Pratik R. Joshi has worked as a Software Engineer at MapR Technologies since summer 2015. His responsibilities include implementing MapReduce jobs, security features, and testing for MapR's distributed JSONDB. He has also completed independent projects involving predictive analytics and analyzing geotagged tweets using Apache Spark and Kafka. Pratik obtained his Master of Science degree in Computer Science from the University of Southern California in 2015 and his Bachelor of Engineering degree in Computer Engineering from the University of Mumbai in 2013.

1. Pratik R. Joshi
185 Estancia Drive, Apt #325, San Jose, CA – 95134 | (213) 400-0946 | pratikjo@usc.edu
www.linkedin.com/in/pratikrvjoshi/ https://github.com/PratikRJoshi
EMPLOYMENT_______________________________________________________________________
Software Engineer MapR Technologies Summer 2015 - Present
• Implemented MapReduce jobs and security features for operational tools running on top of MapR’s JSONDB.
• Implemented and tested table replication feature for high-availability feature of distributed JSONDB
• Designed and implemented a basher tool for load, stress and longevity testing of the tools working on top of JSONDB
• Implemented setup for multi-node YARN cluster and daily regression run via shell scripts and execution using Jenkins
Independent Projects___________________________________________________________________
Kaggle Competition for eBay iPad sales prediction – edX MIT Analytics Edge Fall 2015
• Developed an Analytics model to predict the sales success of a set of eBay listings for Apple iPads from Spring 2015
• Trained the model from training set consisting of 181614 listings and tested on data set consisting of 7890 listings
• Obtained an accuracy of 91% on Kaggle’s internal data set used to test the final model
• Project URL: https://github.com/PratikRJoshi/edX-Kaggle-Competition
Geo Tweet Analyzer [Apache Kafka and Spark Streaming] Spring 2016
• Designed and implemented Kafka Producer to parse Twitter Public tweet stream and store information in Kafka
• Designed and implemented Spark Streaming consumer to parse the streams and generate tweet location heat map from it
• Project URL: https://github.com/PratikRJoshi/Geo-Tweet-Analyzer
• More Independent Projects - https://github.com/PratikRJoshi/Independent-Projects
Technical Skills________________________________________________________________________
Languages: Java, C++, Objective-C, HTML, CSS, JavaScript, XML, PHP, JSP, Servlets, AJAX, jQuery, SQL, Android SDK
Open Source Technologies: Apache Zeppelin, Apache Kafka, Apache Spark
Servers: Apache Tomcat, XAMPP Databases: Oracle 12c, MySQL 5.0, SQLite3
Operating Systems: Windows, Mac, Linux Tools: GitHub, Mercurial, XCode 6.3, Eclipse, IntelliJ, Maven
Education____________________________________________________________________________
Los Angeles, CA University of Southern California August 2013 - May 2015
M. S. in Computer Science GPA: 3.6
Coursework: Algorithms, Artificial Intelligence, Database Systems, Information Integration and Retrieval, Search Engines
Mumbai, India University of Mumbai August 2009 - May 2013
Bachelor of Engineering in Computer Engineering GPA: 3.5
Academic Projects______________________________________________________________________
Storage and Retrieval in Spatial Database [Oracle 12c, Java, SQL] Spring 2014
• Developed spatial database which consisted of USC campus stored as set of polygons and kept track of various objects like
fire hydrants, buildings on fire and those not on fire in Oracle spatial database.
• Queries were fired on spatial database to retrieve list of spatial objects near other desired objects
Android Mobile Application using Yahoo Weather API [HTML/CSS, JS, PHP, Yahoo Weather API, YUI Datatable] Fall 2013
• Designed and developed Android application using information from Yahoo Weather API displaying details of current
weather along with weather forecast.
Neural Network for image detection [Java] Spring 2014
• Designed a neural network for detecting images given in 28X28 pixel grid
• Trained and tested with 2 sets of 30,000 sample images and achieved 91% prediction accuracy