1.Design distributed systems using Hadoop.
2.Use HDFS and MapReduce for storing and analyzing data at scale.
3.Use Pig and Spark to create scripts to process data on a Hadoop cluster in more complex ways.
4.Utilize Hive and MySQL to analyze relational data.
5.Using HBase, Cassandra, and MongoDB to analyze non-relational data.
6.Query data with Drill, Phoenix, and Presto.
7.Choosing the right / appropriate data storage technology for your application
8.Learn how Hadoop clusters are managed by YARN, Tez, Mesos, Zookeeper, Zeppelin, Hue, and Oozie.
9.Publish data using Kafka, Sqoop, and Flume to your Hadoop cluster.
10.Consume streaming data using Spark Streaming.
1. Companies are willing to invest in hiring big data experts
2. big data certification course will fetch you a good salary
3. Predictive analysis will keep you ahead of your competitors
4. Big data allows you to diversify your revenue streams
5. Big data is important in the healthcare industry
6. Big data allows you to re-develop the products/services you are selling.
7. Google Trends, Google Finance, AWS public datasets, are examples.
1. Big Data Developer
2. Big Data Solution Architect
3. Big Data/Hadoop Test Engineers
4. Hadoop Administrator
5. Data Scientist
6. Big Data Analyst
7. Big Data Researcher
8. Big Data Manager
The average salary (Source)for Skill: Big Data Analytics Rs 1048K.
Overview of the hadoop ecosystem( Installation and history).
Diving into hadoop’s core: (HDFS and MapReduce).
HDFS: What it is and how it works.
Working with large datasets in HDFS using CLI.
MapReduce: What it is and how it works, distributes processing.
Programming with Pig:
Introduction to Pig.
Integrating with Hadoop.
Programming with Spark:
Introduction to Spark.
Working with Resilient Datasets.
Working with relational database.
Introduction to Hive.
2 Exercises with Hive.
Integrating MySQL with Hadoop.
Import Data from MySQL to Hadoop.
Working with non-relational database:
Introduction to NoSQL.
2 Exercises with HBase.
Cassandra Basics And Overview.
2 Exercises with Cassandra.
Integrating MongoDB with Spark.
Choosing the right database for the application requirements.
Using Drill, Phoenix and Presto for Data Querying:
Overview of Drill
2 Exercises for Drill.
Overview of Phoenix.
Integration with Pig.
Overview of Presto.
Integrating Cassandra and Hive with Presto.
Managing the cluster and performance using Yarn, Tez, Mesos, ZooKeeper, Oozie and Hue:
Other Technologies Available.
Setting up Kafka & Flume for Monitoring and Publishing Data:
2 Exercises for Monitoring logs & data.
Working with streams of data:
Spark Streaming Overview.
Apache Storm Overview
A dynamic and self - motivated Trainer and System Administrator. Aspiring for a Bright and challenging career in the field of Training and Networking Technology, which could enable me to upgrade myself with emerging trends and technologies to benefits of the professional growth and accomplishment of organizational goals.