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10 posts
Run Airflow in WSL for DAG development, inspect scheduling and backfills, and keep SQLite, Linux paths, and workstation uptime limits explicit.
Use WSL as an Ansible control node with Linux Python, explicit inventories, SSH agent checks, and safe playbook tests against disposable hosts.
Use Apache Beam's DirectRunner in WSL to test pipeline logic, serialization, ordering assumptions, and assertions before validating a distributed runner.
Develop dbt projects inside WSL with pinned adapters, explicit profiles, compiled-SQL review, data tests, and a clean boundary from Windows tooling.
Use DuckDB inside WSL to query Parquet and local datasets, keep database files on ext4, and distinguish embedded analytics tests from server workloads.
Run JupyterLab inside WSL with Linux kernels, reproducible environments, token authentication, and deliberate host access rather than an exposed notebook server.
Develop Spark jobs in WSL local mode, keep JVM and filesystem boundaries clear, and test transformations without mistaking a laptop for a cluster.
Build and test a local Superset dashboard in WSL, isolate Python and metadata state, validate database connectivity, and avoid development-only security settings.
Follow Python from its CWI origins to a 1991 Usenet source release, examining the early language's design, distribution, and deliberately modest beginnings.
Build reproducible Python environments in WSL by separating Windows and Linux interpreters, using project virtual environments, and validating native dependencies.