Turn Claude API knowledge into real data platform productivity — RAG over your runbooks and reports, AI agents for pipeline monitoring, and MCP servers your whole data stack can connect to. Written for data engineers, analytics practitioners, and data team leads.
You know basic Python and want to build Claude-powered applications. You want structured guidance from your first API call to production patterns.
You manage or work within a data & AI team — Snowflake, dbt, BI dashboards, data quality. You want to put LLM APIs to work on the problems your team already owns.
You are evaluating how Claude fits into analytics, ops, or decision workflows. You want applied training without starting from zero on the API.
4 parts · 11 articles · ~29,000 words · Advanced Beginner → Intermediate
Each article stands alone — start anywhere, or read end-to-end for the full picture.
All code examples use a data platform scenario — Snowflake warehouses, dbt pipelines, data quality runbooks, and BI reporting — so every technique maps to work your team already does.
The practitioner's case for learning the Claude API properly — what this series covers, who it's for, and how to use it.
Install the SDK, configure your environment, make a reliable API call with streaming, and understand the request/response structure you'll use throughout the series.
Define Claude's role and behaviour with system prompts, control output format with stop sequences and prefill, and extract structured JSON reliably.
Send images, PDFs, and mixed multimodal content to Claude — base64 encoding, URL references, multi-image reasoning, and cost-aware patterns.
Improve output quality on complex tasks with thinking modes, and reduce cost and latency with prompt caching — including model support, TTLs, and break-even analysis.
Build a retrieval pipeline from scratch: chunk documents, generate embeddings, implement vector search, BM25 keyword search, and combine them with Reciprocal Rank Fusion.
Improve retrieval precision with a cross-attention reranker, and solve the decontextualized chunk problem with Claude-generated context at index time.
Use Claude's server-side tools without writing execution code — real-time web search with citations, sandboxed Python execution with file output, and structured file editing.
Define your own tool schemas, implement the agent loop that executes tool calls and feeds results back, and handle multi-tool and batch tool call patterns.
Understand what MCP is, how the client-server protocol works, and how to connect your Claude application to an existing MCP server via stdio or HTTP transport.
Define tools, resources, and prompts with the FastMCP framework, expose them to any MCP-compatible client, and deploy a production-ready server for your own data or business logic.
DataMy helps APAC organizations modernize data platforms, improve analytics, and deploy AI systems that work in production. This training section extends that mission by making implementation knowledge directly available — for teams and individuals who want to build with modern AI APIs, not just read about them.
The series is written from a practitioner's perspective. Every article includes real, runnable code and honest trade-offs — cost, latency, architecture choices, and when not to use a feature. It covers what vendor documentation doesn't emphasize.
This training is particularly relevant if you are building AI over your data documentation, RAG pipelines for internal knowledge bases, cost monitoring agents for your data platform, or MCP servers that expose your data stack to any AI client — the exact scenarios DataMy builds with APAC data and AI teams.
If your team needs help applying these patterns in a business context, DataMy offers related consulting services.
Architecture and implementation support for Snowflake, Databricks, and modern data stacks.
Learn MoreDashboard design and analytics engineering for Tableau, Power BI, and Qlik.
Learn MoreAI system design, RAG implementation, and Claude integrations for APAC enterprise teams.
Learn MoreStart with Article A1 for the foundations, or jump directly to RAG, agent tools, or MCP — whichever maps to your next data platform project.