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Apache NiFi

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Apache NiFi vs Logstash: What are the differences?

Introduction

Apache NiFi and Logstash are two popular data processing tools used for ingesting, transforming, and routing data in real-time. Although they serve similar purposes, there are key differences between the two.

  1. Data Processing Approach: Apache NiFi and Logstash employ different approaches to data processing. Apache NiFi utilizes a flow-based programming model, where data is routed through interconnected processors and processors perform specific actions on the data. Logstash, on the other hand, follows a pipeline-based approach, where data flows through a series of stages and each stage applies a specific filter or action on the data.

  2. Flexibility and Extensibility: Apache NiFi provides a more flexible and extensible framework for data processing. It offers a wide range of processors with various functionalities and allows users to create custom processors to meet specific requirements. Logstash, although extensible, has a more limited set of built-in plugins and its extensibility mainly relies on community-contributed plugins.

  3. Ease of Use: Apache NiFi focuses on simplicity and ease of use with its user-friendly graphical interface. It provides a drag-and-drop visual interface for designing and monitoring data flows, making it easy for users to create and maintain data pipelines. Logstash, while also supporting a graphical interface called Kibana, primarily relies on configuration files, which may require more technical expertise to set up and manage.

  4. Integration with Ecosystem: Apache NiFi is tightly integrated with the Apache Hadoop ecosystem, allowing seamless integration with Big Data technologies and tools such as Hadoop, Hive, HBase, and more. It can leverage the full power of the Hadoop ecosystem for data processing and storage. Logstash, on the other hand, is part of the Elasticsearch ecosystem and is commonly used for log analysis and ingesting data into Elasticsearch for indexing and search.

  5. Scalability: Apache NiFi provides built-in scalability features, such as the ability to deploy multiple instances in a cluster and distribute data processing across the cluster. It can handle high volumes of data and scale horizontally to meet increased demand. Logstash can also be scaled using multiple instances, but it requires external tools like Elasticsearch and RabbitMQ to achieve distributed processing and scalability.

  6. Community and Support: Apache NiFi has a vibrant and active community with regular updates, documentation, and support available from the Apache Software Foundation. It also has a large user base, contributing to its ecosystem of processors and extensions. Logstash, being an open-source project by Elastic (previously Elasticsearch), also benefits from a strong community and support, with regular updates and a vast range of community-contributed plugins available.

In summary, Apache NiFi and Logstash differ in their data processing approach, flexibility, ease of use, integration with ecosystems, scalability, and community support. Understanding these differences can help you choose the right tool for your specific data processing needs.

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Pros of Apache NiFi
Pros of Logstash
  • 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
  • 69
    Free
  • 18
    Easy but powerful filtering
  • 12
    Scalable
  • 2
    Kibana provides machine learning based analytics to log
  • 1
    Great to meet GDPR goals
  • 1
    Well Documented

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Cons of Apache NiFi
Cons of Logstash
  • 2
    HA support is not full fledge
  • 2
    Memory-intensive
  • 1
    Kkk
  • 4
    Memory-intensive
  • 1
    Documentation difficult to use

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

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What are some alternatives to Apache NiFi and Logstash?
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.
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.
Airflow
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
See all alternatives