MASTER OF COMPUTER APPLICATIONS
Course : BigData Technologies
Code : RLMCA204
For private circulation only www.ajce.in
, Syllabus
Introduction to Big Data Platform – History of Data Management Structuring Big data - Elements
of Big Data, Big data stack - Big data Analytics - Introducing Technologies for handling Big Data:
Distributed and Parallel Computing for Big Data - Cloud Computing and Big Data
Big Data Storage Concepts- Clusters - File Systems and Distributed File Systems- NoSQL –
Sharding – Replication – Sharding and Replication – CAP Theorem – ACID – BASE Big Data
Processing Concepts- Parallel Data Processing – Distributed Data Processing – Hadoop –
Processing in Batch Mode – Processing in Real time Mode
Introduction to Hadoop Ecosystem - Hadoop Distributed File System-HDFS Architecture -
Features of HDFS - Map ReduceFeatures of Map Reduce- Hadoop Yarn - HBase- Hive – Sqoop
– ZooKeeper – Flume – Oozie.
Understanding Map Reduce Fundamentals- Map Reduce Framework- Exploring Features of Map
Reduce- Working of Map Reduce- Exploring Map and Reduce Functions- Techniques
to optimize Map Reduce- Hardware/ Network TopologySynchronization- File System- Uses of
Map Reduce
Big Data Storage Technology – On-Disk Storage Devices – Distributed File Systems, RDBMS
Databases, NoSQL Databases, NewSQL Databases – In-Memory Storage Devices: In-Memory
Data Grids, In-Memory Databases.
Introduction to Big Data Analysis Techniques- Quantitative Analysis – Qualitative Analysis –
Data Mining - Statistical Analysis - Machine Learning – Semantic Analysis – Visual Analysis
, Chapter/
Module Topic Reference Text
Page. No
Introduction to Big Data Platform – History of Data DreamTech Editorial Services,
Chapters- 1
I Management Structuring Big data - Elements of Big Data, “Big Data”, DreamTech Press,
Page: 1-19
Big data Analytics 2015 Edition.
DreamTech Editorial Services, Chapter -6
I Big data stack “Big Data”, DreamTech Press, Pages: 149-
2015 Edition. 159
Introducing Technologies for handling Big Data DreamTech Editorial Services,
Chapter- 3
I Distributed and Parallel Computing for Big Data - “Big Data”, DreamTech Press,
Pages 53-73
Cloud Computing and Big Data. 2015 Edition.
Big Data Storage Concepts- Clusters - File Systems and
Distributed File Systems- NoSQL – Sharding –
Thomas Erl ,”Big Data
Replication – Sharding and Replication – CAP Theorem
Fundamentals Concepts, Drivers Chapter 5,6
II – ACID – BASE Big Data Processing Concepts- Parallel
and Techniques”, Pearson Pages: 91-144
Data Processing – Distributed Data Processing – Hadoop
Education First Edition,2016
– Processing in Batch Mode – Processing in Real time
Mode
Introduction to Hadoop Ecosystem - Hadoop Distributed
DreamTech Editorial Services,
File System-HDFS Architecture - Features of HDFS - Chapter-4
III “Big Data”, DreamTech Press,
Map Reduce -Features of Map Reduce- Hadoop Yarn - Pages: 83-115
2015 Edition.
HBase- Hive – Sqoop – ZooKeeper – Flume – Oozie.
Understanding Map Reduce Fundamentals- Map Reduce
Framework- Exploring Features of Map Reduce-
DreamTech Editorial Services,
Working of Map Reduce- Exploring Map and Reduce Chapter-5
IV “Big Data”, DreamTech Press,
Functions- Techniques to optimize Map Reduce- Pages 121-133
2015 Edition.
Hardware/ Network Topology Synchronization- File
System- Uses of Map Reduce
Big Data Storage Technology – On-Disk Storage Devices Thomas Erl ,”Big Data
– Distributed File Systems, RDBMS Databases, NoSQL Fundamentals Concepts, Drivers Chapter- 7
V
Databases, New SQL Databases – In-Memory Storage and Techniques”, Pearson Pages: 145-179
Devices: In-Memory Data Grids, In-Memory Databases Education , First Edition,2016
Introduction to Big Data Analysis Techniques- Thomas Erl ,”Big Data
Quantitative Analysis – Qualitative Analysis – Data Fundamentals Concepts, Drivers Chapter-8
VI
Mining - Statistical Analysis - Machine Learning – and Techniques”, Pearson Pages: 181-205
Semantic Analysis – Visual Analysis. Education, First Edition,2016
,
Course : BigData Technologies
Code : RLMCA204
For private circulation only www.ajce.in
, Syllabus
Introduction to Big Data Platform – History of Data Management Structuring Big data - Elements
of Big Data, Big data stack - Big data Analytics - Introducing Technologies for handling Big Data:
Distributed and Parallel Computing for Big Data - Cloud Computing and Big Data
Big Data Storage Concepts- Clusters - File Systems and Distributed File Systems- NoSQL –
Sharding – Replication – Sharding and Replication – CAP Theorem – ACID – BASE Big Data
Processing Concepts- Parallel Data Processing – Distributed Data Processing – Hadoop –
Processing in Batch Mode – Processing in Real time Mode
Introduction to Hadoop Ecosystem - Hadoop Distributed File System-HDFS Architecture -
Features of HDFS - Map ReduceFeatures of Map Reduce- Hadoop Yarn - HBase- Hive – Sqoop
– ZooKeeper – Flume – Oozie.
Understanding Map Reduce Fundamentals- Map Reduce Framework- Exploring Features of Map
Reduce- Working of Map Reduce- Exploring Map and Reduce Functions- Techniques
to optimize Map Reduce- Hardware/ Network TopologySynchronization- File System- Uses of
Map Reduce
Big Data Storage Technology – On-Disk Storage Devices – Distributed File Systems, RDBMS
Databases, NoSQL Databases, NewSQL Databases – In-Memory Storage Devices: In-Memory
Data Grids, In-Memory Databases.
Introduction to Big Data Analysis Techniques- Quantitative Analysis – Qualitative Analysis –
Data Mining - Statistical Analysis - Machine Learning – Semantic Analysis – Visual Analysis
, Chapter/
Module Topic Reference Text
Page. No
Introduction to Big Data Platform – History of Data DreamTech Editorial Services,
Chapters- 1
I Management Structuring Big data - Elements of Big Data, “Big Data”, DreamTech Press,
Page: 1-19
Big data Analytics 2015 Edition.
DreamTech Editorial Services, Chapter -6
I Big data stack “Big Data”, DreamTech Press, Pages: 149-
2015 Edition. 159
Introducing Technologies for handling Big Data DreamTech Editorial Services,
Chapter- 3
I Distributed and Parallel Computing for Big Data - “Big Data”, DreamTech Press,
Pages 53-73
Cloud Computing and Big Data. 2015 Edition.
Big Data Storage Concepts- Clusters - File Systems and
Distributed File Systems- NoSQL – Sharding –
Thomas Erl ,”Big Data
Replication – Sharding and Replication – CAP Theorem
Fundamentals Concepts, Drivers Chapter 5,6
II – ACID – BASE Big Data Processing Concepts- Parallel
and Techniques”, Pearson Pages: 91-144
Data Processing – Distributed Data Processing – Hadoop
Education First Edition,2016
– Processing in Batch Mode – Processing in Real time
Mode
Introduction to Hadoop Ecosystem - Hadoop Distributed
DreamTech Editorial Services,
File System-HDFS Architecture - Features of HDFS - Chapter-4
III “Big Data”, DreamTech Press,
Map Reduce -Features of Map Reduce- Hadoop Yarn - Pages: 83-115
2015 Edition.
HBase- Hive – Sqoop – ZooKeeper – Flume – Oozie.
Understanding Map Reduce Fundamentals- Map Reduce
Framework- Exploring Features of Map Reduce-
DreamTech Editorial Services,
Working of Map Reduce- Exploring Map and Reduce Chapter-5
IV “Big Data”, DreamTech Press,
Functions- Techniques to optimize Map Reduce- Pages 121-133
2015 Edition.
Hardware/ Network Topology Synchronization- File
System- Uses of Map Reduce
Big Data Storage Technology – On-Disk Storage Devices Thomas Erl ,”Big Data
– Distributed File Systems, RDBMS Databases, NoSQL Fundamentals Concepts, Drivers Chapter- 7
V
Databases, New SQL Databases – In-Memory Storage and Techniques”, Pearson Pages: 145-179
Devices: In-Memory Data Grids, In-Memory Databases Education , First Edition,2016
Introduction to Big Data Analysis Techniques- Thomas Erl ,”Big Data
Quantitative Analysis – Qualitative Analysis – Data Fundamentals Concepts, Drivers Chapter-8
VI
Mining - Statistical Analysis - Machine Learning – and Techniques”, Pearson Pages: 181-205
Semantic Analysis – Visual Analysis. Education, First Edition,2016
,
