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Apache NiFi

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Apache NiFi vs StreamSets: What are the differences?

Introduction

Apache NiFi and StreamSets are both popular data integration tools used for collecting, transforming, and distributing data across various systems. While they have similar purposes, there are several key differences between them that distinguish their capabilities and features.

  1. Data Ingestion Approach: Apache NiFi follows a flow-based approach for data ingestion, where data is processed in a dataflow model with a visual interface. It provides a drag-and-drop interface to design, manage, and monitor data flows. On the other hand, StreamSets uses a pipeline-based approach, where data is processed in a pipeline model with a graphical representation of stages. It allows users to define pipelines using a graphical User Interface (UI) and provides a rich set of stages for data transformation.

  2. Ease of Use: NiFi focuses on providing a user-friendly interface for designing and managing dataflows. It offers a wide range of processors, connectors, and processors that can be easily added to the dataflow. StreamSets also provides an intuitive user interface but offers a slightly steeper learning curve compared to NiFi. It requires a deeper understanding of the stages and their configurations for designing efficient data pipelines.

  3. Scalability and Performance: Apache NiFi is designed to have a lightweight footprint and is highly scalable. It can handle large volumes of data with its distributed and parallel processing capabilities. StreamSets also supports scalable processing and can handle large data volumes efficiently. However, NiFi provides better performance for data ingestion and processing due to its optimized flow-based architecture.

  4. System Compatibility: NiFi is built on Java, making it compatible with any system that supports Java. It can easily integrate with numerous systems and technologies, including Hadoop, Kafka, and various databases. StreamSets, on the other hand, supports both Java and Python programming languages, providing flexibility in integration with different technologies.

  5. Community and Support: Apache NiFi has a large and active open-source community with extensive documentation, tutorials, and forums. It has been in development for a longer period and is backed by Apache Software Foundation. StreamSets also has an active community but is relatively newer compared to NiFi. It also offers comprehensive documentation, guides, and support but may have a slightly smaller user base.

  6. Use Case Focus: Apache NiFi is more suited for complex dataflow scenarios requiring advanced routing, content-based filtering, and complex data transformations. It provides robust data governance and provenance features, making it ideal for enterprise-scale data integration. StreamSets, on the other hand, focuses more on real-time streaming data and event-based processing. It offers a wide range of streaming connectors and real-time data delivery capabilities, making it a preferable choice for streaming use cases.

In summary, Apache NiFi and StreamSets differ in their data ingestion approach, ease of use, scalability, system compatibility, community support, and use case focus. These differences allow users to choose the tool that best fits their specific requirements.

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Pros of Apache NiFi
Pros of StreamSets
  • 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 StreamSets
    • 2
      HA support is not full fledge
    • 2
      Memory-intensive
    • 1
      Kkk
    • 2
      No user community
    • 1
      Crashes

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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 StreamSets?

    An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.

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

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    What are some alternatives to Apache NiFi and StreamSets?
    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