Module 1: Hadoop: Master your Big data
This module will help the candidate to Learn the skills on how to master big data, that is Big data touch, Getting started: Hadoop, Hadoop framework: Stepping into Hadoop, HDFS: What and Why?, Working on HDFS, Hadoop 2. x -
- A Programming paradigm
- Closer look to Map reduce
- Practical approach to Map reduce
- Hadoop 1.x vs Hadoop 2.x
- Hadoop 3.x
- Quiz Module 1
Module 2: Hive: Big data SQL
This module will equip the candidate with the knowledge of Apache hive that is Teasing the Honey bee & Hive Illustration like External tables in hive, Loading different file formats
- Hive illustration: Loading data into Hive tables
- Hive illustration: Simple Operations on Hive table
- Hive illustration: Query Operations on Hive table
- Hive illustration: Querying complex structures
- Hive illustration: Views
- Quiz Module 2
Module 3: Spark: Stream and analyze the big data
This module helps the learner to understand Spark Basics, Face to face, Spark - Architecture, RDDs - Building blocks of Spark, Spark Terminologies & also builds the ability of how to go with Case study.
- Spark and Hadoop - Face to face
- Spark - Architecture
- RDDs - Building blocks of Spark
- RDDs continued
- Spark Terminologies
- Pyspark - Getting hands dirty
- Spark - MLlib
- Pyspark - Clustering
- Music data - Study the case - 01
- Music data - Study the case - 02
- Music data - Study the case - 03
- Spark streaming and Real time data analytics
- Spark streaming Architecture
- Real-time Data Analysis on Twitter Data : Demo
- Quiz Module 3
Module 4: Apache Kafka - A distributed streaming platform
This module will contribute to understanding of your concept on Kafka - What and Where?
- Kafka - Key components_Broker_Producer
- Kafka - Key components_Topics_Partitions
- Kafka - Key components_Consumer_Replicas
- Kafka - APIs and Clusters
- More fun with Kafka
- Zookeeper - Basic principles
- Live Kafka demo with Twitter
- Quiz Module 4
Module 5: Advanced Spark
This module will equip the candidate with the knowledge of Configure the Spark, Spark Properties, Performance Tuning and Data serialization
- Memory tuning
- Garbage collection
- Memory usage and levels of parallelism
- Data locality and broadcasting
- Job scheduling
- Modes in cluster management
- Dynamic resource allocation
- Decommission of executors
- Application schedule
- Quiz Module 5
Module 6: Complementary Module 1: Soft Skills Development
- Business Communication
- Preparation for the Interview
- Presentation Skills
Module 7: Complimentary Module 2: Resume Writing
- Resume Writing

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