weevr 1.3.0

Metadata-driven execution engine for Fabric/Spark data pipelines.

weevr

Configuration-driven data shaping for Spark in Microsoft Fabric.

weevr lets you declare data transformation intent in YAML and execute it on PySpark. Define what should happen to your data — sources, transforms, joins, validations, write behavior — and let a stable engine handle how it runs. No code generation, no abstraction leaks. Just Spark DataFrame operations driven by configuration.

Installation

pip install weevr

Quick Start

Define a thread — the smallest unit of work — using a .thread file:

# dim_customer.thread
config_version: "1.0"
sources:
  raw_customers:
    type: delta
    path: "${lakehouse_path}/raw/customers"
steps:
  - filter:
      expr: "is_active = true"
  - select:
      columns: [customer_id, name, email, region]
target:
  path: "${lakehouse_path}/curated/dim_customer"
write:
  mode: overwrite

Run it from a Fabric Notebook or any PySpark environment:

from weevr import Context

ctx = Context(spark, "my-project.weevr")
result = ctx.run("dim_customer.thread")
print(result.status)  # "success"

Features

• Declarative transforms — Filter, join, dedup, sort, rename, cast, derive, select, drop, aggregate, window, pivot, and union — all expressed in YAML
• Flexible write modes — Overwrite, append, and merge (upsert) with configurable match, update, and delete behavior
• Validation and data quality — Pre-write rules with severity routing (info, warn, error, fatal) and automatic row quarantine; post-write assertions for row counts, null checks, uniqueness, and custom expressions
• DAG orchestration — Automatic dependency resolution, parallel thread execution within weaves, sequential weave ordering, configurable failure behavior, and auto-cache management
• Configuration inheritance — Define patterns once at loom or weave level, cascade to threads with child-wins semantics
• Variable injection — Environment-agnostic configs with parameter files and runtime overrides
• Incremental processing — Watermark-based incremental loads, CDC merge routing with hard/soft delete support, and Delta Change Data Feed integration
• Observability — OTel-compatible execution spans, structured JSON logging, row count reconciliation, and execution trace trees
• Null-safe defaults — Opinionated join semantics and key handling that prevent common Spark pitfalls
• Python API — Context class with run() for execution, load() for config inspection, and verification modes for dry-run validation

Core Abstractions

weevr organizes work through four concepts:

• Thread — The smallest executable unit. Reads from one or more sources, applies transforms, validates, and writes to a single target.
• Weave — A collection of threads forming a dependency DAG. Represents a subject area or processing stage. Independent threads run in parallel.
• Loom — A deployable unit packaging one or more weaves with defined execution order. The primary unit of versioning and release.
• Project — A logical grouping of looms. Defines the boundary for shared configuration, parameter files, helpers, and UDFs.

Core Principles

• Declarative intent, imperative execution — Configuration is read at runtime and drives execution directly. No code generation from YAML.
• Spark-native, Fabric-aligned — All execution uses Spark DataFrame APIs inside Fabric's runtime. No external systems or runtimes.
• Deterministic and idempotent — Same configuration and inputs produce consistent behavior. Safe to rerun.
• Opinionated defaults, configurable overrides — Safe defaults for null handling, join behavior, and failure semantics. Override when you need to.
• Configuration reuse through inheritance — Define patterns once at higher levels and inherit down. Reduces effort, enforces standards.

Non-goals

weevr is intentionally not:

• A low-code or no-code platform
• A visual workflow designer
• A replacement for Spark or re-implementation of data processing primitives
• An abstraction layer that hides the underlying execution engine

The goal is to reduce orchestration friction and enforce repeatable patterns — not to obscure how data is processed.

Target Audience

• Analysts who know SQL but not Spark
• Data engineers who want config-driven consistency
• Teams building medallion architectures in Fabric
• Anyone seeking repeatable, governed data transformation patterns

Deployment Model

weevr's engine is a general-purpose library distributed via PyPI. It contains no project-specific configuration.

Integration projects are separate repositories containing YAML configs, Fabric Notebooks, and any project-specific UDFs or helpers. This separation lets the engine evolve independently while teams own their configuration.

Compatibility

Component	Version
Python	3.11
PySpark	3.5
Delta Lake	3.2
Microsoft Fabric Runtime	1.3

What's Next

• Extensibility — Reusable stitch patterns, project-level UDF and helper registries
• Developer tooling — Test framework, CLI validation, dry-run modes
• Advanced merge patterns — Insert-only mode, complex update strategies

Documentation

Full documentation is available at ardent-data.github.io/weevr.

Contributing

See CONTRIBUTING.md for development setup, workflow expectations, and pull request conventions. Contributions are welcome.

License

Apache License 2.0. See LICENSE for details.