DataWeave

Declarative Data Pipeline Framework -- Define ETL workflows in YAML. Transform, validate, profile, and load data with zero configuration.

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Why I Built This

Every data team I have worked with ends up writing the same boilerplate: load CSV, filter rows, compute columns, validate quality, dump results. The logic is simple but the wiring is tedious, and it scatters across Jupyter notebooks that nobody wants to maintain.

DataWeave replaces that boilerplate with a single YAML file. You declare what should happen to your data -- filter, transform, aggregate, validate -- and the framework handles the how. Think of it as Great Expectations meets dbt, but lightweight enough to pip-install and run in five minutes.

I wanted to build something that:

• Eliminates copy-paste ETL -- define once in YAML, run anywhere.
• Catches data quality issues early -- null checks, outlier detection, pattern matching built in.
• Generates human-readable reports -- hand the HTML profile to a stakeholder, no notebook required.
• Stays small -- no Spark cluster, no Airflow DAG, no cloud dependency. Just pandas and a YAML file.

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Quick Demo (60-Second Walkthrough)

# Install
pip install -e .

# Run a pipeline
dataweave run samples/pipeline.yaml -v

# Profile your data
dataweave profile samples/sales_data.csv

# Validate against rules
dataweave validate samples/sales_data.csv samples/validation_rules.yaml -v

What just happened:

1. run loaded 25 sales records, filtered out cancelled orders, computed revenue, sorted by date, and wrote cleaned output.
2. profile analyzed every column -- types, nulls, outliers, distributions -- and generated an HTML report.
3. validate checked 8 quality rules (not-null, unique, min/max, regex patterns) and reported pass/fail.

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Architecture

flowchart TD
    A[YAML Config] -->|loader.py| B[PipelineConfig Model]
    B -->|engine.py| C[PipelineEngine]
    D[CSV / JSON / Parquet] -->|pandas| C
    C --> E{Step Loop}
    E -->|filter| F[FilterOperator]
    E -->|transform| G[TransformOperator]
    E -->|aggregate| H[AggregateOperator]
    E -->|join| I[JoinOperator]
    E -->|validate| J[ValidateOperator]
    E -->|sort / select / rename / dedup / fill_na| K[Other Operators]
    F & G & H & I & J & K --> L[Transformed DataFrame]
    L -->|output| M[CSV / JSON / Parquet]
    L -->|profiler.py| N[DataProfile]
    N -->|report.py + Jinja2| O[HTML Report]

    style A fill:#e3f2fd,stroke:#1565c0
    style C fill:#e8f5e9,stroke:#2e7d32
    style O fill:#fff3e0,stroke:#e65100

Component Overview

Module	Responsibility
models.py	Pydantic models for YAML config validation
loader.py	Parse YAML into typed PipelineConfig
engine.py	Execute steps in sequence, collect timing
operators/	Pluggable operator implementations
schema.py	Automatic type detection and schema inference
profiler.py	Column-level statistics and anomaly detection
report.py	Jinja2-powered HTML report generation
cli.py	Click-based command-line interface

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Installation

# From source
git clone https://github.com/sonimaharshi1999/dataweave.git
cd dataweave
pip install -e ".[dev]"

# Run tests
python -m pytest tests/ -v

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Usage

Define a Pipeline in YAML

name: sales_etl
description: "Clean and transform sales data"

source:
  path: data/sales.csv
  format: csv

steps:
  - name: remove_cancelled
    operator: filter
    params:
      column: status
      op: ne
      value: cancelled

  - name: compute_revenue
    operator: transform
    params:
      expressions:
        revenue: "quantity * unit_price"

  - name: validate_quality
    operator: validate
    validations:
      - column: order_id
        check: not_null
        severity: error
      - column: quantity
        check: min
        value: 1
        severity: error

  - name: aggregate_by_region
    operator: aggregate
    params:
      group_by: region
      aggregations:
        revenue: sum
        quantity: sum

output:
  path: output/summary.csv
  format: csv

Available Operators

Operator	Description	Key Params
filter	Keep rows matching a condition	column, op, value
transform	Add/modify columns with expressions	expressions, drop
aggregate	Group-by aggregation	group_by, aggregations
join	Merge with another dataset	join.right_source, join.on, join.how
validate	Run data quality checks	validations[]
select	Keep only listed columns	columns
rename	Rename columns	columns (old -> new map)
sort	Sort by column(s)	by, ascending
deduplicate	Remove duplicate rows	subset, keep
fill_na	Fill missing values	columns, strategy

Filter Operators

Supported comparison operators for filter:

• eq, ne -- equals, not equals
• gt, ge, lt, le -- numeric comparisons
• in, not_in -- membership tests
• contains -- substring match

Transform Expressions

expressions:
  upper_name: "upper(customer_name)"      # String functions
  revenue: "quantity * unit_price"         # Arithmetic
  name_length: "len(customer_name)"       # Length

Programmatic API

from dataweave.loader import load_pipeline
from dataweave.engine import PipelineEngine
from dataweave.profiler import profile_dataframe

config = load_pipeline("pipeline.yaml")
engine = PipelineEngine(config)

# Run and get results
result = engine.run()
print(f"Success: {result.success}, Rows: {result.final_row_count}")

# Or get the DataFrame directly
df = engine.run_to_dataframe()

# Profile any DataFrame
profile = profile_dataframe(df)
print(f"Outliers in price: {profile.columns[2].outlier_count}")

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Performance / Benchmarks

Measured on a 2023 MacBook Pro (M3, 16 GB RAM) with synthetic CSV data:

Dataset Size	Columns	Pipeline Steps	Duration
1,000 rows	10	5 (filter + transform + sort + dedup + validate)	~15 ms
100,000 rows	10	5	~120 ms
1,000,000 rows	10	5	~1.2 s
1,000,000 rows	10	Profile (all columns)	~3.5 s

Key takeaways:

• Sub-second for datasets under 500K rows with typical pipelines.
• Profiling is the most expensive operation due to per-column statistics and outlier detection.
• Memory usage scales linearly with row count -- roughly 80 bytes/row for a 10-column dataset.

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What I Would Do Differently

1. Expression engine -- The current transform expressions use string parsing with regex. A proper AST-based expression evaluator (or even a safe subset of Python's eval with restricted globals) would support nested functions and complex arithmetic without fragile string splitting.

2. Streaming execution -- Right now every operator materializes the full DataFrame. For very large files, a chunked/streaming mode that processes N rows at a time would cut peak memory usage significantly.

3. Operator composition -- The step-by-step model is clean but forces intermediate DataFrames. A lazy evaluation approach (like Polars or Spark) that fuses operations before executing would improve performance on long pipelines.

4. Plugin system -- Operators are registered at import time. A proper entry-point-based plugin system would let users add custom operators without forking the package.

5. Incremental profiling -- The profiler scans every column every time. For append-only data sources, maintaining running statistics (count, mean, variance via Welford's algorithm) would make re-profiling near-instant.

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Scaling Considerations

• Vertical scaling: DataWeave rides on pandas, which is single-threaded. For datasets beyond ~10M rows, swap the pandas backend for Polars (drop-in for most operations) or Dask for out-of-core parallel processing.
• Horizontal scaling: The YAML config is portable. Wrap PipelineEngine.run() in an Airflow/Prefect task to distribute across workers. Each pipeline is stateless -- no shared state to coordinate.
• Data formats: CSV is the default, but Parquet support is built in. Switching to Parquet for large datasets cuts I/O time by 5-10x and enables predicate pushdown if you add a Polars backend.
• Validation at scale: The validate operator checks every row. For billion-row tables, sample-based validation (check a random 1% and extrapolate) would keep quality assurance practical.
• Caching: Intermediate step results could be cached to disk (keyed by step config hash) so re-running a modified pipeline skips unchanged upstream steps.

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Project Structure

dataweave/
  pyproject.toml
  README.md
  .gitignore
  .github/workflows/test.yml
  src/dataweave/
    __init__.py
    __version__.py
    py.typed
    models.py          # Pydantic config models
    loader.py          # YAML -> PipelineConfig
    engine.py          # Pipeline execution engine
    schema.py          # Schema inference & type detection
    profiler.py        # Column profiling & anomaly detection
    report.py          # HTML report generation (Jinja2)
    cli.py             # Click CLI (run, profile, validate)
    operators/
      __init__.py
      base.py          # Operator ABC & registry
      core.py          # All built-in operators
  samples/
    sales_data.csv     # Sample dataset (25 rows)
    pipeline.yaml      # Sample pipeline config
    validation_rules.yaml
  tests/
    conftest.py
    test_models.py
    test_schema.py
    test_operators.py
    test_engine.py
    test_profiler.py
    test_cli.py

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Sample Input / Output

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Project Overview

Reports

• HTML Report - interactive report
• PDF Report - downloadable PDF
• TXT Report - plain text

License

MIT License -- Maharshi Soni