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Gluestick is an open source ETL toolkit developed and maintained by hotglue. It is optimized for usage in hotglue pre-processing scripts. The code is available on GitHub and is free to use under the MIT license.

GitHub Repository

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Getting started with gluestick

# Install from PyPI
pip install gluestick

# Import utilities
import gluestick as gs

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Key Features

  • Robust ETL utilities for data processing
  • Singer protocol integration
  • Advanced JSON and object handling
  • Snapshot management for incremental loads
  • Production-ready error handling

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File Reading Functions

Reader

RECOMMENDED: Class for reading sync-output data. Provides methods to read directories, get file metadata, and extract primary keys from parquet files.

read_csv_folder

Reads multiple CSV files from a directory, organizing them by entity type based on filename. Supports custom converters and index columns per entity.

read_parquet_folder

Similar to read_csv_folder but for Parquet files. Automatically organizes files by entity type and supports ignoring specific files.

get_catalog_schema

Retrieves DataFrame schema from Singer catalog.

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Snapshot Management

snapshot_records

Manages data snapshots by updating existing snapshots or creating new ones. Supports type coercion and handles both CSV and Parquet formats.

read_snapshots

Reads snapshot data for a specific stream from either Parquet or CSV format. Supports additional pandas read options.

drop_redundant

Removes duplicate rows based on content hashing. Maintains state of processed data and supports update tracking.

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JSON & Object Handling

json_tuple_to_cols

Converts JSON tuple columns into separate columns based on key-value pairs.

explode_json_to_rows

Explodes array of objects into multiple rows with columns for each object key.

explode_json_to_cols

Converts JSON array columns into separate columns, with one column per array value.

compress_rows_to_col

Compresses exploded rows back into a single column with array data.

array_to_dict_reducer

Converts arrays into dictionaries using specified key-value properties.

clean_obj_null_values

Replaces null values with None in stringified objects for further processing.

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Data Transformation

clean_convert

Recursively cleans None values from lists and dictionaries. Handles nested structures and datetime conversions.

map_fields

Maps row values according to a specified mapping dictionary. Supports nested structures and conditional mapping.

rename

Renames DataFrame columns using JSON format with type conversion support.

localize_datetime

Localizes datetime columns to UTC timezone. Handles both naive and timezone-aware timestamps.

deep_convert_datetimes

Transforms all nested datetimes to ISO format recursively.

exception

Standardized error handling with file logging. Creates consistent error reporting across ETL pipelines.

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Data Export & Error Handling

to_export

Exports data to various formats (Singer, Parquet, JSON, JSONL, CSV). Supports schema validation, object stringification, and custom formatting.

to_singer

Exports DataFrame to Singer format with schema validation and type handling.