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Apache NiFi

365
681
+ 1
65
EMQX

30
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+ 1
6
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Apache NiFi vs EMQ: What are the differences?

Apache NiFi and EMQ are two popular technologies used for data processing and messaging in the world of big data. Below are the key differences between Apache NiFi and EMQ:

  1. Architecture: Apache NiFi follows a flow-based programming paradigm where data is processed by connecting predefined processors in a flow. On the other hand, EMQ is a distributed and scalable MQTT broker that follows a publish/subscribe messaging pattern.

  2. Use Cases: Apache NiFi is widely used for data ingestion, routing, transformation, and processing tasks. It is particularly suitable for building data pipelines and ETL processes. EMQ, on the other hand, is primarily used for real-time messaging applications such as IoT and M2M communication.

  3. Scalability: Apache NiFi can be scaled horizontally by adding more nodes to the cluster. This allows for high availability and increased processing capacity. EMQ is inherently designed for scalability and can handle a large number of concurrent connections efficiently.

  4. Integration: Apache NiFi provides a rich set of processors for integrating with various data sources and destinations, including databases, APIs, and IoT devices. EMQ seamlessly integrates with MQTT clients and brokers, making it easy to build MQTT-based applications.

  5. Monitoring and Management: Apache NiFi comes with a user-friendly web interface that allows users to monitor data flows, track performance metrics, and manage the system. EMQ also provides a web dashboard for monitoring MQTT connections, message rates, and broker status.

  6. Community and Support: Apache NiFi has a large and active community with extensive documentation, tutorials, and community-contributed processors. EMQ, being primarily focused on MQTT, has a niche community but offers commercial support options for enterprise users.

In Summary, Apache NiFi is ideal for data processing and ETL tasks with a flow-based programming model, while EMQ is best suited for real-time messaging applications using MQTT protocol.

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Pros of Apache NiFi
Pros of EMQX
  • 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
  • 3
    QoS 2
  • 2
    Clusters
  • 1
    Plugins

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Cons of Apache NiFi
Cons of EMQX
  • 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 EMQX?

    EMQX is a cloud-native, MQTT-based, IoT messaging platform designed for high reliability and massive scale. Licensed under the Apache Version 2.0, EMQX is 100% compliant with MQTT 5.0 and 3.x standard protocol specifications.

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    What companies use Apache NiFi?
    What companies use EMQX?
    See which teams inside your own company are using Apache NiFi or EMQX.
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    What tools integrate with Apache NiFi?
    What tools integrate with EMQX?

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    What are some alternatives to Apache NiFi and EMQX?
    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