1. Big Data

2. Technologies
 Apache HBase
 Apache Hive
 Apache Hadoop
 MapReduce
 Spring XD
 Apache Sqoop
 Apache Spark (Streaming/Mlib) using Java
 Apache Kafka
 Apache Solr
 Unix Shell Scripting
 Apache Zookeeper
 Apache Oozie
 Machine Learning

3. Projects/Proof of Concepts
 Import of structured big data to HBase using Hadoop MapReduce,SpringXD ,Sqoop etc.
 Import of unstructured big data to HDFS using Hadoop API, Spark Streaming, WebHDFS,SpringXD
etc.
 Realtime ingestion of structured & unstructured data from GreenPlum,Twitter etc onto HDFS using
Spark Streaming,SpringXD,Apache Kafka. Display of realtime ingestion graph using WebSockets API
on springboot application.
 Hive-HBase integration. This feature allows Hive QL statements to access HBase tables for both read
(SELECT) and write (INSERT). It is even possible to combine access to HBase tables with native Hive
tables via joins and unions.
 Big Data indexing & search(structured & unstructured) using Apache Solr
 Big Data Growth Monitoring and Charting using Hadoop API and SpringBoot.
 Big Data Compression and Encryption
 Hadoop MapReduce Job Scheduling and Tracking using Oozie,CronTab.
 Reusable component created for Apache Solr to self-restart and email notification on failure.
 Reusable component created to monitor status of SolrCloud using SpringBoot-AngularJs application
and to start/stop Solr Nodes and ZooKeeper
 Multi-Node SolrCloud creation and administration.

4. Projects/Proof of Concepts
 Apache Hadoop Installation on Windows 8.1 Operating System by building Hadoop Binaries from
source code using Maven
 Created Hadoop Cluster by integrating different machines running on windows and unix operating
systems and then used this cluster to run map-reduce jobs.
 Apache Spark Installation on top of Hadoop on Windows 8.1 Operating System.
 Machine Learning Algorithms(Linear Regression,Logistic Regression etc) implemented using Apache
Spark MLib