Google Cloud Platform

Summary

I am certified in Google Data Analytics, and I recognize the significance of cloud platforms in modern technology. Learning platforms like Google Cloud Platform (GCP) is essential for scaling data workflows, managing large datasets, and deploying machine learning models efficiently. GCP offers powerful tools for data storage, processing, and integration, enabling flexible and robust solutions that drive real-time analytics and AI development. Whether it's building machine learning pipelines, managing cloud storage, or automating workflows, my experience with GCP equips me to handle complex, data-driven projects with precision.

My experience mostly comes from my Cinefly internship, personal projects, and my current role as a Research Analyst Intern, where I use GCP to store and analyze data from research articles.

Data Warehousing (BigQuery) :

BigQuery is a fully managed, AI-ready data platform, designed to handle large-scale data with built-in machine learning, geospatial analysis, and business intelligence features. I have consistently used BigQuery as my primary data warehouse in both my Cinefly internship and personal projects. My experience includes creating temporary and permanent tables, managing external data sources, and using BigQuery Studio for unified analytics. I’ve also extensively used Looker Studio to visualize data directly from BigQuery, enabling seamless integration for management insights.

Data Processing (Dataflow, Data Fusion) :

Google Cloud’s Dataflow and Data Fusion are crucial for building and managing data pipelines, particularly for ETL processing. I’ve utilized these tools to design, build, and optimize data pipelines, ensuring efficient data integration and transformation for various analytics purposes. These platforms have been key in automating data workflows and processing large volumes of data in real-time.

Machine Learning (AI Platform) :

Google Cloud’s AI Platform has been an essential tool in my machine learning projects. I've explored its functionalities through Google’s courses, particularly with Vertex AI Studio, Vision AI, TensorFlow, and AutoML. My practical experience includes building and deploying machine learning models, leveraging GCP's scalable infrastructure for training, and integrating ML models into business solutions.

API Integration & Management :

My strongest experience in GCP revolves around API integration, having worked with several Google Cloud APIs, such as the Cloud Intelligence API, Speech Recognition API, Looker API, NLP API, as well as data storage APIs like BigQuery API and Cloud Dataflow API. These have been crucial for automating workflows, integrating external services, and building comprehensive data pipelines that feed into real-time analytics systems.

Networking :

I also have a some understanding of GCP networking principles, including setting up Virtual Private Clouds (VPCs) and configuring load balancing for high-availability systems.