SUMMARY

• Around 9 years of professional IT experience in Bigdata Environment, Hadoop Ecosystem and good experience in Spark, SQL, Java Development.
• Hands on experience across Hadoop Eco System that includes extensive experience in Big Data technologies like HDFS, MapReduce, YARN, Spark, Sqoop, Hive, Pig, Impala, Oozie, Oozie Coordinator, Zoo - Keeper and Apache Cassandra, HBase.
• Experience in using various tools like Sqoop, Flume, Kafka, NiFi and Pig to ingest structured, semi-structured and unstructured data into the cluster.
• Proficient with Apache Spark ecosystem such as Spark, Spark Streaming using Scala and Python.
• Designing both time driven and data driven automated workflows using Oozie and used Zookeeper for cluster co-ordination.
• Experience in Hadoop cluster using Cloudera’s CDH, Hortonworks HDP.
• Developed highly optimized Spark applications to perform various data cleansing, validation, transformation and summarization activities according to the requirement.
• Data pipeline consists Spark, Hive and Sqoop, and custom build Input Adapters to ingest, transform and analyze operational data.
• Developed Spark jobs and Hive Jobs to summarize and transform data.
• Involved in converting Hive/SQL queries into Spark transformations using Spark Data Frames and Python.
• Experience in working with structured data using HiveQL, join operations, Hive UDFs, partitions, bucketing and internal/external tables.
• Expertise in writing MapReduce Jobs in Java, Python for processing large sets of structured semi-structured and unstructured data sets and stores them in HDFS.
• Experience working with Python, UNIX and shell scripting.
• Experience in Extraction, Transformation and Loading (ETL) of data from multiple sources like Flat files and Databases.
• Good knowledge of cloud integration with AWS using Elastic MapReduce (EMR), Simple Storage Service (S3), EC2, Redshift and Microsoft Azure.
• Experience with complete Software Development Life Cycle (SDLC) process which includes Requirement Gathering, Analysis, Designing, Developing, Testing, Implementing and Documenting.
• Hands on Experience in Spark architecture and its integrations like Spark SQL, Data Frames and Datasets APIs.
• Worked on Spark for enhancing the executions of current processing in Hadoop utilizing Spark Context, Spark SQL, Data Frames and RDD’s.
• Involved in converting Hive/SQL queries into Spark transformations using Spark RDDs, Spark SQL and Python.
• Hands on experience Using Hive Tables by Spark, performing transformations and Creating Data Frames on Hive tables using Spark.
• Used Spark-Structured-Streaming to perform necessary transformations.
• Expertise in converting MapReduce programs into Spark transformations using Spark RDD's
• Strong understanding of AWS components such as EC2 and S3
• Performed Data Migration to GCP
• Experience in Implementing Continuous Delivery pipeline with Maven, Ant, Jenkins and AWS.
• Exposure to CI/CD tools - Jenkins for Continuous Integration, Ansible for continuous deployment.
• Worked with waterfall and Agile methodologies.

TECHNICAL SKILLS

Big data Technologies: HDFS, Map Reduce, Pig, Hive, Sqoop, Oozie, Scala, Spark, Kafka, Nifi, Airflow, Flume, Snowflake, Ambari, Hue

HadoopFrameworks: Cloudera CDHs, Hortonworks HDPs, MAPR

Database: Oracle 10g/11g, PL/SQL, MySQL, MS SQL Server 2012, DB2

Language: C, C++, Java, Scala, Python

AWS Components: IAH, S3, EMR, EC2,Lambda, Route 53, Cloud Watch, SNS

Methodologies: Agile, Waterfall

Build Tools: Maven, Gradle, Jenkins.

Databases: NO-SQL, HBase, Cassandra, MongoDB, DynamoDB

IDE Tools: Eclipse, Net Beans, Intellij

Modelling Tools: Rational Rose, Star UML, Visual paradigm for UML

BI Tools: Tableau

Operating System: Windows 7/8/10, Vista, UNIX, Linux, Ubuntu, Mac OS X

PROFESSIONAL EXPERIENCE

Confidential - Santa Clara, CA

Senior Big Data Developer

Responsibilities:

• Experienced in writing the Spark Streaming and Structured Streaming Applications in Python and Java
• Exposure on usage of Apache Kafka develop data pipeline of logs as a stream of messages using producers and consumers.
• Hands on experience in customizing Splunk dashboards, visualizations, configurations using customized Splunk queries.
• Developed MapReduce programs to cleanse the data in HDFS obtained from heterogeneous data sources. Processed metadata files into AWS S3 and Elastic search cluster.
• Involvedin designing, building and managing applications to process large amounts of data in a DevOps (AWS) ecosystem
• Developed custom aggregate functions usingSparkSQL and performed interactive querying.
• Populated HDFS and HBase with huge amounts of data using Apache Kafka.
• Work on Migration on data to AWS for having an efficient pipeline.
• Writing Splunk Queries, Expertise in searching, monitoring, analyzing and visualizing Splunk logs.
• CSV files using Spark/PySpark. Experience in DevelopingSparkapplications usingSpark - SQLinDatabricksfor data extraction, transformation and aggregation from multiple file formats for analyzing & transforming the data to uncover insights into the customer usage patterns. Deploying Spark jobs in Amazon EMR and running the job on AWS clusters.
• Developed MapReduce programs to cleanse the data in HDFS obtained from heterogeneous data sources. Processed metadata files into AWS S3 and Elastic search cluster.
• Created monitoring jobs and setup alerts and dashboards using the Kapacitor and Grafana to monitor the performance of the pipelines.
• Setting up Airflow DAGs to trigger the jobs with a scheduled interval.
• Worked with automation and orchestration tools like Jenkins, Docker and Kubernetes to set up a bootstrap for the pipelines.
• Deployed new version changes of data schema produced by Kafka and processed them using the Cassandra and Oracle.
• Loaded data into the cluster from dynamically generated files using Flume
• Implement POC with Hadoop. Extract data with Spark into HDFS.
• Writing SQL queries to analyze data to solve subscription payments of customers.
• Extract Transform and Load data from Sources Systems to Azure Data Storage services using a combination of Azure Data Factory, T-SQL, Spark SQL and U-SQL Azure Data Lake Analytics . Data Ingestion to one or more Azure Services - (Azure Data Lake, Azure Storage, Azure SQL, Azure DW) and processing the data in InAzure Databricks.
• Created Pipelines in ADF using Linked Services/Datasets/Pipeline/ to Extract, Transform and load data from different sources like Azure SQL, Blob storage, Azure SQL Data warehouse, write-back tool and backwards.
• Developed Spark applications usingPysparkandSpark-SQLfor data extraction, transformation and aggregation from multiple file formats for analyzing & transforming the data to uncover insights into the customer usage patterns.

Environment: Java, Hadoop, Hive, Pig, Sqoop, Scala, Kafka, Scala, Flume, HBase, Python, Nifi, Pyspark, AWS, Oracle 10g/11g/12C, Cassandra, GCP, HDFS, Data Lake, Spark, MapReduce, Ambari, Cloudera, Tableau, Snappy, Zookeeper, NoSQL, Shell Scripting, Ubuntu, Solar, Docker, Kubernetes, Jenkins

Confidential - Baltimore, MD

Senior Big Data Developer

Responsibilities:

• Experienced in writing Spark Applications in Scala and Python.
• Analyzed large and critical datasets using Cloudera, HDFS, HBase, MapReduce, Hive, Hive UDF, Pig, Sqoop, Zookeeper and Spark.
• Analyzed the SQL scripts and designed the solution to implement using Pyspark.
• Used Kafka consumer’s API in Scala for consuming data from Kafka topics
• Designed and implemented MapReduce based large-scale parallel relation-learning system.
• Implemented Apache Airflow for authoring, scheduling and monitoring Data Pipelines
• Developed Python code to gather the data from HBase (Cornerstone) and designs the solution to implement usingPySpark.
• Implemented Kafka model which pulls the latest records into Hive external tables.
• Imported data into HDFS from various SQL databases and files using Sqoop and from streaming systems using Storm into Big Data Lake.
• Loaded all datasets into Hive from Source CSV files using spark and Cassandra from Source
• Implemented a Continuous Delivery pipeline with Docker, and Git Hub and AWS.
• Created and deployed data dashboards on docker to visualize the inquiries querying in SQL to retrieve data as per client inquiries providing daily reports using production data and experimental analysis using development data.
• Wrote Pig scripts to store the data into HBase
• Exported the analyzed data to Teradata using Sqoop for visualization and to generate reports for the BI team.
• Google Cloud Platform (Google Cloud Storage, Big Query, Big Table, Cloud SQL, Pub/Sub) SQL Data Integration and Hadoop Developer roles and responsibilities.
• Experience in building and architecting multiple Data pipelines, end to end ETL and ELT process for Data ingestion and transformation in GCP and coordinate task among the team.
• Spark Streaming collects this data from Kafka in near-real-time and performs necessary transformations and aggregation on the fly to build the common learner data model and persists the data in NoSQL store (HBase). Migrated the computational code in HQL toPySpark. Responsible for estimating the cluster size, monitoring and troubleshooting of the Spark databricks cluster.
• Completed data extraction, aggregation and analysis in HDFS by usingPySparkand store the data needed to Hive.
• Worked on Google Cloud Platform Services like Vision API, Instances.
• Exposure on usage of Apache Kafka develop data pipeline of logs as a stream of messages using producers and consumers.
• Experienced in fact dimensional modeling (Star schema, Snowflake schema), transactional modeling and SCD (Slowly changing dimension)
• Written Complex MapReduce programs.
• Installed and configured Hive and also written Hive UDFs.
• Involved in HDFS maintenance and administering it through Hadoop-Java API
• Loaded data into the cluster from dynamically generated files using Flume and from RDBMS using Sqoop.
• Writing Splunk Queries, Expertise in searching, monitoring, analyzing and visualizing Splunk logs.
• Experience in alert handling, standard availability and performance report generation. Experience in root cause analysis of post-production performance related issues through Splunk tool.
• Designing, optimizing and executing Splunk-based enterprise solutions.
• Sound knowledge in programming Spark using Scala.
• Installed and configured Splunk Universal Forwarders on both UNIX (Linux, Solaris, and AIX) and Windows Servers.
• Hands on experience in customizing Splunk dashboards, visualizations, configurations using customized Splunk queries.
• Monitored the Splunk infrastructure for capacity planning, scalability, and optimization.
• Experienced in using Splunk- DB connects for real-time data integration between Splunk Enterprise and rests all other databases.
• Involved in writing Java API’s for interacting with HBase
• Involved in writing Flume and Hive scripts to extract, transform and load data into Database
• Used HBase as the data storage
• Installed and configured Hadoop MapReduce, HDFS, developed multiple MapReduce jobs in java for data cleaning and pre-processing.
• Experienced in Importing and exporting data into HDFS and Hive using Sqoop.
• Participated in development/implementation of Cloudera Hadoop environment.
• Used Amazon web services (AWS) like EC2 and S3 for small data sets.
• Experienced in performance tuning of Spark Applications for setting right Batch Interval time, correct level of Parallelism and memory tuning.
• To meet specific business requirements wrote UDF’s inScalaandPyspark.
• Developed JSON Scripts for deploying the Pipeline in Azure Data Factory (ADF) that process the data using the Sql Activity.
• Hands-on experience on developing SQL Scripts for automation purpose.
• Implemented AWS services to provide a variety of computing and networking services to meet the needs of applications
• Experienced in running query using Impala and used BI tools to run ad-hoc queries directly on Hadoop.
• Installed and configured Hive and also written Hive UDFs and Used MapReduce and Junit for unit testing.
• Experienced in working with various kinds of data sources such as Teradata and Oracle. Successfully loaded files to HDFS from Teradata, and load loaded from HDFS to Hive and Impala.
• Installed Oozie workflow engine to run multiple Hive and Pig jobs which run independently with time and data availability.
• Developed MapReduce programs to parse the raw data, populate staging tables and store the refined detain partitioned tables in the EDW.
• Strong Experience in implementing Data warehouse solutions in Amazon web services (AWS) Redshift; Worked on various projects to migrate data from on premise databases to AWS Redshift, RDS and S3.
• Leveraged cloud and GPU computing technologies for automated machine learning and analytics pipelines, such as AWS, GCP
• Monitored and managed the Hadoop cluster using Apache Ambary
• Involved in implementing security on Hortonworks Hadoop Cluster using with Kerberos by working along with operations team to move non secured cluster to secured cluster.

Environment: Java, Hadoop, Hive, Pig, Sqoop, Scala, Kafka, Scala, Flume, HBase, Python, Nifi, Pyspark, AWS, Hortonworks, Oracle 10g/11g/12C, Teradata, Cassandra, GCP, HDFS, Data Lake, Spark, MapReduce, Ambari, Cloudera, Tableau, Snappy, Zookeeper, NoSQL, Shell Scripting, Ubuntu, Solar.

Confidential - Denver, CO

Big Data Developer

Responsibilities:

• Developed real time data processing applications by using Scala and Python and implemented Apache Spark Streaming from various streaming sources like Kafka and JMS.
• Knowledge on Pyspark and used Hive to analyze sensor data and cluster users based on their behavior in the events.
• Experienced in writing live Real-time Processing and core jobs using Spark Streaming with Kafka as a data pipe-line system.
• Developed Shell, Perl and Python scripts to automate and provide Control flow to Pig scripts.
• Worked on Amazon AWS concepts like EMR and EC2 web services for fast and efficient processing of Big Data.
• Involved in loading data from Linux file systems, servers, java web services using Kafka producers and partitions.
• Develop and deploy the outcome using spark code in Hadoop cluster running on GCP.
• Applied Kafka custom encoders for custom input format to load data into Kafka Partitions.
• Implement POC with Hadoop. Extract data with Spark into HDFS.
• Used Spark SQL with Scala for creating data frames and performed transformations on data frames.
• Used Spark API over Cloudera Hadoop YARN to perform analytics on data in Hive.
• Developed code to read data stream from Kafka and send it to respective bolts through respective stream.
• Worked on Google Cloud Platform Services like Vision API, Instances.
• Involvedin designing, building and managing applications to process large amounts of data in a Hadoop/DevOps (GCP) ecosystem
• Developed Spark applications using Scala for easy Hadoop transitions.
• Implemented applications with Scalaalong with Akka and Play framework.
• Optimized the code using Pyspark for better performance
• Worked on Spark streaming using Apache Kafka for real time data processing.
• Developed MapReduce jobs using MapReduce Java API and HIVEQL.
• Developed UDF, UDAF, UDTF functions and implemented it in HIVE Queries.
• Developing Scripts and Batch Job to schedule a bundle (group of coordinators) which consists of various Hadoop Programs using Oozie.
• Experienced in optimizing Hive queries, joins to handle different data sets.
• Involved in ETL, Data Integration and Migrationby writing Pig scripts.
• Developed MapReduce programs to cleanse the data in HDFS obtained from heterogeneous data sources. Processed metadata files into AWS S3 and Elastic search cluster.
• Integrated Hadoop with Solr and implement search algorithms.
• Experience in Storm for handling real-time processing.
• Hands on Experience working in Hortonworks distribution.
• Worked hands on No-SQL databases like MongoDB for POC purpose in storing images and URIs.
• Designed and implemented MongoDB and associated RESTful web service.
• Involved in writing test cases and implement test classes using MRUnit and mocking frameworks.
• Developed Sqoop scripts to extract the data from MYSQL and load into HDFS.
• Very capable at using AWS utilities such as EMR, S3 and Cloud watch to run and monitor Hadoop/Spark jobs on AWS.
• Experience in processing large volume of data and skills in parallel execution of process usingTalendfunctionality.
• Created multi-node Hadoop and Spark clusters in AWS instances to generate Terabytes of data and stored it in AWS HDFS.
• UsedTalendtool to create workflows for processing data from multiple source systems.

Environment: Map Reduce, HDFS, Sqoop, Java, Pyspark, LINUX, Oozie, GCP, Hadoop, Pig, Hive, Solr, Spark Streaming, Kafka, Storm, Spark, Scala, Akka, Python, MongoDB, Hadoop Cluster, Amazon Web Services, Talend.

Confidential - Detroit, MI

Sr. Big Data Admin/Developer

Responsibilities:

• Worked directly with the Big Data Architecture Team which created the foundation of this Enterprise Analytics initiative in a Hadoop-based Data Lake.
• Created multi-node Hadoop and Spark clusters in AWS instances to generate terabytes of data and stored it in AWS HDFS.
• Installed/Configured/Maintained Apache Hadoop clusters for application development and Hadoop tools like Hive, Pig, HBase, Zookeeper and Sqoop.
• Extracted real time feed using Kafka and Spark streaming and convert it to RDD and process data in the form of Data Frame and save the data as Parquet format in HDFS.
• Developed data pipeline using Flume, Sqoop, Pig and Java MapReduce to ingest customer behavioral data and financial histories into HDFS for analysis.
• Involved in collecting and aggregating large amounts of log data using Apache Flume and staging data in HDFS for further analysis.
• Upgraded the Hadoop cluster from CDH4.7 to CDH5.2 and worked on installing cluster, commissioning & decommissioning of Data Nodes, NameNode recovery, capacity planning, and slots configuration.
• Developed Spark scripts to import large files from Amazon S3 buckets and imported the data from different sources like HDFS/Hbase into SparkRDD.
• Involved in converting Hive/SQL queries into Spark transformations using Spark RDD, Scala and Python.
• Involved in migration of ETL processes from Oracle to Hive to test the easy data manipulation and worked on importing and exporting data from Oracle and DB2 into HDFS and HIVE using Sqoop.
• Worked on Installing Cloudera Manager, CDH and install the JCE Policy File to Create a Kerberos Principal for the Cloudera Manager Server, enabling Kerberos Using the Wizard.
• Developed Spark jobs using Scala and Python on top of Yarn/MRv2 for interactive and Batch Analysis.
• Monitored cluster for performance and, networking and data integrity issues and responsible for troubleshooting issues in the execution of MapReduce jobs by inspecting and reviewing log files.
• Created 25+ Linux Bash scripts for users, groups, data distribution, capacity planning, and system monitoring.
• Install OS and administrated Hadoop stack with CDH5 (with YARN) Cloudera distribution including configuration management, monitoring, debugging, and performance tuning.
• Developed and analyzed the SQL scripts and designed the solution to implement using Pyspark
• Developed a data pipeline using Kafka, Spark and Hive to ingest, transform and analysing data.
• Supported MapReduce Programs and distributed applications running on the Hadoop clusterand scripting Hadoop package installation and configuration to support fully-automated deployments.
• Migrated existing on-premises application to AWS and used AWS services like EC2 and S3 for large data sets processing and storage and worked with ELASTIC MAPREDUCE and setupHadoopenvironment in AWS EC2 Instances.
• Worked with systems engineering team to plan and deploy new Hadoop environments and expand existing Hadoop clusters.
• Installed, Configured, Maintained, Tuned and Supported Splunk Enterprise Server 6.0 and Splunk Universal Forwarder 6.0.
• Administered a complex cluster-based environment involving search heads in a cluster while the indexers are in standalone mode.
• Configured Splunk forwarder to send unnecessary log events to "Null Queue" using props and transforms configurations.
• Created and configured management reports and dashboards in Splunk for application log monitoring.
• Active monitoring of Jobs through alert tools and responding with certain action to logs analyses the logs and escalate to high level teams on critical issues.
• Responsible for developing Splunk queries and dashboards targeted at understanding application performance and capacity analysis.
• Extensive experience on setting up the Splunk to monitor the customer volume and track the customer activity.
• Perform maintenance, monitoring, deployments, and upgrades across infrastructure that supports all our Hadoop clustersand worked on Hive for further analysis and for generating transforming files from different analytical formats to text files.
• Created Hive External tables and loaded the data in to tables and query data using HQL and worked with application teams to install operating system, Hadoop updates, patches, version upgrades as required.
• Worked and learned a great deal from AWS Cloud services like EC2, S3, EBS, RDS and VPC.
• Monitoring Hadoop cluster using tools like Nagios, Ganglia, and Cloudera Manager and maintaining the Cluster by adding and removing of nodes using tools like Ganglia, Nagios, and Cloudera Manager.
• Worked on Hive for exposing data for further analysis and for generating transforming files from different analytical formats to text files.
• Worked on migrating MapReduce programs into Spark transformations using Spark and Scala, initially done using python (PySpark)
• Experienced in Cauterize NiFi Pipeline on EC2 nodes integrated with Spark, Kafka, Postgres

Environment: Hadoop, MapReduce, Python, Hive, PIG, Sqoop, Python, Spark, Spark-Streaming, Spark SQL, AWS EMR, AWS S3, AWS Redshift, Python, Scala, Pyspark, MapR, Java, Oozie, Flume, HBase, Nagios, Ganglia, Hue, Hortonworks, Cloudera Manager, Zookeeper, Cloudera, Oracle, Kerberos and RedHat 6.5

Confidential

JAVA/Hadoop/Spark Developer

Responsibilities:

• Involved in Requirements Analysis and design an Object-oriented domain model
• Implemented test scripts to support test driven development and continuous integration
• Experience in Importing and exporting data into big data, HDFS and Hive using Sqoop
• Developed MapReduce programs to clean and aggregate the data
• Exploring with Spark for improving the performance and optimization of the existing algorithms inHadoopusing Spark Context, Spark-SQL, Data Frame, and Pair RDD's.
• Development of new listeners for producers and consumer for both Rabbit MQ and Kafka
• Used Micro service with Spring Boot interacting through a combination of REST and Apache Kafka message brokers.
• Involved in writing queries in SparkSQL using Scala. Worked with Splunk to analyze and visualize data.
• Worked in complete SDLC phase like Requirements, Specification, Design, Implementation and Testing
• Developed Spring and Hibernate data layer components for application
• Developed profile view web pages add, edit using HTML, CSS, JQuery, Java Script
• Developed the application by using MAVEN script
• Developed the mechanism for logging and debugging with Log4j
• Involved in developing database tractions through JDBC
• Used GIT for version control
• Responsible for troubleshooting issues in the execution of MapReduce jobs by inspecting and reviewing log files.
• Used oracle as Database and used load for queries execution and involved in writing SQL scripts, PL/SQL code for procedures and functions
• Developed Front-end applications which will interact the mainframe applications using J2C connectors
• Hands on experience in exporting the results into relational databases using Sqoop for visualization and to generate reports for BI team
• Designing, Development and implementation of JSPs in presentation layer for submission, Application, reference implementation
• Deployed Web, presentation and business components on Apache Tomcat Application Server.
• Involvement in post-production support, Testing and used JUNIT for unit testing of the module
• Worked in Agile methodology

Environment: HDFS, Hive, Sqoop, Java, Core Java, Maven, HTML, CSS, Java Script, GIT, Map R, JUNIT, Agile, Log4j, SQL, Agile.

Confidential

Java Developer

Responsibilities:

• Developed multiple MapReduce jobs in Java for data cleaning and preprocessing and assisted with data capacity planning and node forecasting.
• Involved in design and ongoing operation of several Hadoop clusters and Configured and deployed Hive Meta store using MySQL and thrift server
• Implemented and operated on-premises Hadoop clusters from the hardware to the application layer including compute and storage.
• Uploaded and processed more than 30 terabytes of data from various structured and unstructured sources into HDFS (AWS cloud) using Sqoop and Flume.
• Designed custom deployment and configuration automation systems to allow for hands-off management of clusters via Cobbler, FUNC, and Puppet.
• Prepared complete description documentation as per the Knowledge Transferred about the Phase-II TalenD Job Design and goal and prepared documentation about the Support and Maintenance work to be followed in TalenD.
• Deployed the company's first Hadoop cluster running Cloudera's CDH2 to a 44-node cluster storing 160TB and connecting via 1 GB Ethernet.
• Debug and solve the major issues with Cloudera manager by interacting with the Cloudera team.
• Modified reports and TalenD ETL jobs based on the feedback from QA testers and Users in development and staging environments.
• Handled importing other enterprise data from different data sources into HDFS using Sqoop and performing transformations using Hive, MapReduce and then loading data into HBase tables.
• Involved in Cluster Maintenance and removal of nodes using Cloudera Manager.
• Collaborated with application development teams to provide operational support, platform expansion, and upgrades for Hadoop Infrastructure including upgrades to CDH3.
• Participated in Hadoop development Scrum and installed, Configured Cognos8.4/10 and TalenDETL on single and multi-server environments.

Environment: Apache Hadoop, Cloudera, Pig, Hive, TalenD, Map-reduce, Sqoop, UNIX, Cassandra, Java, LINUX, Oracle 11gR2, UNIX Shell Scripting, Kerberos

Confidential

Junior Developer

Responsibilities:

• Implemented Multi-Threaded Environment and used most of the interfaces under the collection framework by using CoreJavaConcepts.
• Developed Graphical User Interfaces by using JSF, JSP, HTML, DHTML, Angularjs, CSS, and JavaScript and developed scripts inpythonfor Financial Data coming from SQLDeveloperbased on the requirements specified.
• Implemented severalJava/J2EE design patterns like Spring MVC, Singleton, Spring Dependency Injection and Data Transfer Object.
• Used JAX-WS (SOAP) for producing web services and involved in writing programs to consume the web services using SOA with CXF framework and developed few web pages using JSP, JSTL, HTML, CSS,Javascript, Ajax and JSON.
• Implemented business logic, data exchange, XML processing and created graphics usingPython and Django.
• Wrote code to fetch data from Web services using JQUERY AJAX via JSON response and updating the HTML pages and developed high traffic web applications using HTML, CSS, and JavaScript, jQuery, Bootstrap, ExtJS, AngularJS, Node.js and react.js.
• Write SQL queries and create PL/SQL functions/procedures/packages that are optimized forAPEX and improve performance and response times ofAPEXpages and reports
• Used JQuery library, NodeJS and AngularJS for creation of powerful dynamic WebPages and web applications by using its advanced and cross browser functionality.
• UsedJavaServer Pages for content layout and presentation with Python and Extracted and loaded data usingPythonscripts and PL/SQL packages
• Worked with various frameworks of JavaScript like BackboneJS, AngularJS, and EmberJS etc.
• Written with object-orientedPython, Flask, SQL, Beautiful Soup, httplib2, Jinja2, HTML/CSS, Bootstrap, jQuery, Linux, Sublime Text, GIT.
• Developed GUI using JSP, Struts, HTML3, CSS3, XHTML, JQuery, Swing and JavaScript to simplify the complexities of the application.
• Wrote and executed various MYSQL database queries frompythonusingPython-MySQL connector and MySQLdb package and generatedPythonDjango forms to record data of online users and used PyTest for writing test cases.

Environment: Python, Django, Java, JSF MVC, Spring IOC, APEX, Ruby on Rails, Spring JDBC, Hibernate, ActiveMQ, Log4j, Ant, MySQL, JDK 1.6,J2EE, JSP, Servlets, HTML, LDAP, Salesforce, ESB Mule, JDBC, MongoDB, DAO, EJB3.0, PL/SQL, react.js, Web Sphere, Eclipse, Angular.JS, and CVS.