introduction.md

Flow is a PHP based, strongly typed data processing framework with low memory footprint.

Supported PHP versions:

• ➡️ Installation
• 📜 Documentation
• 📈 Project Roadmap
• 🛠️ Contributing

Tip

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Table of contents

• ETL
• CLI
• Adapters
  • chartjs
  • csv
  • doctrine
  • elasticsearch
  • google sheet
  • http
  • json
  • logger
  • meilisearch
  • parquet
  • text
  • xml
• Libraries
  • array-dot
  • azure-sdk
  • doctrine-dbal-bulk
  • dremel
  • filesystem
  • parquet
  • parquet-viewer
  • snappy
• Bridges
  • filesystem-azure
  • monolog-http

Key Features

• Low memory footprint: Optimized for efficiency, Flow PHP ensures minimal memory usage, maintaining low and constant consumption regardless of data size. This makes it ideal for handling large datasets without compromising performance.
• Versatile Data Source Interaction: Whether your data resides in databases, spreadsheets, or online platforms, Flow PHP can seamlessly read from and write to any data source.
• Rich Collection of Data Transformation Functions: Transform your data with ease using a wide array of built-in functions. From simple mapping to complex manipulations, the framework covers all your data transformation needs.
• Direct Access to Remote Filesystems: Flow PHP provides the ability to interact directly with remote filesystems, facilitating efficient data handling and processing without the need for local storage.
• Advanced Data Partitioning: Efficiently partition your data for improved manageability and processing. This feature is crucial for handling large datasets or for processing data in distributed systems.
• Grouping & Aggregating: Easily group and aggregate data to extract meaningful insights. This feature is particularly useful for summarizing datasets and performing statistical analysis.
• Remote File Processing: Process files stored remotely with the same ease as local files, enabling powerful and flexible data integration from various sources.
• Join Operations: Perform join operations between different datasets. This is essential for combining data from multiple sources, providing a more comprehensive view.
• Efficient Sorting: Sort your data based on specific criteria or conditions, ensuring that your datasets are organized precisely as needed.
• ASCII Table Display: Visualize your datasets as neatly formatted ASCII tables, making it easier to read and analyze data directly from the console.
• Schema Validation: Ensure data quality and consistency by validating your datasets against predefined schemas.
• Window Functions: Utilize window functions for advanced data analysis, allowing you to perform calculations across sets of rows that are related to the current row.
• Built-In Caching Mechanism: Improve performance with built-in caching, reducing processing time and enhancing overall efficiency, especially in repetitive data processing tasks.

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