Working on LinkedIn home grown distributed data store called Espresso. Espresso is LinkedIn’s horizontally scalable document store for primary data such as member and company profiles, InMail, social gestures (likes, comments, shares) various advertising data sets, etc.

https://engineering.linkedin.com/teams/data/projects/espresso

Led the architectural overhaul of the transport layer for Espresso. The new architecture can now scale 10X more to accommodate the growth of the platform and reduced query latency by 75%. Linkedin Engineering Blog https://www.linkedin.com/blog/engineering/infrastructure/solving-espresso-s-scalability-and-performance-challenges-to-sup

Improved resilience and stability improvements of Espresso, a stateful, distributed database system, addressing issues such as single-host failure, client request storms and others

By employing strategic enhancements, such as enabling follower reads, improving connection lifecycle management, implementing storage host failure resiliency, and introducing mechanisms to protect the system from request overloads, we achieved significant progress over the years.

Packt : https://www.packtpub.com/big-data-and-business-intelligence/learning-elasticsearch

Amazon : https://www.amazon.com/Learning-Elasticsearch-Abhishek-Andhavarapu-ebook/dp/B01MURNWEB

This book will show you how to build highly scalable search applications using Elasticsearch. You can use Elasticsearch for a small application or a large application with billions of documents. It is built to scale horizontally and can handle both structured and unstructured data. You will install and setup Elasticsearch and Kibana, and handle documents using the Distributed Document Store. You will see how to query, search, and index your data, and perform aggregation-based analytics with ease. You will see how to use Kibana to explore and visualize your data. Furthermore, you will also learn to handle document relationships, work with geospatial data, and much more, with this easy-to- follow guide. Finally, you will see how you can set up and scale your Elasticsearch clusters in production environments.

Packed with easy-to- follow examples, this book will ensure you will have a firm understanding of the basics of Elasticsearch and know how to utilize its capabilities efficiently.

Currently working on the next generation Order Search over billions of orders using Elasticsearch, Kafka, Storm which will enable text search and complex filtering on orders. Huge performance gain will help millions of users using eBay.

• Redesigned the existing ETL pipelines (Hadoop, Hive) with Apache Spark on Yarn and HBase.

• Improved the speeds of the ETL pipelines by 50X.

• Distributed percolator to create alerts on streaming data. - Hadoop, Hive, Elasticsearch, RabbitMQ.

• Awarded “Employee of year 2013” for my contributions in design and development of big data platform.

• Experience in architecting the reporting platform based on SQL in to NoSQL using ElasticSearch. Improved speeds 100X and highly scalable.

• Developed an API to talk to Elasticsearch.

• Designed and developed the etl process to push the data from SQL to HADOOP and from HADOOP to Elasticsearch.

• Designed Spotlight Data Mining (http://www.allegiance.com/products/spotlight). Spotlight mines millions of records of data to uncover patterns of significance and value in real time.

• Implemented a queue based execution plan for Spotlight to take advantage of all the nodes in the farm.

• Worked on UBER Reporting framework. UBER Framework allows large scale analytical queries on unstructured data.