zeroetl 0.1.0

A Python package for ingesting data into Iceberg tables using PySpark.

zeroetl

zeroetl is a Python package designed to simplify data ingestion into Apache Iceberg tables using PySpark. It provides flexible functions for ingesting data from various sources (Pandas DataFrames, CSV files) and robust utilities for managing Iceberg tables, including schema creation, data deduplication, snapshot expiration, table compaction, and querying. The package emphasizes a "zero-ETL" approach by streamlining direct ingestion and leveraging Iceberg's capabilities for data quality and table optimization.

Features

• Flexible Data Ingestion:

  • Ingest data from Pandas DataFrames or CSV files with user-defined schema support.
  • Automatically converts string-based timestamp columns to PySpark TimestampType.

• Built-in Deduplication: Performs deduplication (similar to a MERGE operation) based on a configurable primary key and timestamp column, ensuring data quality.

• Iceberg Table Creation: Programmatically create or replace Iceberg tables with custom schemas, partitioning, and locations.

• Snapshot Management: Expire old snapshots to optimize storage and query performance.

• Table Optimization: Compact data files to improve read performance.

• Flexible Querying: Query the latest table state or perform time-travel queries using snapshot IDs.

• Configuration-Driven: Highly configurable via Python dictionaries for Spark settings, table names, and more.

• PySpark Integration: Leverages Apache Spark for scalable data processing.

Installation

Prerequisites

• Python 3.8+
• pip (Python package installer)
• Access to an S3-compatible storage for your Iceberg warehouse.
• AWS credentials configured as environment variables or in a .env file (see Configuration).

Install via Pip (Recommended for Users)

python -m venv venv
source venv/bin/activate  # On Windows: `venv\Scripts\activate`
pip install zeroetl

Install from Source (For Developers)

git clone https://github.com/your_username/zeroetl.git
cd zeroetl
python -m venv venv
source venv/bin/activate  # On Windows: `venv\Scripts\activate`
pip install -r requirements.txt
pip install -e .

Configuration

Create a .env file in your project directory with the following:

AWS_ACCESS_KEY_ID=YOUR_AWS_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY=YOUR_AWS_SECRET_ACCESS_KEY
AWS_BUCKET_NAME=s3a://your-iceberg-warehouse-bucket/

Quick Start

Set up your environment:

• Install zeroetl (see Installation).
• Create a .env file (see Configuration).
• Prepare a sample users.csv (e.g., in examples/users.csv):

id,name,email,signup_ts,age
5,David,[e-mail],2023-01-05T16:00:00.000Z,40
6,Eve,[e-mail],2023-01-06T17:00:00.000Z,45
7,Frank,[e-mail],2023-01-07T18:00:00.000Z,50

Create a pipeline script (e.g., my_pipeline.py):

import pandas as pd
from pyspark.sql.types import StructType, StringType, IntegerType, TimestampType
from zeroetl.ingestion import ingest_data_to_iceberg
from zeroetl.table_management import create_iceberg_table, query_iceberg_table
import os

# Ingestion configuration
INGESTION_CONFIG = {
    "table_name": "mycatalog.db.users",
    "primary_key_col": "id",
    "timestamp_col": "signup_ts",
    "warehouse_path": os.getenv("AWS_BUCKET_NAME"),
    "spark_configs": {"spark.sql.shuffle.partitions": "4"}
}

# Table management configuration
TABLE_MANAGEMENT_CONFIG = {
    "table_name": "mycatalog.db.users",
    "warehouse_path": os.getenv("AWS_BUCKET_NAME"),
    "spark_configs": {"spark.sql.shuffle.partitions": "4"},
    "query_type": "latest"
}

# Input schema
user_defined_schema = StructType() \
    .add("id", StringType()) \
    .add("name", StringType()) \
    .add("email", StringType()) \
    .add("signup_ts", StringType()) \
    .add("age", IntegerType())

# Table creation configuration
CREATE_TABLE_CONFIG = {
    "table_name": INGESTION_CONFIG["table_name"],
    "schema": "id STRING, name STRING, email STRING, signup_ts TIMESTAMP, age INT",
    "partition_spec": "days(signup_ts), bucket(16, id)",
    "location": "s3a://zero-etl-mesh-demo/warehouse/db/users",
    "warehouse_path": INGESTION_CONFIG["warehouse_path"]
}

def run_pipeline():
    try:
        # Create Iceberg table
        print(f"Creating table: {CREATE_TABLE_CONFIG['table_name']}")
        create_iceberg_table(CREATE_TABLE_CONFIG)

        # Ingest data from Pandas DataFrame
        pandas_data = pd.DataFrame({
            'id': ['1', '2', '4'],
            'name': ['Alice', 'Bob', 'Charlie'],
            'email': ['[e-mail]', '[e-mail]', '[e-mail]'],
            'signup_ts': ['2023-01-01T12:00:00.000Z', '2023-01-02T13:00:00.000Z', '2023-01-04T15:00:00.000Z'],
            'age': [25, 30, 35]
        })
        ingest_data_to_iceberg(
            input_data=pandas_data,
            schema=user_defined_schema,
            source_type='pandas_df',
            config=INGESTION_CONFIG
        )

        # Ingest data from CSV
        csv_file_path = 'examples/users.csv'
        ingest_data_to_iceberg(
            input_data=csv_file_path,
            schema=user_defined_schema,
            source_type='csv',
            config=INGESTION_CONFIG
        )

        # Query table
        query_iceberg_table(TABLE_MANAGEMENT_CONFIG)

    except Exception as e:
        print(f"Error: {e}")
        import traceback
        traceback.print_exc()

if __name__ == "__main__":
    run_pipeline()

Run pipeline

python my_pipeline.py