Apache NiFi vs Azure Data Factory

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Apache NiFi

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Azure Data Factory

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Advice on Apache NiFi and Azure Data Factory
Vamshi Krishna
Data Engineer at Tata Consultancy Services · | 4 upvotes · 228.3K views

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

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Pros of Apache NiFi
Pros of Azure Data Factory
  • 17
    Visual Data Flows using Directed Acyclic Graphs (DAGs)
  • 8
    Free (Open Source)
  • 7
  • 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 Azure Data Factory
    • 2
      HA support is not full fledge
    • 2
    • 1
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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 Azure Data Factory?

      It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.

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      What companies use Apache NiFi?
      What companies use Azure Data Factory?
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      What tools integrate with Apache NiFi?
      What tools integrate with Azure Data Factory?

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      What are some alternatives to Apache NiFi and Azure Data Factory?
      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 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