Humanify vs riko
Overview
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CLI (Node.js)
Manual
Detailed Comparison
riko is a pure Python library for analyzing and processing streams of structured data. riko has synchronous and asynchronous APIs, supports parallel execution, and is well suited for processing RSS feeds. riko also supplies a command-line interface for executing flows, i.e., stream processors aka workflows. | It is a free and open source server and web application, written in Node.js, that allows adding human intelligence to data streaming in scenarios where computers are not suitable to make educated enough choices. In just a couple lines of code it will ingest your data stream, open an HTTP server with a WebApplication that will be fed with all the data from the stream. Now you and your team can add decisions to each item of your data stream. |
Read csv/xml/json/html files;Create text and data based flows via modular pipes;Parse, extract, and process RSS/Atom feeds;Create awesome mashups, APIs, and maps;Perform parallel processing via cpus/processors or threads | Simple installation;
Fast data review;
Human in the loop input;
Open source |
Statistics | |
GitHub Stars 1.6K | GitHub Stars 7 |
GitHub Forks 75 | GitHub Forks 1 |
Stacks 0 | Stacks 0 |
Followers 6 | Followers 1 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to riko, Humanify?
Apache NiFi
An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
Apache Storm
Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.
Confluent
It is a data streaming platform based on Apache Kafka: a full-scale streaming platform, capable of not only publish-and-subscribe, but also the storage and processing of data within the stream
KSQL
KSQL is an open source streaming SQL engine for Apache Kafka. It provides a simple and completely interactive SQL interface for stream processing on Kafka; no need to write code in a programming language such as Java or Python. KSQL is open-source (Apache 2.0 licensed), distributed, scalable, reliable, and real-time.
Heron
Heron is realtime analytics platform developed by Twitter. It is the direct successor of Apache Storm, built to be backwards compatible with Storm's topology API but with a wide array of architectural improvements.
Kafka Streams
It is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology.
Kapacitor
It is a native data processing engine for InfluxDB 1.x and is an integrated component in the InfluxDB 2.0 platform. It can process both stream and batch data from InfluxDB, acting on this data in real-time via its programming language TICKscript.
Redpanda
It is a streaming platform for mission critical workloads. Kafka® compatible, No Zookeeper®, no JVM, and no code changes required. Use all your favorite open source tooling - 10x faster.
Faust
It is a stream processing library, porting the ideas from Kafka Streams to Python. It provides both stream processing and event processing, sharing similarity with tools such as Kafka Streams, Apache Spark/Storm/Samza/Flink.
Samza
It allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka.
