Aditi Wadekar has over 2 years of experience working with big data technologies like MongoDB, Hadoop, Spark and AWS. She has expertise in setting up and managing MongoDB sharded clusters, performing data migration between databases, and developing reports and dashboards using tools like Splunk. She is looking for a role that offers professional growth and opportunities to enhance her big data skills.

1. Email: aditiwadekar10@gmail.com| Mobile: +91-87933-49212
BIG DATA –PROFESSIONAL
Working in Big Data with a two plus years of IT experience. I am part of Opus Consulting
Solutions Analytics Team. My responsibilities are to setup, configure & manage Hadoop and
MongoDB cluster. Perform Data Migration Activities. I am looking for an organization that
offers professional growth and provides an environment conducive to excellence and enhance
my ability to be an achiever.
Big Data Skills:
MongoDB
 Created Sharded cluster with replica Set on AWS production cluster
 Worked with Authentication and Authorization on cluster
 Installed and configured Nagios for monitoring shard cluster
 Tested Node migration, Cluster upgradation and Backup-recovery on MongoDB cluster
 Worked with Aggregate queries to analyze data
Hadoop
 Hands on experience with Apache Hadoop
 Experimented tools like Hbase, Pig, Hive by inserting, updating and performing query
operations
 Implemented Spark on Real time data
 Installed and configured cloudera-manager for testing purpose
BI-Tool
 Data Migration using Pentaho data Integration tools.
 Used Hunk/Splunk to generate and analyze reports
Hands on Experience with AWS Environment
 EC2, volumes, snapshot, S3, IAM
Other Technical Skills:
 Operation System: CentOS, RHEL,Ubuntu, Windows.
 Databases: MongoDB, Mysql, SQL Server
 Tools: Hunk/Splunk, Pentaho data Integration tools.
 Languages: Python
 Virtualization: VMware, Virtual Box
 Scripting/Automation: Bash, Shell Scripting
 Big Data: Ambari, HDFS, Hive HA, Spark
o Hue ,Oozie, YARN/Map reduce, NoSQL, Sqoop
o MongoDB, AWS
Process Oriented skills:
Worked for the Complete Lifecycle Requirement gathering, Analysis, Build, Unit testing,
Migration, Production deployment & Support activities.

2. CERTIFICATIONS
1. Certified on MongoDB Advanced Deployment and Operations August 2016
2. Initiation on Real time data analytics over Hadoop and MongoDB April 2015
3. Outstanding Team – “Cardtronics Big Data Team” April 2014-Sept 2014
4. Certified Courses
○ Hadoop 101 Sept 1, 2016
○ Introduction to Pig May 8, 2015
○ Using HBase for Real-time Access to your Big Data March 8, 2015
○ Spark Fundamentals I Jan 20, 2015
○ M102: MongoDB for DBAs Dec 1, 2014
○ Certified in Hadoop Fundamentals -1 Oct 27, 2014
○ M101J MongoDB for Java Developers Oct 7, 2014
AWA RDS/RECOGNITIONS
 Reward and Recognition obtained for outstanding Teamwork of Big Data Team
 Reward obtained for taking initiation on Real time data analytics over Hadoop and
MongoDB.
 Active participation in Opus Women Football Team and Women Cricket Team.
EXPERIENCE
Opus Consulting Solution 2014 – Present
1. Implemented a project required by the business users to analyze the number of
transactions taking place location and month wise across US, Jan 2016
○ Data migration and validation from MongoDB to Hadoop.
○ Installing and Configuring Splunk to connect Hadoop and MongoDB.
○ Created Providers and indexes in Splunk to connect hdfs.
○ Developed Dashboards in Splunk based on input parameters
○ Scripts written in Python to load data to Hadoop.
○ Developed, Configured and scheduled reports using Splunk
○ Developed SPL out of Stored Procedures and developed Reports.
○ Created users and roles in Splunk.
○ Query on Splunk to generate 20+ reports out of Hadoop.
Roles and Responsibility
○ Perform Requirement gathering to obtain business logic of the report
○ Provide estimates for development and testing of reports
○ Understand the business logic of the report.
○ Designed and Developed Reports in Hunk
○ Enhanced skills in SQL and SPL (Search Processing Language) while
developing reports

3. 2. Worked on Implementing Spark on Real-Time transactional Data, Dec 2014
○ Installed and configured spark with MongoDB and Hadoop on CentOS 6.5.
MongoDB contained real-time data. Spark streaming was used to deal with live
streaming data. Spark was connected to MongoDB and various queries were
performed on the transactional data. The implementation helped in overcoming
the drawbacks of MongoDB that is joins. Queries were performed in faster and
efficient way on huge volume of data.
○ Apache Zeppelin was installed and configured with spark. The reports were
being developed in zeppelin and the transactional data was being analyzed in an
efficient way
Roles and Responsibility
○ Requirement gathering and provide design of how spark can be implemented on
Real time data.
○ Installation and configuration of MongoDB and Hadoop on Servers.
○ Developed queries in Spark and Zeppelin to connect and query over MongoDB
and Hadoop data.
○ The query was improvised to check the efficiency of the tool.
○ Reports generated out of Zeppelin
3. Created Hadoop and MongoDB as Data Warehouse, April 2015
○ Installed and configured MongoDB sharded cluster on AWS production. The
aim was to create Hadoop as a data warehouse.
○ Setup Hadoop cluster along with Ambari, Hue on Environment.
○ Performed data migration Activity using sqoop from SQL to Hadoop.
○ Familiar with Hbase, Pig, Hive by inserting, updating and performing query
operations
○ Developed Backup-scripts for taking MongoDB backup to prevent loss of data.
○ Used Pentaho-data integration tool to migrate data from SQL to MongoDB
○ Developed scripts in python to migrate data from MongoDB to Hadoop cluster.
○ Validate the data in MongoDB and Hadoop.
○ Hadoop consisted of 3 years of data.
Roles and Responsibility
○ Setting up Hadoop cluster and monitoring the cluster.
○ Perform Data Migration Activity from SQL to Hadoop or from MongoDB to
Hadoop
○ Create roles and Users in hive and grant permissions.
○ Setting up MongoDB sharded cluster with SSL authentication and
authorization.
○ Developed MongoDB Backup Scripts.
○ Installed and Configured Nagios
○ Monitored MongoDB cluster using Nagios.
○ Developed Aggregate queries in MongoDB to test the data in collections.

4. Candidate for Bachelors in Applied Science, Information Technology from College of
Engineering, Pune - 7.68 (Sept 2010-Aug2014)