matz/streem

Features

• Data Pipeline Orchestration - Links data sources to destinations using a domain language to process elements through a sequence of operations.
• Stream-Based Languages - Ships a custom programming language and script syntax dedicated to defining concurrent data flows and pipeline processing.
• Collection Element Transformations - Applies a function to each element in a pipeline to modify or filter data before passing it to the next stage.
• Concurrent Data Processors - Handles multiple pieces of information simultaneously by passing them through a pipeline of independent functions.
• Concurrent Stream Processing - Executes multiple pipeline stages simultaneously to move data elements through the system with higher throughput.
• Data Flow Definitions - Provides a domain-specific language to declaratively specify the routing and connection of data sources and processing components.
• Data Pipeline Orchestrators - Implements a platform for automating complex sequences of data processing and transformation tasks.
• Stream and Pipeline Orchestration - Offers an engine for managing the flow, transformation, and execution of continuous data streams.
• Per-Element Effect Execution - Executes a function for every individual item passing through a pipeline to modify or filter the data.
• Data Transformation Functions - Applies specific functions to individual items in a stream to modify or filter information.
• Concurrent Data Pipelines - Moves data through multiple simultaneous stages to increase throughput via functional transformations.
• Value-Flow Pipelines - Processes individual data elements through a sequential chain of linked operations from source to destination.
• Data Flow Scripting - Connects processing stages using a stream script to manage how information moves concurrently through the system.
• Domain Specific Language Parsers - Converts custom script syntax into an internal representation of connected processing stages for execution.
• Functional Transformations - Applies pure functions to each item in a stream to modify or filter data before the next stage.
• Domain Specific Languages - Provides a specialized language to simplify the definition and management of complex data flow workflows.
• Concurrent Task Execution - Manages the simultaneous movement of data through multiple pipeline stages to increase processing throughput.
• Flow-Based Programming Models - Defines how data flows through a sequence of connected processing stages using a specialized language.
• Pipeline Definitions - Parses custom script syntax into an internal graph of connected processing stages to automate data flow setup.
• Functional Stream Transformations - Applies mapping or filtering functions to individual items as they pass through a sequential chain.
• Data Flow Orchestrators - Manages the movement of information from a source to a destination through a series of linked operations.
• Data Transformation Pipelines - Builds a series of operations to move data from a source to a destination while transforming and filtering it.
• Stage Composition - Connects discrete processing units into a larger pipeline by piping the output of one function into the input of another.
• Pipeline Execution Models - Processes data by passing elements through a sequence of independent functions that transform and filter information.

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