datablade 0.0.5

datablade is a suite of functions to provide standard syntax across data engineering projects.

datablade

datablade is a small, single-machine Python toolkit for data engineers who need reliable “file → DataFrame/Parquet → SQL DDL” workflows.

It focuses on:

• Reading common file formats with memory-aware heuristics
• Streaming large files in chunks (without concatenating)
• Normalizing DataFrame columns for downstream systems
• Generating CREATE TABLE DDL across a small set of SQL dialects
• Producing bulk-load commands (and executing BCP for SQL Server)

What datablade Does

datablade helps data engineers:

• Load data efficiently from common file formats with automatic memory heuristics
• Standardize data cleaning with consistent column naming and type inference
• Generate database schemas for multiple SQL dialects from DataFrames or Parquet schemas
• Handle datasets that don't fit in memory using chunked iteration and optional Polars acceleration
• Work across databases with cross-dialect DDL and bulk-load command generation
• Maintain data quality with built-in validation and logging

When to Use datablade

datablade is ideal for:

✅ ETL/ELT Pipelines - Building reproducible data ingestion workflows across multiple source formats

✅ Multi-Database Projects - Deploying the same schema to SQL Server, PostgreSQL, MySQL, or DuckDB

✅ Large File Processing - Streaming CSV/TSV/TXT/Parquet without concatenating

✅ Data Lake to Warehouse - Converting raw files to Parquet with optimized schemas

✅ Ad-hoc Data Analysis - Quickly exploring and preparing datasets with consistent patterns

✅ Legacy System Integration - Standardizing messy column names and data types from external sources

When datablade is not the right tool

• Real-time streaming ingestion (Kafka, Spark Structured Streaming)
• Distributed compute / cluster execution (Spark, Dask)
• Warehouse-native transformations and modeling (dbt)
• A full-featured schema migration tool (Alembic, Flyway)
• Direct database connectivity/transactions (datablade generates SQL; it does not manage connections)

Installation

pip install git+https://github.com/brentwc/data-prep.git

Optional dependencies:

# For high-performance file reading with Polars
pip install git+https://github.com/brentwc/data-prep.git#egg=datablade[performance]

# For development and testing
pip install git+https://github.com/brentwc/data-prep.git#egg=datablade[dev]

# All optional dependencies
pip install git+https://github.com/brentwc/data-prep.git#egg=datablade[all]

Features

datablade provides four main modules:

📊 datablade.dataframes

DataFrame operations and transformations:

• Clean and normalize DataFrame columns
• Auto-detect and convert data types
• Generate optimized Parquet schemas
• Convert pandas DataFrames to PyArrow tables
• Generate multi-dialect SQL DDL statements
• Memory-aware file reading with automatic chunking
• Polars integration for high-performance large file processing
• Partitioned Parquet writing for datasets that don't fit in memory

🌐 datablade.io

Input/output operations for external data:

• Fetch JSON data from URLs
• Download and extract ZIP files

🛠️ datablade.utils

General utility functions:

• SQL name quoting
• Path standardization
• List flattening
• Configurable logging with Python logging module

🗄️ datablade.sql

Multi-dialect SQL utilities:

• Multi-dialect support: SQL Server, PostgreSQL, MySQL, DuckDB
• Dialect-aware identifier quoting
• CREATE TABLE generation for all dialects (from pandas DataFrames)
• CREATE TABLE generation from Parquet schemas (schema-only, via PyArrow)
• Bulk loading helpers:
  • SQL Server: executes bcp via subprocess
  • PostgreSQL/MySQL/DuckDB: returns command strings you can run in your environment

Quick Start

import pandas as pd
from datablade import configure_logging, read_file_smart
from datablade.dataframes import clean_dataframe_columns, pandas_to_parquet_table
from datablade.io import get_json
from datablade.utils import sql_quotename
from datablade.sql import Dialect, generate_create_table, generate_create_table_from_parquet

# Configure logging
import logging
configure_logging(level=logging.INFO, log_file="datablade.log")

# Read a file into a single DataFrame (materializes)
df = read_file_smart('large_dataset.csv', verbose=True)

# Clean DataFrame
df = clean_dataframe_columns(df, verbose=True)

# Convert to Parquet
table = pandas_to_parquet_table(df, convert=True)

# Generate SQL DDL for multiple dialects
sql_sqlserver = generate_create_table(df, table='my_table', dialect=Dialect.SQLSERVER)
sql_postgres = generate_create_table(df, table='my_table', dialect=Dialect.POSTGRES)

# Generate SQL DDL directly from an existing Parquet schema (no data materialization)
# Note: nested Parquet types (struct/list/map/union) are dropped with a warning.
ddl_from_parquet = generate_create_table_from_parquet(
    "events.parquet",
    table="events",
    dialect=Dialect.POSTGRES,
)

# Fetch JSON data
data = get_json('https://api.example.com/data.json')

Memory-Aware File Reading

from datablade.dataframes import read_file_chunked, read_file_iter, read_file_to_parquets, stream_to_parquets

# Read large files in chunks
for chunk in read_file_chunked('huge_file.csv', memory_fraction=0.5):
    process(chunk)

# Stream without ever concatenating/materializing
for chunk in read_file_iter('huge_file.csv', memory_fraction=0.3, verbose=True):
    process(chunk)

# Parquet is also supported for streaming (single .parquet files)
for chunk in read_file_iter('huge_file.parquet', memory_fraction=0.3, verbose=True):
    process(chunk)

# Partition large files to multiple Parquets
files = read_file_to_parquets(
    'large_file.csv',
    output_dir='partitioned/',
    convert_types=True,
    verbose=True
)

# Stream to Parquet partitions without materializing
files = stream_to_parquets(
    'large_file.csv',
    output_dir='partitioned_streamed/',
    rows_per_file=200_000,
    convert_types=True,
    verbose=True,
)

Blade (Optional Facade)

The canonical API is module-level functions (for example, datablade.dataframes.read_file_iter).

If you prefer an object-style entrypoint with shared defaults, you can use the optional Blade facade:

from datablade import Blade
from datablade.sql import Dialect

blade = Blade(memory_fraction=0.3, verbose=True, convert_types=True)

for chunk in blade.iter("huge.csv"):
    process(chunk)

files = blade.stream_to_parquets("huge.csv", output_dir="partitioned/")

# Generate DDL (CREATE TABLE)
ddl = blade.create_table_sql(
    df,
    table="my_table",
    dialect=Dialect.POSTGRES,
)

# Generate DDL from an existing Parquet file (schema-only)
ddl2 = blade.create_table_sql_from_parquet(
    "events.parquet",
    table="events",
    dialect=Dialect.POSTGRES,
)

Documentation

• Docs Home - Documentation landing page
• Usage Guide - File reading (including streaming), SQL, IO, logging
• Testing Guide - How to run tests locally
• Test Suite - Testing documentation and coverage

Testing

Run the test suite:

# Install with test dependencies
pip install -e ".[test]"

# Run all tests
pytest

# Run with coverage report
pytest --cov=datablade --cov-report=html

See tests/README.md for detailed testing documentation.

Backward Compatibility

All functions are available through the legacy datablade.core module for backward compatibility:

# Legacy imports (still supported)
from datablade.core.frames import clean_dataframe_columns
from datablade.core.json import get

Requirements

Core dependencies:

• pandas
• pyarrow
• numpy
• openpyxl
• requests

Design choices and limitations

• Single-machine focus: datablade is designed for laptop/VM/server execution, not clusters.
• Streaming vs materializing:
  • Use read_file_iter() to process arbitrarily large files chunk-by-chunk.
  • read_file_smart() returns a single DataFrame and may still be memory-intensive.
• Parquet support:
  • Streaming reads support single .parquet files.
  • Parquet “dataset directories” (Hive partitions / directory-of-parquets) are not a primary target API.
• Parquet → SQL DDL:
  • Uses the Parquet schema (PyArrow) without scanning data.
  • Complex/nested columns (struct/list/map/union) are dropped and logged as warnings.
• DDL scope: CREATE TABLE generation is column/type oriented (no indexes/constraints).

Optional dependencies:

• polars (for high-performance file reading)
• psutil (for memory-aware operations)
• pytest (for testing)

License

MIT