SUMMARY

• Overall 9 Years of IT experience as Big Data analytics and Java developer in Confidential, Google, Scientific Games Corporation and different client in NTT DATA.
• Proficient with Bigdata related technologies like Hadoop frameworks, Map Reduce, Hive, HBase, PIG, Sqoop, Spark, Kafka, Flume, ZooKeeper, Oozie.
• Experience working in Microsoft Azure, Google Cloud environment(GCP) and integration of Big Data technologies with Azure and GCP.
• Good knowledge of No - SQL databases Cassandra, MongoDB and HBase.
• Having working experience on Cloudera Data Platform using VMware Player, Cent OS 6 Linuxc environment. Strong experience on Hadoop distributions like Cloudera, and HortonWorks.
• Have knowledge in AWS.
• Experienced in writing complex MapReduce programs that work with different file formats like Text, Sequence, Xml, JSON and Avro.
• Excellent Java development skills using J2EE, J2SE, Servlets, JSP, Spring, Hibernate, JDBC.
• Strong experience in writing complex queries for Oracle.
• Experience working with Build tools like Maven and Ant.
• Experienced in both Waterfall and Agile Development (SCRUM) methodologies
• Proficient with Agile PLM as Java Developer. Hands on experience on PC, PCM, PQM, PPM, Agile SDK, Process Extensions and Event based PXs
• Performed Data Flow Testing between Agile and Oracle EBS through Application Integration Architecture also known as D2R PIP
• Possess strong skills in Agile Development, testing and solution designing Agile SDK applications and working on Integration solution between Agile and Oracle Applications to AIAPIP.
• Deployed WebServices, URL based PXs in weblogic application.
• Basic functional understanding of SOA and Oracle Applications modules - Inventory, Engineering, Bill of Materials.
• Strong Debugging and problem solving skills with good understanding of system development methodologies, techniques and tools.
• Team player, highly productive and result-driven in team and individual projects, strong research, time management with good interpersonal and communication skills.
• Flexible enough to adapt new technologies.
• Outstanding Interpersonal and Communication Skills and Self-Starter.
• Ability to deliver high quality stuff very efficiently on time.

TECHNICAL SKILLS

Hadoop Technologies: HDFS, YARN, MapReduce, Hive, Pig, Sqoop, Flume, Spark, Kafka, Zookeeper, and Oozie

NO SQL Databases: HBase, Cassandra, MongoDB

Cloud Technologies: Microsoft Azure, Google Cloud Computing, Cloudera

Languages: Java, Scala, SQL, PL/SQL, Pig Latin, HiveQL, Unix, Shell Scripting

Java & J2EE: Core Java, Servlets, Hibernate, Spring, Structs

Web Technologies: HTML, JavaScript, XML

Databases: MySQL, Oracle

IDE: Eclipse, Tomcat

Application Server: Weblogic, JBoss, Tomcat

PLM Technology: Oracle Agile PLM

Operating System: UNIX, Windows, LINUX

PROFESSIONAL EXPERIENCE

Confidential

Spark Developer

Responsibilities:

• During the initial Phase we loaded the data from legacy (.net) database to Azure Cosmos DB using MongoSpark API without applying the initial rules i.e. by not applying the Price and promo for a particular product using Mongo API.
• During the next Phase we loaded the data into Azure Cosmos DB using MongoSpark API with applying the rules by applying the Price and promo for a particular product using Mongo API.
• During the next Phase we get the data from kafka to Azure Cosmos DB Collections using Spring boot and from Cosmos Collections we use MongoSpark API with applying the rules for the Price and promo for a particular product using Mongo Spark API.
• Now the data that is available in Cosmos Collection will be called from external team and this products are queried online in Confidential Website to many Stores located.
• Now the data that is available in Cosmos Collection will be called from external team and this products are queried online in Confidential Website to many Stores that are located.
• Worked on POC part on Kafka - Spark Streaming - Mongo and also Mongo-Spark Streaming-kafka to push the prices and promo in HDI Insight Server
• Comparing the performance part of MongoSpark and CosmosSql approach to load the data into Cosmos Collections. Used Custom MongoSpark connector jar with MongoStringPartitioner approach to load millions of data based on different input collections.
• Performance tuning using the different approaches Using MongoSpark connector and also mongo related queries into the development into Cosmos DB.
• Worked on POC Part on Azure Databricks by developing the existing batch Jobs and spark streaming(Structured Streaming)

Google

Big Data Developer

Responsibilities:

• Architecting, managing and delivering the technical projects/products for various business groups.
• All the data was loaded from our relational DBs to HIVE using Sqoop. We were getting four flat files from different vendors. These were all in different formats e.g. text, EDI and XML formats.
• Architected all the ETL data loads coming in from the source system and loading into the data warehouse
• Created Hive External tables to stage data and then move the data from Staging to main tables
• Implemented Installation and configuration of multi - node cluster on Cloud using Google cloud platform.
• Experienced in working with Apache Storm.
• Utilized Oozie workflow to run Pig and Hive Jobs Extracted files from Mongo DB through Sqoop and placed in HDFS and processed.
• Migrated large volume of PB data warehouse data to HDFS.
• Utilize GCP services with focus on big data Architect /analytics / enterprise data warehouse and business intelligence solutions to ensure optimal architecture, scalability, flexibility, availability, performance, and to provide meaningful and valuable information for better decision-making.
• Experience in data cleansing and data mining.
• Worked on tools Flume, Storm and Spark.
• Proof-of-concept to determine feasibility and product evaluation of Big Data products
• Writing Hive join query to fetch info from multiple tables, writing multiple Map Reduce jobs to collect output from Hive
• Involved in migration of data from existing RDBMS (oracle and SQL server) to Hadoop using Sqoop for processing data.
• Used Flume to collect, aggregate, and store the web log data from different sources like web servers, mobile and network devices and pushed to HDFS.
• Continuous monitoring and managing the Hadoop cluster in GCP.
• Worked on configuring and managing disaster recovery and backup on Cassandra Data.
• Developed Spark jobs to transform the data in HDFS.
• Analyzed large amounts of data sets to determine optimal way to aggregate and report on it.
• Used Hive to analyze data ingested into HBase by using Hive-HBase integration and compute various metrics for reporting on the dashboard
• Involved in developing Map-reduce framework, writing queries scheduling map-reduce
• Developed the code for Importing and exporting data into HDFS and Hive using Sqoop
• Installed and configured Hadoop and responsible for maintaining cluster and managing and reviewing Hadoop log files.
• Developed Shell, Perl and Python scripts to automate and provide Control flow to Pig scripts.

Environment: Hadoop, HIVE, HDFS, HBASE, Data Modeling, MapReduce (MRv1, MRv2), R-language, Python, Zookeeper, Sqoop, Oozie, ETL, Cassandra and Teradata and TALEND Big Data