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Mountainash Ecosystem

One expression algebra. Every backend. The whole pipeline.

Mountainash is a composable Python data engineering ecosystem where every capability — expressions, rules, validation, schema conformance, pipelines — compiles to a single algebra that runs natively on Polars, pandas, DuckDB, Snowflake, BigQuery, and every backend that Ibis and Narwhals reach. The backend is a deployment decision, not a code decision.

This collection covers 12 library entries, with an inherited catalogue of 1,210 concepts across 115 chapters. Seven manuals are available; five remain deferred. Learning graphs and API references accompany the available books, while inherited MicroSim availability varies: some diagrams are static and some simulations are not implemented.

Publishing status: Rules has moved to independent per-repository publishing. The six manuals marked transitional still use their live central sites in both portal environments until their own cutovers. Deferred entries link to source repositories, not unpublished textbooks.

Core Framework

The foundation libraries that everything else builds on.

  • Mountainash Core

    Cross-backend expression and relational pipeline library. Write col(), lit(), when() in Polars-compatible syntax — compile to any backend. 18 terminal output formats.

    15 chapters · 200 concepts

    Open manual (transitional)

  • Mountainash Data

    Unified database and lakehouse connectivity. Ibis (23+ dialects), DuckDB, Apache Iceberg, Delta Lake — one connection interface for every analytical backend.

    9 chapters · 100 concepts

    Open manual (transitional)

  • Mountainash Settings

    Typed configuration framework. Layered resolution from YAML/TOML/env vars, secret injection from Vault or SSM before validation, auto-derived database connection parameters.

    9 chapters · 110 concepts

    Open manual (transitional)

Rules & Logic

Business rules engines and data transformation.

  • Mountainash Rules

    Vectorised backend-agnostic business rules engine. Decision tables with 11 match strategies, accumulator for combinatorial problems, per-rule explainability.

    9 chapters · 90 concepts

    Open manual

  • Mountainash Rules Babel

    Format translation for decision tables. Import from CSV, JDM, Drools. Export to DMN 1.3 with auto-generated FEEL expressions for regulatory compliance.

    9 chapters · 70 concepts

    Textbook deferred — unavailable. Source repository

  • Mountainash TreeSpec

    Bidirectional hierarchical data transformation. Flatten XML, JSON, Protobuf, Avro, and Pydantic into relational tables — and rebuild them with round-trip fidelity.

    9 chapters · 100 concepts

    Open manual (transitional)

Connectivity

API integration and authentication.

  • Mountainash API Client

    Declarative REST API integration. Define endpoints as typed dataclasses — the framework derives auth, pagination, retry, and rate-limiting.

    9 chapters · 80 concepts

    Textbook deferred — unavailable. Source repository (now mountainash-http-client)

  • Mountainash Auth Client

    Type-safe multi-provider authentication. OAuth 2.0, OIDC, API keys, HMAC, and 8 more auth methods via a discriminated-union configuration model.

    8 chapters · 85 concepts

    Textbook deferred — unavailable. Source repository

Utilities

Shared infrastructure libraries.

  • Mountainash Transport

    Cloud-native storage abstraction. Local filesystem, S3, GCS, and Azure Blob with a unified interface for read, write, list, and streaming operations.

    9 chapters · 90 concepts

    Textbook deferred — unavailable. Source repository

  • Mountainash Utils Secrets

    Unified multi-provider secret management. HashiCorp Vault, AWS Secrets Manager, GCP Secret Manager, Azure Key Vault, env vars, and dotenv.

    7 chapters · 70 concepts

    Open manual (transitional)

Applications

Domain-specific applications built on the framework.

  • Mountainash PointBreak

    Semantic dependency analysis for Python API evolution. Detect breaking changes, assess impact across consumers, and plan safe migrations.

    9 chapters · 95 concepts

    Open manual (transitional)

  • Mountainash Wearables

    Unified health and fitness data from wearable platforms. Garmin, Fitbit, Apple Health normalised into a common schema with backend-portable analytics.

    11 chapters · 120 concepts

    Textbook deferred — unavailable. Source repository

Where to Start

If you're new to the ecosystem, start with Mountainash Core (transitional) — it introduces the expression and relation abstractions that every other library builds on. From there, branch into whichever domain you need:

                    Mountainash Core
                    /       |       \
                   /        |        \
         Data + Settings   Rules    API Client + Auth
              |              |            |
         Transport      Rules Babel   PointBreak
         Utils Secrets   TreeSpec    Wearables

Each manual is self-contained, but cross-references core concepts where relevant. See the About page for persona-based guidance on which packages to start with.