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Project 2:

Project Name	 : Work Order Automation
Clients           	 : AEP (American Electric Power)
Period	                 : August 2017 to October 2017
Technologies & Tools	: Apache Spark (PySpark), SparkSQL, HDFS, Hive, Sqoop, Pig, Awk Scripting
Role	                 : Developer


Project Description:

	AEP has fixed IoT smart devices in customer places for transmitting the meter reading for every 15 minutes. Along with this, the device health data also will be transmitted from the IoT smart devices on a daily basis. The current investigation process is manually performed by the analysts. The objective is to automate the manual process followed earlier for Alert generation, work order creation, investigation and remediation. This project helped the client potentially in cost optimization and time management.

Roles & Responsibilities:
	Developed Pyspark applications to ingest raw data from multiple sources, cleanse the data, perform transformations and actions, analyze the data for potential meters with device health issues, and store as an alert in the partitioned hive tables.
	The device health data was analyzed using certain criteria’s defined by the management.
	Developed Pyspark applications to generate work orders based on the alerts generated using SOAP call and notify the business people with mail alerts. 
	Developed Pyspark application to retrieve the latest update of a work order after order investigation and remediation and overwrite the partitions accordingly.
	Performed various levels of unit testing and system testing to ensure the quality and accuracy of the Pyspark applications.

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Project 1:

Project Name	:  AMI Aggregation
Clients 	                : AEP (American Electric Power)
Period	                : May 2017 to July 2017
Technologies & Tools	: Apache Pig, HDFS, and Hive
Role	                : Developer

Project Description:
	AEP has fixed IoT smart devices in customer places for transmitting the meter reading for every 15 minutes. The data is in raw xml format which is ingested into the oracle data store. The daily usage data has to be made available for the end users to visualize the previous day usage of their corresponding meters. And data has to be aggregated at transformer level for making business decisions such as capacity planning, revenue calculation etc.

Roles & Responsibilities:
	Worked as the Lead developer for the entire project with the team size of 3.
	Involved in the initial architecture and planning sessions and helped client in choosing the right Big Data Technology stack.
	Designed and developed end to end analytical processing framework for the self-service analytics dashboard.
	Used Sqoop to ingest data from oracle databases on scheduled batch processing and on demand.
	Used Pig Latin scripts to ingest raw data from flat files and hive tables, apply transformation, populate staging tables, aggregate the data as per requirement and persist in the partitioned, bucketed, transaction enabled hive tables.
	Performed various levels of unit testing and system testing to ensure the data quality.

Worked as a Bigdata Developer & Admin.

Few of my responsibilities include:

	Responsible for Hadoop architecture designing, installations, development, testing, maintenance and support.
	Good exposure to various Hadoop distributions like Cloudera, Hortonworks and MapR
	Involved in loading large database tables from RDBMS to HDFS using Sqoop tool.
	Developed Map reduce jobs, and UDFs in java for extending functionalities of Pig and Hive.
	Hands on experience with various file formats like Avro, JSON, XML, Sequence files, Parquet, ORC, etc. and compression techniques.
	Created Partitioning, Bucketing, Map side Join, Parallel execution for optimizing the hive queries. 
	Involved in creation and designing of streaming data ingest pipelines using technologies such as Apache Storm- Kafka. 
	Collecting and aggregating large amounts of log data using Apache Flume and staging data in HDFS for further analysis 
	Writing Scala scripts to process the data stored in HDFS using Spark framework.
	Worked on Spark SQL and Data frames for faster execution of Hive queries using Spark HiveContext. 
	Good exposure to visualization tools such as Zeppelin notebook, Jupyter notebook, etc.
	Implemented POCs using Big Data tools like Sqoop, Flume, Hive, Pig, HBase, Kafka, Storm, Spark, Zeppelin.
	Preparing questionnaires and RFPs for new proposals.
	Designing architectures using MS Visio for new proposals requested by clients.
	Involved in integration of Hadoop with SAP Hana using SAP HANA Vora.
	Installed and configured 30 node Hortonworks Data Platform (HDP) cluster and 20 node MapR enterprise edition cluster.

➢ Loading files to HDFS and writing Hive queries to process required data. 
➢ Loading data to Hive tables and writing queries to process. 
➢ Maintaining and Administrating the 06 node Hadoop cluster. 
➢ Worked on setting up Hadoop over multiple nodes and designed and developed Java map reduce jobs. 
➢ Involved in loading data from LINUX file system to HDFS. 
➢ Implemented test scripts to support test driven development and continuous integration. 
➢ Responsible to manage data coming from different sources. 
➢ Load and transform large sets of structured, semi structured and unstructured data. 
➢ Experience in managing and reviewing Hadoop log files. 
➢ Worked on Hive for exposing data for further analysis and for generating transforming files from different analytical formats to text files. 
➢ Managing and scheduling Jobs on a Hadoop cluster. 
➢ Installed and configured Hadoop Map Reduce, HDFS, Developed multiple Map Reduce jobs in java for data cleaning and preprocessing. 
➢ Importing and exporting data into HDFS and Hive using Sqoop. 
➢ Involved in creating Hive tables, loading with data and writing hive queries that will run internally in map reduce way. 
➢ Written Hive queries for data analysis to meet the business requirements.

Elliott Management

WestBridge Capital

United Asset Strategies

Nordic Mezzanine Advisers

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