Excellent Training from Expert HADOOP & BIG DATA Professionals Developers
At CoreXtrime Consulting Service, we provide excellent HADOOP & BIG DATA Training to our students to make them capable to develop any type of applications. Our highly experience training faculties are well-versed in providing effectual HADOOP & BIG DATA training from starting to completion of training.
Leading Big Data & Hadoop training institute in Ahmedabad, Gujarat
Course Curriculum
| S.No |
Course Outline |
Duration in Hours |
| 1 | Java Fundamentals | 16 |
| 1.1 | Basic java concepts (Object,Counstructor,Inheritance,Overriding) | |
| 1.2 | Multi-threading | |
| 1.3 | File I/O –Java. IO | |
| 1.4 | Collections –Java.Util.*, Java.Math, Java.Lang | |
| 1.6 | Java Serialization | |
| 1.7 | Eclipse IDE – Java Development. | |
| 2 | Hadoop Fundamentals | 8 |
| 2.1 | What is Big Data? Why Big Data? | |
| 2.2 | Hadoop Architecture & Components | |
| 2.4 | Hadoop Processing – Map Reduce, Spark Frameworks | |
| 3 | Hadoop Architecture And HDFS | 4 |
| 3.1 | Hadoop1.x (HDFS Basis) | |
| 3.2 | Hadoop2.x (YARN , Federation ) | |
| 3.3 | File Storage | |
| 3.4 | Fault Tolerance, High Availablity | |
| 3.5 | Hadoop Configuration File | |
| 3.6 | Single Node Cluster / Multinode Cluster | |
| 3.7 | HDFS Commands | |
| 4 | Map Reduce | 9 |
| 4.1 | What Is MapReduce? | |
| 4.2 | Basic MapReduce Concepts | |
| 4.3 | Hadoop 2.x MapReduce Architecture and Components | |
| 4.4 | Input Splits , Relation Between Input Splits and HDFS Blocks | |
| 4.5 | Concepts of Mappers, Reducers, Combiners and Paritioning | |
| 4.6 | Demo (Word Count , Weather DataSet) | |
| Advance MapReduce | ||
| 4.7 | Counters , Distributed Cache | |
| 4.8 | MapSide Join , Reduce Side Join | |
| 4.9 | Inputs and Output formats to MR Program | |
| 4.1 | MRUnit | |
| 5 | Spark | 10 |
| 5.1 | What Is Spark? | |
| 5.2 | Basic Spark Concepts | |
| 5.3 | How Spark differs from Map Reduce? | |
| 5.4 | Spark Ecosystem | |
| 5.5 | Working With RDD | |
| 5.6 | Spark SQl | |
| 5.7 | Saprk Streaming | |
| 5.8 | Spark GraphX | |
| 5.6 | SparkMlib | |
| 6 | Hive | 10 |
| 6.1 | What is Hive, why we need it and its importance? | |
| 6.2 | How Hive is different from Traditional RDBMS | |
| 6.3 | Metastore in Hive | |
| 6.4 | Hive Data Types , Modeling in Hive, creating Hive structures and data load process. | |
| 6.5 | Concepts of Blocks, Hashing, External Tables etc. | |
| 6.6 | Concepts of serialization, deserialization | |
| 6.7 | Different Hive data storage formats including ORC, RC | |
| 6.8 | Introduction ton HiveQL and examples. | |
| 6.9 | Joining Table | |
| 6.1 | Concepts of Partitioning, Bucketing, Indexing | |
| 6.11 | Hive as an ELT tool and difference between Pig and Hive | |
| 6.12 | Writing and mastering Hive UDFs and Thrift Server | |
| 7 | Pig and Latin | 12 |
| 7.1 | Basics of Pig and Why Pig? | |
| 7.2 | Grunt | |
| 7.3 | Pig’s Data Model (Data Types) | |
| 7.4 | Apache Pig Architecture | |
| 7.5 | Installation | |
| 7.6 | Relational Operators (Group Operator, COGROUP Operator, Joins , Union, Diagnostic Operators) | |
| 7.7 | Writing Evaluation (Programming Structure in Pig) | |
| 7.8 | Filter | |
| 7.9 | Load & Store Functions | |
| 7.1 | Benefits of Pig over SQL language | |
| 7.11 | Built In Functions | |
| 7.12 | Execution of xml file using Pig | |
| 8 | HBase | 12 |
| 8.1 | Introduction to NoSQL | |
| 8.2 | HBase – Introduction (HBASE v/s RDBMS ) | |
| 8.3 | When to use HBase | |
| 8.4 | Hbase Architecture | |
| 8.5 | HBase Families & Components | |
| 8.6 | HBase Data Model | |
| 8.7 | Data Storage and Distribution | |
| 8.8 | Zookeeper | |
| 8.9 | HBase Master | |
| 9 | Oozie | 3 |
| 9.1 | Flume and Sqoop Demo | |
| 9.2 | Oozie Components, Oozie Workflow, | |
| 9.3 | Scheduling with Oozie, Demo on Oozie Workflow, | |
| 9.4 | Oozie for MapReduce, PIG, Hive, and Sqoop | |
| 9.5 | Hadoop Project Demo, | |
| Projects in Hadoop will also cover |