PHD candidates should provide an authentic personal statement reflecting on their own personal interest. In the event that any outside resources are used those should be cited in APA format.

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PHD candidates should provide an authentic personal statement reflecting on their own personal interest. In the event that any outside resources are used those should be cited in APA format. Submission should be a maximum of 500 words or 125 words per question or prompt. Applicants must answer each of the four prompts for their program.

PHD IT:

1. What are your research interests in area of Information technology? Why are you inspired to research this area and why do you think it is important to research in this area.

2. Why did you select PHD in information technology? Why did you selected university of the XXXXXXXXXXXX.

3. As an individual, what are you strengths and weakness and how will they impact you as a PHD IT student?

4. Where do you see the future of information technology going and where do you see yourself in this mix after obtaining PHD in Information Technology from UC?

Note: for answering question 4 you can relate my current job roles and responsibilities which are specified below.

Professional Summary:

4 plus years of experience of combined experience in IT industry, played major role in implementing, developing and maintenance of various Web Based applications using Java, J2EE Technologies and Hadoop Ecosystems.

Excellent understanding of Hadoop architecture and complete understanding of Hadoop daemons and various components such as HDFS, YARN, Resource Manager, Node Manager, Name Node, Data Node with CDH4 and CDH5.

Experience in Linux and Bigdata Administration and experience in Automation, Continuous integration and configuration management tool like Ansible.

Responsibilities:

Build data expertise and own data quality for ingestion pipelines.

Interface with engineers, product managers and product analysts to understand data needs.

Architect, build and launch extremely efficient & reliable pipelines to move data to our data warehouses.

Design, build and launch new data extraction, transformation and loading processes in production.

Create new systems and tools to enable the customer to consume and understand data faster.

Good troubleshooting skills on Hue, which provides GUI for developer's/business users for day to day activities.

Monitored workload, job performance and capacity planning using Cloudera Manager.

Involved in Analyzing system failures, identifying root causes and recommended course of actions.

Experienced in installation, configuration, troubleshooting and maintenance of Kafka & Spark clusters.

Wrote the shell scripts to monitor the health check of Hadoop daemon services and respond accordingly to any warning or failure conditions

Interact with Cloudera support and log the issues in Cloudera portal and fixing them as per the recommendations.

Set up static and dynamic resource pools for Yarn applications and impala queries in Cloudera Manager based on the application team needs.

Involved in extracting the data from various sources into Hadoop HDFS for processing.

Worked Extensively with Linux platform to setup 40 plus nodes cdh clusters as part of customers’ requirements.

Experience on Commissioning, Decommissioning, Balancing, and Managing Nodes and tuning server for optimal performance of the cluster.

Integrated external vendor tools like Unravel and SAS viya with Hadoop for performance and monitoring.

Involved in setting up the Kerberos authentication.

Development in build pipelines, write python scripts and to perform transformation on existing tables and new sources.

Tested new releases and Hadoop components in lab clusters.

Configured Stream sets to store the converted data to SQL SERVER using JDBC drivers.

Experienced using Ansible scripts to deploy Cloudera CDH 5.12.1 to setup Hadoop Cluster.

Dry Run Ansible Playbook to Provision the OpenStack Cluster and Deploy CDH Parcels.

Participated in requirement gathering of the project in documenting the business requirements.

Environment: Apache Hadoop, Hive, Impala, Hdfs, Linux, Sql, Nfs4, Airflow, CDH, Kerberos, Shell, Sqoop, Unravel, Ansible, Python, SAS Viya

    • 5 years ago
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