sifft 0.9.0

Spark Ingestion Framework for Tables (SIFFT) - a Python library providing a consistent interface for ingesting files into Apache Spark environments.

SIFFT

File ingestion pipelines are repetitive and fragile. SIFFT (Spark Ingestion Framework For Tables) is a Python library that aims to:

• Make file reading format-agnostic with automatic delimiter and header detection
• Validate data quality with CSVW metadata and constraint checking before it enters your tables
• Provide consistent error handling across CSV, Excel, TSV, and pipe-delimited files
• Support schema evolution and merge/upsert operations for Delta tables
• Reduce boilerplate code for common Spark ingestion patterns

Install

pip install sifft              # Without PySpark (use your Databricks runtime)
pip install sifft[pyspark3]    # With PySpark 3.5 + Delta Lake 3.x
pip install sifft[pyspark4]    # With PySpark 4.x + Delta Lake 4.x

Quick Start

from pyspark.sql import SparkSession
from file_processing import process_file
from dataframe_validation import validate_csvw_constraints
from table_writing import write_table, TableWriteOptions

spark = SparkSession.builder.appName("Pipeline").getOrCreate()

# Read (auto-detects format, delimiter, header)
result = process_file("data.csv", spark)

# Validate (if CSVW metadata exists alongside the file)
if result.metadata:
    report = validate_csvw_constraints(result.dataframe, result.metadata)
    assert report.valid, f"{len(report.violations)} violations"

# Write
write_table(result.dataframe, "catalog.schema.target", spark,
            TableWriteOptions(format="delta", mode="append"))

Features

• File Processing — CSV, TSV, pipe-delimited, Excel. Auto-detection of delimiters and headers. Checksum-based deduplication.
• Data Validation — CSVW constraint checking: required, unique, min/max, pattern, enum, primary keys.
• Table Writing — Delta, Parquet, ORC. Append, overwrite, merge/upsert, schema evolution.
• File Management — Safe move/list across local, S3, Azure, GCS.
• Extensibility — Custom format handlers, write modes, and constraint validators.

Documentation

• Getting Started — installation, requirements, full quick start
• File Processing — reading files, tracking, deduplication
• Data Validation — schema validation and CSVW constraints
• Table Writing — write modes, formats, schema evolution
• File Management — safe move/list with cloud support
• CSVW Metadata — metadata format and constraint reference
• Extensibility — custom handlers and validators
• AutoLoader Integration — using SIFFT with Databricks AutoLoader

Compatibility

• Python 3.10+
• PySpark 3.5.x, 4.0.x, or 4.1.x
• Databricks / Unity Catalog compatible

Development

just test        # Run tests in Docker
just test-local  # Run tests locally (requires Java)
just --list      # All available commands

Design Decisions

Architecture Decision Records are in design_decisions/.

License

MIT — see LICENSE.

Contributors

• Iwan Dyke
• Fahad Khan
• Michal Poreba