Search Results for "input"

...You can use Bytewax for a variety of workloads from moving data à la Kafka Connect style all the way to advanced online machine learning workloads. Bytewax is not limited to streaming applications but excels anywhere that data can be distributed at the input and output.

A lightweight stream processing library for Go. go-streams provides a simple and concise DSL to build data pipelines. In computing, a pipeline, also known as a data pipeline, is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion. Some amount of buffer storage is often inserted between elements.

Numaflow is a Kubernetes-native tool for running massively parallel stream processing. A Numaflow Pipeline is implemented as a Kubernetes custom resource and consists of one or more source, data processing, and sink vertices. Numaflow installs in a few minutes and is easier and cheaper to use for simple data processing applications than a full-featured stream processing platform.

Strings edit is a library that provides I/O facilities for integers, floating-point numbers, Roman numerals, and strings. Both input and output subroutines support string pointers for consequent stream processing. The output can be aligned in a fixed size field with padding. Numeric input can be checked against expected values range to be either saturated or to raise an exception. For floating-point output either relative or absolute output precision can be specified. ...

Amadeus is a high-performance, distributed data processing framework written in Rust, designed to offer an ergonomic and safe alternative to tools like Apache Spark. It provides both streaming and batch capabilities, allowing users to work with real-time and historical data at scale. Thanks to Rust’s memory safety and zero-cost abstractions, Amadeus delivers performance gains while reducing the complexity and bugs common in large-scale data pipelines. It emphasizes developer productivity...

...The primary target is Java log files (Log4J, common, ...), where it is very common to have multiline log entries (for example log entries with a stacktrace). It follows Unix philosophy, does only its primary job and expects its input to be generated by other more advanced tools (tail, cat, type, find...); There is no goal to be compatible with Unix grep. LogsGrep is written in the Java programming langue having performance and low resource usage in mind (no strings, no object creation, stream-processing).