

CARREER OBJECTIVE
Enthusiastic Bigdata Hadoop Developer eager to contribute to team success through hard work, attention to detail and excellent organizational skills. Clear understanding of Bigdata and training in Hadoop. Motivated to learn, grow and excel in It Industry.
PROFILE SUMMARY
• Experienced Bigdata Hadoop Developer with over 2+ years of professional IT experience.
• Excellent Experience in Hadoop architecture and various components such as HDFS, MapReduce, Yarn, Spark sql, Spark Data frame and Datasets, Spark Streaming etc.
• Good Knowledge on Hadoop Cluster architecture and monitoring the cluster.
• Experience in managing and reviewing Hadoop log files.
• Implemented in setting up standards and processes for Hadoop based application design and implementation.
• Experience in importing and exporting data using Sqoop from HDFS to Relational Database Systems and vice-versa.
• Excellent reputation for resolving problems and improving customer satisfaction.
• Working Experience on Enterprise Hadoop like Cloudera.
• Experience in handing various failover e.g. Name node failover, Data node failover.
• Experience in various Real-Time Hadoop Issues & well acquainted with Problems solving.
• Experience tuning the cluster configuration for resources YARN.
• Good Understanding of Hadoop main components and its Architecture.
• Ability to handle and resolve complicated situations at work with ensured customer satisfaction.
• Requirement Gathering and Analysis, Managing, Tracking and Improving SLAs, Effort Estimation.
• Security deployment ensuring the Clients meets their Security requirements.
• Ability to adapt to evolving technology strong sense of responsibility and accomplishment.
Role and Responsibility:
Hadoop Distribution - Cloudera Manager
Hadoop Components - HDFS, YARN, HBASE, HIVE, SQOOP, Zookeeper, Hue, Spark SQL, Spark Data frame and Datasets Spark Streaming
Operating System - Linux, Red-hat, Cantos, Ubuntu and Windows
Cloud - Azure Data Bricks, Amazon Web Service (EC2, S3, EMR, Snapshot, AMI, IAM, CloudWatch, RDS)