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pudo/dataset

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dataset.readthedocs.org↗

Dataset

This project is a SQL data access layer and schema generator that allows for reading and writing records in relational databases by treating tables as simple data structures. It functions as an automatic schema generator that creates database tables and columns on the fly based on the structure of the incoming data.

The tool provides a high-performance bulk loader that imports large datasets using grouped atomic transactions to ensure data consistency. It also includes a record upsert mechanism that determines whether to update an existing row or insert a new one based on unique identifiers.

The system covers dynamic schema management, including implicit column resolution and table provisioning. It further provides a collection-based query interface for retrieving records or extracting unique values without writing manual queries.

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Features

  • Dynamic Column Resolutions - Detects new fields in incoming data and modifies the database schema to accommodate them without manual migrations.
  • SQL Data Access Layers - Read and write records by treating tables as simple data structures instead of writing manual queries.
  • Dynamic Schema Evolution - Automatically creating and updating database tables and columns on the fly based on the structure of incoming data.
  • SQL Translation Layers - Provides a translation layer that allows records to be read and written by treating database tables as simple data structures.
  • Schema-on-Write Generators - Create tables and columns automatically when writing data to a destination that does not yet exist.
  • Bulk Data Loading - Efficiently importing large sets of records into a database using bulk loading and transaction support.
  • Collection Querying - Wraps SQL SELECT statements in simplified methods to retrieve full tables or unique column values.
  • Data Abstraction Layers - Maps database rows to simple data structures to allow record manipulation without writing manual SQL queries.
  • Upsert Operations - Implements a mechanism to update existing rows or insert new ones based on the presence of unique identifiers.
  • On-the-Fly Table Creations - Automatically creates database tables and columns based on the structure of the data being inserted at runtime.
  • Dynamic Column Provisioning - Automatically generates and modifies database tables and columns on the fly based on the structure of the incoming data.
  • Dynamic Column Resolution - A tool that creates SQL tables and columns on the fly based on the structure of the data being written.
  • Upsert Operations - Update existing rows or insert new ones based on whether a matching record exists in the table.
  • Bulk Loaders - Groups multiple data insertions into a single atomic transaction to increase write throughput and ensure data consistency.
  • Transactional Updates - Groups multiple data insertions into a single transaction to increase write performance and ensure atomic updates.
  • Database Transaction Wrappers - Wraps multiple database operations into atomic transactions to ensure data consistency and increase write performance during bulk loading.
  • Upsert Operations - Checks for existing records using unique identifiers to determine whether to update a row or insert a new one.
  • Database Clients - Data handling for SQL stores.
  • Database ORMs - Listed in the “Database ORMs” section of the Awesome Python awesome list.
  • Database Tools - JSON-like interface for working with SQL databases.
  • Object Relational Mappers - Stores Python dicts in relational databases.
  • ORM Frameworks - Stores dictionaries in databases.
4,865 stars·299 forks·Python·MIT·24 views

Star history

Star history chart for pudo/datasetStar history chart for pudo/dataset

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does pudo/dataset do?

This project is a SQL data access layer and schema generator that allows for reading and writing records in relational databases by treating tables as simple data structures. It functions as an automatic schema generator that creates database tables and columns on the fly based on the structure of the incoming data.

What are the main features of pudo/dataset?

The main features of pudo/dataset are: Dynamic Column Resolutions, SQL Data Access Layers, Dynamic Schema Evolution, SQL Translation Layers, Schema-on-Write Generators, Bulk Data Loading, Collection Querying, Data Abstraction Layers.

Which projects share features with pudo/dataset?

Projects with overlapping indexed features include: ponyorm/pony — Pony is a Python object-relational mapper that maps classes to relational database tables using an object-oriented… sqlalchemy/sqlalchemy — SQLAlchemy is a comprehensive Python SQL toolkit and object-relational mapper that provides a full suite of tools for… coleifer/peewee — Peewee is a SQL object-relational mapper and query builder that provides an object-oriented interface for mapping… dotnetcore/freesql — FreeSql is a .NET object-relational mapper and data access layer that translates object-oriented code into SQL for… uptrace/bun — Bun is a type-safe object relational mapper for Go that prioritizes SQL-first query construction and result mapping.… redis/go-redis — This project is a feature-rich Go client library designed for interacting with Redis. It serves as a comprehensive…

Projects sharing features with Dataset

These projects share indexed features with Dataset. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
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    sqlalchemy/sqlalchemy

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    coleifer/peewee

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    Peewee is a SQL object-relational mapper and query builder that provides an object-oriented interface for mapping application classes to relational database tables. It functions as a relational database toolkit for managing schemas, executing migrations, and handling complex table relationships. The project distinguishes itself by providing an asyncio database driver for non-blocking database operations, ensuring event loop responsiveness. It also supports semi-structured data storage, allowing the storage and querying of flexible JSON documents within traditional relational database systems.

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    FreeSql is a .NET object-relational mapper and data access layer that translates object-oriented code into SQL for multiple relational database providers. It functions as a fluent SQL query builder and database schema synchronizer, allowing developers to align database table and index structures with entity class definitions. The framework is specifically optimized for .NET Native AOT to ensure reduced memory footprints and faster startup times. It includes a database traffic manager to distribute load through read-write splitting, dynamic table sharding, and tenant-based data isolation. Bro

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