● Learn job critical skills like Big Data & Hadoop frameworks, and leverage the functionality of AWS services.
● The Big Data Engineer course helps you learn to use the database management tool and MongoDB via
interactive sessions & projects.
● Understand Big Data and its analytics in the real world.
● Analyze the Big Data framework like Hadoop and NoSQL to efficiently store and process Big Data to generate
analytics.
● Design of Algorithms to solve Data-Intensive Problems using the Map Reduce Paradigm.
● Design and Implementation of Big Data Analytics using pig and spark to solve data-intensive problems and
generate analytics.
● Design and Implementation of Big Data Analytics using pig and spark to solve data-intensive problems and
generate analytics.
● Implement Big Data Activities using Hive.
Beneficial for - Students of UG/PG Programs & Research Scholars of Information Technology and Computer Science,
etc.
Module-I Introduction to BIG DATA |
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---|---|---|
S.No. | Topics | Duration |
01 | Introduction to Big data | |
02 | The three Vs of big data | |
03 | The value—and truth—of big data | |
04 | The history of big data | 5 days |
05 | Big data use cases | |
06 | Big data challenges | |
07 | How big data works |
Module-II Introduction to Enabling Technologies for Big Data |
||
---|---|---|
S.No. | Topics | Duration |
01 | Predictive Analytics | |
02 | NoSQL Databases | |
03 | Knowledge Discovery Tools | |
04 | Stream Analytics, In-memory Data Fabric | |
05 | Distributed Storage | 5 days |
06 | Data Virtualization | |
07 | Data Integration | |
08 | Data Preprocessing | |
09 | Data Quality |
Module-III Introduction to Big Data Platforms |
||
---|---|---|
S.No. | Topics | Duration |
01 | Apache Hadoop, Apache storm | |
02 | Cloudera | |
03 | Amazon Web Services | 5 days |
04 | Oracle | |
05 | Snowflake |
Module-IV Big Data and Analytics |
||
---|---|---|
S.No. | Topics | Duration |
01 | Classification of Digital Data | |
02 | Structured and Unstructured Data | |
03 | Data Warehouse | 5 days |
04 | Environment Big Data Analytics | |
05 | Classification of Analytics | |
06 | Big Data Analytics importance |
Module-V Introduction to Mongodb and Mapreduce |
||
---|---|---|
S.No. | Topics | Duration |
01 | Why Mongo DB | |
02 | Terms used in RDBMS and Mongo DB | |
03 | Data Types | 5 days |
04 | MongoDB Query Language | |
05 | Introduction to Big Data |
Module-VI Introduction to Mapreduce |
||
---|---|---|
S.No. | Topics | Duration |
01 | Introduction to Mapreduce | |
02 | Mapper- Reducer | |
03 | Combiner - Partitioner | 5 days |
04 | Searching | |
05 | Sorting | |
06 | Compression |
Module-VII Introduction to Hive and Pig | ||
---|---|---|
S.No. | Topics | Duration |
01 | Introduction to Hive | |
02 | Introduction to Architecture | |
03 | File Formats | |
04 | Hive Query Language Statements | 5 days |
05 | Partitions – Bucketing – Views | |
06 | Features – Philosophy - Use Case for Pig - Pig Latin Overview - Pig Primitive Data Types |
|
07 | Complex Data Types - Piggy Bank - User-Defined Functions - Parameter Substitution – Diagnostic |
|
08 | Example using Pig Technology |
Module-VIII Introduction to Data Analytics with R |
||
---|---|---|
S.No. | Topics | Duration |
01 | Introduction to Machine Learning | |
02 | Supervised Learning | |
03 | Unsupervised Learning | |
04 | Regression Model | |
05 | Clustering | 10 days |
06 | Collaborative Filtering | |
07 | Associate Rule Making | |
08 | Decision Tree | |
09 | Big Data Analytics with BigR |