Kalaichelvi Rajendran

Kalaichelvi Rajendran

SQL Database Administrator

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location of Kalaichelvi RajendranGreater Pittsburgh Region

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  • Timeline

  • About me

    Solutions Engineer at Uptycs

  • Education

    • Frankfurt University of Applied Sciences

      2017 - 2020
      Master of Science (M.S.) High integrity systems

      Relevant Courses:Safety Critical Systems, Advanced Formal Modeling, Introductory Data Analysis, Advanced Real-TimeSystems, Implementation of Database Management Systems, Pattern Oriented Software Architecture,Advanced Testing Methods, Advanced IT-Security, Human Machine Interaction, Data Mining Methods,System Theory and Modeling, Simulation Methods, Internet of Things, Formal Specification andVerification, Cloud Computing.

    • SRM IST Chennai

      2008 - 2012
      Bachelor of Technology (B.Tech.) Information Technology

      B.Tech (Information Technology)

  • Experience

    • Tata Consultancy Services

      Jul 2012 - Apr 2017
      SQL Database Administrator

      ABB Information Systems, Data Center Migration Sept 2015 – April 2017● Responsible for analysis of discovery templates and migration of databases(MSSQL, Oracle, MySQL) using Tivoli Storage Manager (TSM).● Responsible for successful movement of databases with high availability,replication, mirroring, and log shipping to the target Data Center without data loss.● Worked on “as is” like-to-like MSSQL database instance migration and individualdatabase migrations.● Played an active part in the discovery phase of the application and analyzed themigration requirements for databases.● Managed successful migration of critical production database instances with noimpact to the business.● Handled end-to-end activities of database migration from source to target ensuringsuccessful connection to the application serverSONY GDC WEST, US, Belgium & Europe July 2012 – Sept 2015● Installed, configured, and administered SQL server 2008 and 2012, as well as SQLServer clustering.● Performed database maintenance activities, such as backup, restoration, andre-indexing.● Applied SQL server service packs and cumulative updates in a timely manner.● Project Activities:○ Deployed SSL and database encryption.○ Supported data center migration activities involving databases and theirsupporting objects.○ Performed database performance tuning for optimal results.○ Ensured back-up procedures were executed without failure.○ Monitored storage space on server and addressed issues with log files,shrinking log, and data files.○ Created maintenance plans and jobs for automation of repetitive tasks.● As a part of the DBA team providing 24/7 production support to the clients:○ Monitored scheduled jobs.○ Ensured high availability of databases.○ Monitored disk space on all servers.○ Performed general health checks on servers.○ Analyzed, identified, and resolved server performance issues. Show less

    • Acarda

      Aug 2017 - Feb 2019
      Work Student – IT Administration

      ■ Active Directory Management■ Network Administration■ Performed onboarding for new employees■ Assisted with JIRA maintenance and troubleshooting■ Performed quality checks on code developed during sprints

    • Frankfurt University of Applied Sciences

      Oct 2019 - Mar 2020
      Master Thesis

      3D Brain Tumor Image Segmentation Using Dense U-Net Architecture Based on DeepConvolutional Neural Networks (CNN) ■ Manual identification of brain tumors with the help of magnetic resonance (MR) images isa tedious, impractical process that requires experienced radiologists or specialists, can oftenresult in significant delays, and may produce noisy data leading to incorrect results. Thispaper aims to propose a model that can improve accuracy in detecting and segmenting braintumors using U-Net-based deep convolutional neural networks.■ Without data detailing the tumor region, Hyperthermia (HT) treatment cannot beperformed. 3D MRI brain tumor image segmentation is the method of dividing digitized 3Dimages of abnormal tissue regions so that they can be accurately diagnosed.■ Using the proposed U-Net-based method, a medical MRI image is given as input to theDeep Learning (DL) model to detect the tumor region. The Deep Learning (DL) model willprocess the image in multiple layers using feature extraction, segmenting the tumor regionas output.■ The combination of two CNN-based network approaches is inspired by the recentlyintroduced DenseNet architecture for image classification. Merging the DenseNet andU-Net approaches will lead to an effective image segmentation algorithm. Show less

    • Uptycs

      Sept 2022 - now
      Solutions Engineer
  • Licenses & Certifications

    • Microsoft Certified Technology Specialist

    • ITIL Foundation Examination

      Jan 2013
    • Microsoft Certified: Azure Fundamentals

      Microsoft
      Aug 2022
      View certificate certificate
    • AWS Certified Cloud Practitioner

      Amazon Web Services (AWS)
      Sept 2022
      View certificate certificate