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Samrat S. AWS, Docker and Data Engineering

My name is Samrat S. and I have over 8 years of experience in the tech industry. I specialize in the following technologies: Python, Java, Docker, MySQL, MongoDB, etc.. I hold a degree in Bachelor of Science (BS). Some of the notable projects I’ve worked on include: Container Log Processing at Scale, Streamlined Data Engineering Architecture. I am based in Kathmandu, Nepal. I've successfully completed 2 projects while developing at Softaims.

I specialize in architecting and developing scalable, distributed systems that handle high demands and complex information flows. My focus is on building fault-tolerant infrastructure using modern cloud practices and modular patterns. I excel at diagnosing and resolving intricate concurrency and scaling issues across large platforms.

Collaboration is central to my success; I enjoy working with fellow technical experts and product managers to define clear technical roadmaps. This structured approach allows the team at Softaims to consistently deliver high-availability solutions that can easily adapt to exponential growth.

I maintain a proactive approach to security and performance, treating them as integral components of the design process, not as afterthoughts. My ultimate goal is to build the foundational technology that powers client success and innovation.

Main technologies

  • AWS, Docker and Data Engineering

    8 years

  • Python

    3 Years

  • Java

    3 Years

  • Docker

    1 Year

Additional skills

Direct hire

Potentially possible

Previous Company

CloudFactory

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Experience Highlights

Container Log Processing at Scale

Containers Log Processing Pipeline at Scale Containers Log Processing Pipeline is a real-time data log processing system designed for internal developers to run/debug their plugins seamlessly. It is responsible for collecting log data from various containers, processing it in real-time, and storing it in a scalable way to debug in a decisive way later.

Streamlined Data Engineering Architecture

Integrating Supabase Realtime, Kafka, Flink, and Typesense. Recently, I built a system that keeps data flowing smoothly and processes it efficiently. This blend of advanced tech ensures we handle real-time data, scale effectively, and supercharge our search capabilities. Technologies Used: 1. Supabase Realtime for Change Data Capture (CDC): Supabase Realtime acts as the backbone for capturing changes in our data. Its efficient Change Data Capture real-time mechanism ensures that we remain up-to-date with every modification, allowing for real-time accuracy in our dataset. 2. Event Processor and Kafka Integration: We've developed a custom event processor to subscribe to Supabase events. This processor efficiently relays these events to Kafka, ensuring a scalable and reliable streaming architecture. As a robust message broker, Kafka handles this continuous stream of events flawlessly. 3. Kafka as a Message Broker: Kafka's prowess in managing distributed data streams is unparalleled. It effectively manages the flow of data from the event processor to the downstream consumers, maintaining the integrity and reliability of our data. 4. Flink as a Stream Processor: Flink, our stream processor, plays a pivotal role in consuming data from Kafka. Its powerful filtering capabilities streamline the data, ensuring only relevant information moves forward in the pipeline. This step is crucial for optimizing downstream processes. 5. Typesense Integration for Enhanced Search: By enriching and adding filtered data from Flink, Typesense, our search engine, gains access to a refined dataset. This integration significantly boosts our search capabilities, allowing for quicker and more accurate searches across various fields and parameters.

Education

  • Tribhuvan University

    Bachelor of Science (BS) in Computer science

    2014-01-01-2018-01-01

Languages

  • English
  • Nepali

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