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Apache NiFi vs riko

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Apache NiFi
357
691
+ 1
65
riko
0
6
+ 1
0
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Apache NiFi vs riko: What are the differences?

What is Apache NiFi? A reliable system to process and distribute data. 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.

What is riko? A Python stream processing engine modeled after Yahoo! Pipes. 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.

Apache NiFi and riko can be categorized as "Stream Processing" tools.

Some of the features offered by Apache NiFi are:

  • Web-based user interface
  • Highly configurable
  • Data Provenance

On the other hand, riko provides the following key features:

  • Read csv/xml/json/html files
  • Create text and data based flows via modular pipes
  • Parse, extract, and process RSS/Atom feeds

riko is an open source tool with 1.47K GitHub stars and 67 GitHub forks. Here's a link to riko's open source repository on GitHub.

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Pros of Apache NiFi
Pros of riko
  • 17
    Visual Data Flows using Directed Acyclic Graphs (DAGs)
  • 8
    Free (Open Source)
  • 7
    Simple-to-use
  • 5
    Scalable horizontally as well as vertically
  • 5
    Reactive with back-pressure
  • 4
    Fast prototyping
  • 3
    Bi-directional channels
  • 3
    End-to-end security between all nodes
  • 2
    Built-in graphical user interface
  • 2
    Can handle messages up to gigabytes in size
  • 2
    Data provenance
  • 1
    Lots of documentation
  • 1
    Hbase support
  • 1
    Support for custom Processor in Java
  • 1
    Hive support
  • 1
    Kudu support
  • 1
    Slack integration
  • 1
    Lot of articles
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    Cons of Apache NiFi
    Cons of riko
    • 2
      HA support is not full fledge
    • 2
      Memory-intensive
    • 1
      Kkk
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      - No public GitHub repository available -

      What is 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.

      What is riko?

      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.

      Need advice about which tool to choose?Ask the StackShare community!

      What companies use Apache NiFi?
      What companies use riko?
        No companies found
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        What tools integrate with Apache NiFi?
        What tools integrate with riko?

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        What are some alternatives to Apache NiFi and riko?
        Kafka
        Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
        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.
        Logstash
        Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana.
        Apache Camel
        An open source Java framework that focuses on making integration easier and more accessible to developers.
        Apache Spark
        Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
        See all alternatives

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