delta-lake-utils 1.0.0

Production-grade utilities for Delta Lake management

delta-lake-utils

Production-grade utilities for Delta Lake table management and optimization on Databricks.

What Does This Package Do?

Automates Delta Lake table optimization, health monitoring, and pipeline generation for Databricks data engineers.

Main Features:

1. Smart OPTIMIZE - Automatically consolidates small files and improves query performance

   • Detects when your Delta table has too many small files
   • Intelligently chooses which columns to Z-ORDER by
   • Reduces query time by up to 10x

2. Health Checker - Diagnoses table problems before they impact production

   • Identifies small file problems
   • Detects data skew across partitions
   • Finds configuration issues

3. Performance Profiler - Measures how fast your Delta operations run

   • Track read/write speeds
   • Identify bottlenecks
   • Compare before/after optimization

4. Medallion Generator - Auto-creates Bronze/Silver/Gold pipeline code

   • Generates production-ready notebooks
   • Follows best practices
   • Saves hours of boilerplate coding

5. Unity Catalog Auditor - Manages permissions and access control

   • Audits table permissions
   • Generates permission scripts
   • Ensures security compliance

Installation

pip install delta-lake-utils

Quick Start

from pyspark.sql import SparkSession
from delta_utils import DeltaOptimizer

spark = SparkSession.builder.getOrCreate()
optimizer = DeltaOptimizer(spark)

# Optimize a table - reduces files, improves performance
result = optimizer.auto_optimize('/mnt/delta/my_table')
print(f"Optimized! Removed {result.files_removed} files")

Use Cases

Use Case 1: Your queries are slow

Problem: Delta table has 5000 small files, queries take 10 minutes
Solution: Run optimizer, consolidates to 50 files, queries now take 1 minute

Use Case 2: Starting a new data pipeline

Problem: Need to build Bronze/Silver/Gold architecture from scratch
Solution: Use medallion generator, get complete pipeline in 30 seconds

Use Case 3: Data quality issues

Problem: Not sure if table is healthy, production keeps failing
Solution: Run health checker, get specific recommendations to fix issues

Use Case 4: Permission audit required

Problem: Need to verify all tables have correct access controls
Solution: Use catalog auditor to check and fix permissions

Documentation

• See QUICKSTART.md for 5-minute tutorial
• See EXPLANATION.md for detailed use cases
• See examples/EXAMPLES.md for code examples

Requirements

• Python 3.8+
• PySpark 3.2+
• Delta Lake 2.0+
• Databricks Runtime 11.0+ recommended

Author

Nalini Panwar GitHub: @panwarnalini-hub

License

MIT License - see LICENSE file for details