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Apache NiFi

313
641
+ 1
63
Kafka Streams

352
434
+ 1
0
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Apache NiFi vs Kafka Streams: What are the differences?

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; Kafka Streams: A client library for building applications and microservices. 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.

Apache NiFi and Kafka Streams can be primarily classified as "Stream Processing" tools.

According to the StackShare community, Apache NiFi has a broader approval, being mentioned in 10 company stacks & 12 developers stacks; compared to Kafka Streams, which is listed in 7 company stacks and 5 developer stacks.

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Pros of Apache NiFi
Pros of Kafka Streams
  • 16
    Visual Data Flows using Directed Acyclic Graphs (DAGs)
  • 8
    Free (Open Source)
  • 7
    Simple-to-use
  • 5
    Reactive with back-pressure
  • 5
    Scalable horizontally as well as vertically
  • 4
    Fast prototyping
  • 3
    Bi-directional channels
  • 2
    Data provenance
  • 2
    Built-in graphical user interface
  • 2
    End-to-end security between all nodes
  • 2
    Can handle messages up to gigabytes in size
  • 1
    Hbase support
  • 1
    Kudu support
  • 1
    Hive support
  • 1
    Slack integration
  • 1
    Support for custom Processor in Java
  • 1
    Lot of articles
  • 1
    Lots of documentation
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    Cons of Apache NiFi
    Cons of Kafka Streams
    • 2
      HA support is not full fledge
    • 2
      Memory-intensive
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      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 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.

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

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

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      Blog Posts

      Jun 24 2020 at 4:42PM

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      What are some alternatives to Apache NiFi and Kafka Streams?
      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.
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      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.
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      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.
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