In-depth guides, tutorials, and references for engineers working with Python, SQL, Spark, cloud platforms, and modern data stacks.
Start exploringWhat we stand for
NPBlue is where engineers go deep. Every guide is written to move you from understanding to production.
Turn a blank canvas into a concrete plan, backed by battle-tested patterns.
Ship with confidence. Structured guides take you from zero to production.
Scale what works. Deep dives on performance, cost, and architecture.
Join a community of practitioners sharing real-world experience.
Technology space
Cutting-edge innovation meets limitless possibilities. Pick a language and go deep.
Cloud computing
Side-by-side service guides, architecture patterns, and cost breakdowns across the big three.
EC2, S3, Lambda, Redshift, and the full services catalogue — demystified and production-ready.
Explore AWS guides →BigQuery, Dataflow, Vertex AI, and GKE — from first deploy to enterprise scale.
Explore GCP guides →Synapse, ADF, AKS, and the broader data platform — patterns that work in the real world.
Explore Azure guides →Data engineering
Transform, secure, and scale your data layer with the tools teams actually run in production.
Transforming raw data into analytics-ready pipelines with elegant, version-controlled SQL.
Read guides → SecurityEncryption, access control, RBAC, and compliance — the silent guardian of trust in every byte.
Read guides → WarehouseThe elastic engine of insight — virtual warehouses, zero-copy cloning, and cost control at scale.
Read guides →Artificial Intelligence
From LLM fundamentals to production agents — the guides engineers need to build AI-powered systems in 2025.
Prompt engineering, fine-tuning, evaluation, and deploying LLMs in production pipelines — everything after "Hello, ChatGPT".
Explore GenAI →Tool use, memory, planning, and MCP — building autonomous agents that actually work in enterprise environments.
Explore Agents →Vector stores, chunking strategies, hybrid search, and evaluating retrieval quality end-to-end.
Explore RAG →Classical ML through MLOps — training, serving, monitoring, and keeping models healthy in production.
Explore ML →Neural network fundamentals, transformer architecture, and modern training techniques at scale.
Explore Deep Learning →Compare token pricing across OpenAI, Anthropic, Google, and open-source models before you commit.
Open tool →Career
Data engineering and cloud interviews unpacked — from system design to behavioural storytelling.
SQL, Python, Spark, and cloud architecture questions with model answers.
STAR-format stories for leadership, conflict, and impact questions at senior levels.
Benchmark your compensation across roles, locations, and experience bands.
