Key Projects: Microservices Event Generation and Storage in BigQuery• Engineered an event framework for developers to generate events, catering to audit and business data, and ML model requirements.• Facilitated data transmission from JAVA code to Kafka from diverse microservices after validation with schema of BigQuery table.• Streamed data from Kafka to GCS using Vector.dev and used BigQuery load jobs API in JAVA to load data from GCS to BigQuery.• Elevated developer experience by crafting a React.JS UI, offering a platform for creating, editing, and visualizing load jobs in BigQuery.CIBIL Data Integration• Orchestrated CIBIL API integration, extracting customer scores, personal details, and loan history in JSON format.• Engineered a resilient microservice in Play Framework, utilizing WS library for API calls with a retry mechanism.• Leveraged Jackson library with custom deserializers to parse JSON data into Java classes, enabling storage in MySQL DB.Clickhouse Integration and Data Sharding• Utilized Kafka for real-time data capture from microservices for ML feature calculation.• Evaluated Clickhouse data insertion methods using Vector.dev, Clickhouse Kafka Engine, and Clickhouse JAVA API.• Implemented sharding for data distribution across Clickhouse nodes, optimizing scalability and using Kafka Engine for insertion.Task Scheduler• Engineered a JAVA scheduler, enabling developers to execute any method within any Java class at defined intervals using cron expressions.• Integrated scheduler in Play Framework as a module and used ExecutorService API to check methods to be executed every minute.• Leveraged Kafka for method execution requests from diverse microservices and used reflection and multi-threading to execute methods.

Objective:- To analyse reliability data to obtain its predictability and improve operations and maintenance of airplanesApproach:- Analysed various types of two years reliability data by making data frames using Pandas library in Python- Implemented Natural Language Processing using Nltk library to analyse discrepancy text using Tf-idf score- Predicted cancellations and time of delays of an airplane by discrepancy text using 4 machine learning algorithmsResults:- Achieved an accuracy score of 95% using Multinomial Naive Bayes algorithm in predicting time of delays- Displayed major reasons of discrepancies using matplotlib and purposed analysis of maintenance action for them