC2, and Dell have invested a huge $15 billion on Data Management and Analytics
  • According to IDC – “Big Data market would grow up to $16.1 billion”
  • According to Indeed.com – “Certified Big Data analysts start earning $117,000 in comparison to their non-certified peers”
  • According to Robert Half Technology’s ‘2015 Salary guide for Technology professionals’- “Big Data is one of the key drivers for Technology hiring in 2015.”

What are the career benefits in-store for you?

  • What are the career benefits in-store for you?

    • The certification make participants ride the Big Data wave, enhance their analytics skills, and help them to land in job roles like Data Scientist, Hadoop Developer, Hadoop Architect, and Hadoop Tester.
    • Top companies like Microsoft, Software AG, IBM, Oracle, HP, SAP, EMC2, and Dell have invested a huge $15 billion on data management and analytics, thereby increasing the number of opportunities for Big data & Hadoop certified professionals.
    • Certified analysts earn $117,000 in comparison to their non-certified peers. (Source-payscale.com)
    • Certified Big Data professionals with hands-on exposure to industry relevant tools have a growing career graph.
    • Possessing this coveted skill makes it easier for an aspirant to switch to his/her industry or function of choice.

Who should do this course?

  • Who should do this course?

Java developers, Architects, Big Data professionals, or anyone who is looking to build a career in Big Data and Hadoop are ideal participants for the Big Data and Hadoop training. Additionally, it is suitable for participants who are:

  • Data Management Professionals
  • Data Warehousing Professionals
  • Business Intelligence Professionals
  • Graduates

EXAM & CERTIFICATION

EXAM & CERTIFICATION

  • How do I become Certified Big Data & Hadoop Developer?

To become a Certified Big Data & Hadoop Developer, it is mandatory that the participant fulfills both the following criteria:

  • Completing any one project out of the five projects given by Simplilearn. The outcome of the project should be verified by the lead trainer and the candidate is evaluated thereafter. Necessary screenshots of the outputs of the project should be mailed to mails@jkmichaelspm.com.
  • Clearing the online examination with a minimum score of 80%. Note: It is mandatory that a participant fulfills both the criteria i.e. completion of 1 Project and clearing the online exam with minimum score of 80% to become a Certified Big Data & Hadoop Developer.

What are the projects covered to get certified and their benefits?

  • What are the projects covered to get certified and their benefits?

The exceptionality of JK Michaels Big Data & Hadoop training is the opportunity for participants to work on 5 live industry-based projects spanning across 11 Unique Data Sets and covering around 3.5 Billion Data Points. This adds immense domain knowledge and real life industry experience to a participant’s Curriculum Vitae.

Project 1: Analyzing a series of Data sets for a US-based customer to arrive at a prudent product mix, product positioning and marketing strategy.

Through this project, participants will get a chance to work as a Hadoop developer and the responsibility for completing all the subprojects within the defined timeframe.

Scenario: Your Company has recently bagged a large assignment from a US-based customer that is into training and development. The larger outcome of this project, deals with launching a suite of educational and skill development programs to consumers across the globe. As part of the project, the customer wants your company to analyze a series of data sets to arrive at a prudent product mix, product positioning, and marketing strategy that will be applicable for at least a decade.

The whole project is divided into 7 subprojects, each involving its own data set.

Subproject 1: Identify motivators for continuous adult education.

Subproject 2: Identify occupations poised for growth & decline over the next 10 years.

Subproject 3: Identify regions that have potential for growth across potential industries.

Subproject 4: Categorize financial capacity of consumers across regions and demographics.

Subproject 5: Identify major gender and geographic attributes for education.

Subproject 6: Analyze the education expenditure and related parameters across the globe.

Subproject 7: Analyze the strength of the financial sector in target markets and participation of the population in the financial sectors.
Project 2: Analyze and perform page ranking for Twitter data set

Scenario: As a Hadoop developer, your task is to perform page ranking of the Twitter data based on the data set provided.

Project 3: Analyze Monthly retail report for the US Market – Retail Industry

Scenario: A US – based online retailer wants to launch a new product category and wants to understand the potential growth areas and areas that have stagnated over a period of time. It wants to use this information to ensure its product focus is aligned to opportunities that will grow over the next 5–7 years. The customer has been provided the data set that they can use.

Project 4: Analyze Mobile connectivity report for the UK Market – Telecom Industry

Scenario: A UK – based customer wants to launch 3G devices in regions where their penetration is low and you have been allocated the task of performing this analysis using Hadoop. The customer has been provided the data set that they can use.

Project 5: Analyze health reports across years for the US Market – Insurance Industry

Scenario: A US – based insurance provider has decided to launch a new medical insurance program targeting various customers. To give this customer, a better understanding of the current realities and the market, you have to perform a series of data analytics tasks using Hadoop. The customer has been provided the data set that they can use.

What are the prerequisites for the certification?

  • What are the prerequisites for the certification?

Aspirants with fundamental programming skills are eligible for this certification. However, a working knowledge of Java, UNIX, and SQL would be an added advantage.

Why choose JK Michaels for your training?

  • Why choose JK Michaels for your training?

JK Michaels Big Data & Hadoop training is the first of its kind, which provides comprehensive training and is suitable for professionals. It is the best in terms of time and money invested. We stand out because participants:-

  • Have the flexibility to choose from 3 different modes of learning
  • Get hands-on lab exercises
  • Work on 5 real life industry-based projects covering 3.5 Billion Data Points spanned across 11 Data Sets. All Projects are based upon real life industrial scenarios.

Take a look at how JK Michaels training stands above other training providers:

 

JK Michaels Other Training Providers
160 Hrs of Total learning  (2x more as compared to any other Training Provider) 90 Hrs or less
60 Hrs of Real Time Industry Based Projects – 3.5 Bn Datapoints to work upon (3x more as compared to any other Training Provider) 20 Hrs or less
25 Hrs of High Quality e-Learning content Not Available
5 Hrs of Doubt Clarification Classes Not Available
Flexibility to choose training type from 3 available training modes Only single training mode
Chance to work on 5 Real Life industry based Projects 1 or No Project
Industry Specific Projects on Top 3 Sectors – Retail, Telecom & Insurance Not Available
11 Unique Data Sets to work upon 7 or less
Free 90 Days e-Learning access worth $252 with every Instructor led training Not Available
Free Java Essentials for Hadoop Not Available

In addition, JK Michaels training is the best because:

  • JK Michaels is in partnership with best in class technology institutes and the World’s Largest Certification Training Provider, with over 400,000+ professionals trained globally.
  • Trusted by the Fortune 500 companies as their learning provider for career growth and training.
  • 2,000+ certified and experienced trainers conduct training for various courses across the globe.
  • All our Courses are designed and developed under a tried and tested Unique Learning Framework that is proven to deliver 98.6% clearance rate in first attempt.
  • Accredited, Approved, and Recognized as a training organization, partner, education provider, and examination center by globally renowned names like Project Management Institute of USA, APMG, CFA Institute, GARP, ASTQB, IIBA, and others.

Who will be the trainer for the classroom training?

Highly qualified and certified instructors with industry relevant experience deliver classroom trainings.

How do I enroll for the classroom training?

You can enroll for this virtual classroom training online. Payments can be made using any of the following options and a receipt of the same will be issued to the candidate automatically via email.
  1. Visa Debit/credit Card
  2. American Express and Diners Club Card
  3. Master Card, Or
  4. PayPal

Where will be the training held?

The venue is finalized a few weeks prior to the training and you will be informed via email. You can get in touch with our 24/7 Support Team for more details. Email us at mails@jkmichaelspm.com and we will promptly answer all your queries. If you are looking for an instant support, you can chat with us too.

Do you provide transportation and refreshments along with the training?

We do not provide transportation or refreshments along with the training.Its a virtual Classroom Training

What will I get along with this training?

In this training, you will have free access to the online Big Data and Hadoop e-learning content, Java Introduction, and real life scenario-based projects.

Can I cancel my enrollment? Do I get a refund?

Yes, you can cancel your enrollment. We provide a complete refund after deducting the administration fee. To know more please go through our Refund Policy.

Do you provide a Money Back Guarantee for the training programs?

Yes, we do provide a Money Back Guarantee for some of our training programs. You can contact mails@jkmichaelspm.com for more information.

Do you provide assistance for the exam?

Yes, we do provide guidance and assistance for some of our certification exams.

Who provides the certification?

Big Data and Hadoop training is ideal for Java developers and architects. There is no governing body that administers Big Data and Hadoop exams. However, many training providers conduct exams to evaluate a candidate’s skills on Big Data and Hadoop training.

Do you provide any course completion certificate?

Yes. We offer a course completion certificate after you successfully complete the training program.

Do you provide any group discounts for classroom training programs?

Yes, we have group discount packages for virtual classroom training programs. Contact mails@jkmichaelspm.com to know more about the group discounts.

What is Big Data?

Big Data is a collection of large and complex data sets that cannot be processed using regular database management tools or processing applications.

What is Apache Hadoop?

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

What are the System Requirements?

To run Hadoop, the participant’s system needs to fulfill the following requirements:
  • 64-bit Operating System
  • 4GB Ram

Do you provide PDUs after completing the training? How many hours of PDU certificate do I get after attending the training?

Yes, we offer PDU certificates to candidates after successfully completing the training. You can earn 45 hours of PDUs after attending the training.