Python

DuckDB crunches gigabytes of CSV and Parquet with no database server, no import step, and no waiting around. You aim a SELECT straight at a file on disk and it answers. On 41 million rows of raw NYC taxi data, I clocked a full group-by aggregation in 20ms and a two-table join in another 20ms, read straight off Parquet with nothing loaded, copied, or indexed first. That is a multi-gigabyte analytical query returning before you lift your finger off Enter key! Every number in this post comes from a benchmark you can run yourself; the scripts and raw results live in a GitHub repo .

uv workspaces give Python a Cargo-style monorepo setup. You get one lockfile, one virtual environment, and auto-resolved inter-package dependencies. Cold installs finish in seconds, not minutes. Pair uv with Ruff for linting and formatting, and the pair replaces Poetry, Black, isort, flake8, and pip-tools in one shot. The rest of this post covers workspace setup, inter-package deps, Ruff config, CI, publishing, and the traps that snag teams moving off older tools.

Dagger lets you write CI/CD pipelines in Go, Python, or TypeScript instead of YAML. Your pipelines run inside containers, execute identically on your laptop and in CI, and get type-checked by your compiler or linter before they ever touch a remote runner. If you’ve spent hours pushing commits just to debug a GitHub Actions workflow, Dagger is the fix.

The core idea: pipeline steps are function calls in a real programming language. Each function call builds a directed acyclic graph (DAG) of container operations. The Dagger Engine (built on BuildKit ) executes this graph with automatic parallelization and layer caching. You run dagger call ci --source . locally, get the same result in GitHub Actions, GitLab CI, or CircleCI, and never write vendor-specific YAML again.

To wire up loose Python microservices, use NATS JetStream as the message bus with the nats-py client. JetStream gives you durable consumers, full stream replay, and exactly-once delivery through message dedup and double-ack. It does this in sub-millisecond time, with one small server binary. No Kafka brokers, no ZooKeeper.

This guide covers JetStream setup, pub/sub with durable consumers, a three-service order pipeline, and the steps to harden it for production.

Why NATS JetStream Over Kafka or RabbitMQ

Before any code, it helps to see why NATS keeps showing up in chats that once went straight to Kafka or RabbitMQ. The short answer: a lot less ops work.

The Claude Agent SDK is the Claude Code engine stripped down to a library. Same agent loop, same built-in tools, same context handling, but you call it from your own Python or TypeScript code instead of the CLI. If you’ve used Claude Code to read files, run shell commands, search codebases, and edit code, the SDK points that same machinery at any problem you want. No human needs to sit in the loop.

Yes, you can point Claude Code at a 541,909-row retail dataset and walk away with a six-sheet Excel workbook, professional charts, and a parameterized report script, without opening a Python file or debugging a single line of code. The complete workflow takes roughly 15 to 20 minutes from raw data to finished output.

The goal is real delegation. Claude handles setup, cleaning, math, and charts. You focus on the right questions to ask.