Technology Stack
Every technology choice is deliberate. These tools solve real problems at scale and are selected based on project requirements.
Cloud Infrastructure
Google Cloud Platform
Best-in-class for data analytics and ML workflows. BigQuery's serverless architecture eliminates operational overhead. GKE provides managed Kubernetes without the complexity.
Amazon Web Services
Unmatched service breadth and maturity. Lambda for event-driven compute. Kinesis for real-time data streams. DynamoDB for single-digit millisecond latency at any scale.
Microsoft Azure
Enterprise integration strength. Databricks for unified analytics. Azure Synapse for data warehousing at scale. Native Active Directory integration for governance.
Data Engineering
Apache Spark
Proven for distributed data processing. Handles petabyte-scale transformations. Native support across cloud providers.
Apache Airflow
Industry standard for workflow orchestration. Code-first approach. Rich plugin ecosystem for extensibility.
Kafka / Pub/Sub
Real-time streaming backbone. High-throughput, fault-tolerant message queues for event-driven architectures.
dbt (Data Build Tool)
Analytics engineering standard. Version-controlled transformations. Automated testing and documentation.
DevOps & Infrastructure
Docker & Kubernetes
Container orchestration at scale. Reproducible environments. Resource efficiency and auto-scaling out of the box.
Terraform / OpenTofu
Infrastructure as Code. Multi-cloud compatibility. State management for reliable deployments.
GitHub Actions
Native CI/CD integration. Minimal configuration. Fast feedback loops for continuous delivery.
Prometheus & Grafana
Production observability. Metrics scraping and alerting. Visual dashboards for real-time monitoring.
AI & Automation
Google GenAI Agent Development Kit (ADK)
Purpose-built for production AI agents. Native integration with Gemini. Structured tool calling and state management.
Model Context Protocol (MCP)
Standardized tool interface for AI systems. Decouples tools from models. Enables flexible, modular AI architectures.
Vector Databases (Pinecone, Milvus)
Semantic search at scale. Low-latency similarity queries. Foundation for intelligent document retrieval and knowledge search.
Backend & API
Python (FastAPI, Flask)
Rapid development velocity. Rich data science ecosystem. Async support for high-concurrency workloads.
Go
High-performance services. Low memory footprint. Compiled binaries for simple deployment.
GraphQL
Client-driven data fetching. Eliminates over-fetching. Strong typing and introspection.
gRPC
High-performance RPC. Binary serialization. Bi-directional streaming for real-time communication.
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