HADOOP

1 - Java Fundamentals
1.1 Basic java concepts
1.2 Multi-threading
1.3 File I/O –Java. IO
1.4 Collections –Java.Util.*, Java.Math, Java.Lang
1.5 Java Generics
1.7 Java Database Connectivity –JDBC
1.8 Java Common Design Patterns
1.9 Java Open Source Frameworks (Spring, Apache Maven, Logging, etc...)

2 - Hadoop Fundamentals 8
2.1 What is Big Data? Why Big Data?
2.2 Hadoop Architecture & Components
2.3 Hadoop Storage & File Formats (ASCII, Avro, Parquet, RC4, JSON, EBCDIC etc.)
2.4 Hadoop Processing – Map Reduce, Spark Frameworks

3 - HDFS 4
3.1 HDFS Basics
3.2 File Storage
3.3 Fault Tolerance

4 - Map Reduce 8
4.1 What Is MapReduce?
4.2 Basic MapReduce Concepts
4.3 Concepts of Mappers, Reducers, Combiners and Paritioning
4.4 Inputs and Output formats to MR Program
4.5 Error Handling and creating UDFs for MR

5 - Spark 4
5.1 What Is Spark?
5.2 Basic Spark Concepts
5.3 How Spark differs from Map Reduce?
5.4 Working with RDD's
5.5 Parallel Programming with Spark
5.6 Spark Streaming

6 - Hive 16
6.1 What is Hive, why we need it and its importance in DWH?
6.2 How Hive is different from Traditional RDBMS
6.3 Modeling in Hive, creating Hive structures and data load process.
6.4 Concepts of Partitioning, Bucketing, Blocks, Hashing, External Tables etc.
6.5 Concepts of serialization, deserialization
6.6 Different Hive data storage formats including ORC, RC, and Parquet.
6.7 Introduction ton HiveQL and examples.
6.8 Hive as an ELT tool and difference between Pig and Hive
6.9 Performance tuning opportunities in Hive, learnings and Best Practices.
6.10 Writing and mastering Hive UDFs
6.11 Error Handling and scope of creating Hive UDFs.

7 - Pig and Latin 12
7.1 Basics of Pig and Why Pig?
7.2 Grunt
7.3 Pig's Data Model
7.4 Writing Evaluation
7.5 Filter
7.6 Load & Store Functions
7.7 Benefits of Pig over SQL language
7.8 Input and Output formats to MR program.
7.9 Error Handling and scope of creating UDFs for Pig.

8 - Sqoop 3
8.1 Sqoop Overview
8.2 Sqoop Exercises
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