Apache NiFi vs Apache Spark in a Nutshell Apache Spark is the most sought-after cluster computing application that is being used today by all organizations small and large, to derive useful insights from their data. Apache Spark provides a way to program entire sets of clusters with implicit fault tolerance and ability to execute jobs in parallel. Apache Nifi, on the other hand, is a software that provides an alternative automate the flow of data between software solutions. Apache Nifi is an easy to use, powerful system that enables its users to process and distribute data efficiently. There have been many speculations between the two platforms on different websites, and Big Data influencers tend to appreciate both platforms for their immense applications and use cases throughout industries. Here are some common differentiating factors that make Apache Spark and Apache Nifi stand against each other in clear light
Provisions – Apache Nifi provides a GUI format for systems' data flows to be configured and monitored effectively. Whereas, Apache Spark aids in large-scale data processing in nearly real-time scenarios at the cost of inexpensive commodity hardware.
Salient utilities – While Apache Nifi is a web-based interface, is highly configurable, secure, and does not allow data replications, Apache Spark is multilingual, provides applications in advance analytics and real-time data processing.
Data pulling Vs. Data pushing – Nifi specifically refers to transferring data from site to site or between clusters. Nifi gives options for pushing and pulling data so that the most appropriate option is picked according to the requirements. On the contrary, Apache Spark does not provide an option to push data into it. Instead, it allows for data to be pulled from other sources.
Applications – Based on their use cases and applications, Apache Nifi and Apache Spark can be effectively differentiated. While Apache Spark is used for streaming data, Fog computing, Interactive data analytics, and Machine Learning applications, Apache Nifi is used for data flow management, data routing applications, etc.
Stability in applications – With Apache Nifi, there are almost no stability issues, but with Apache Spark, stability only depends on the incoming streams, which can be higher at some times and lower at others. Therefore, stability cannot be guaranteed in case of Apache Spark.
Predecessors – The predecessors to Apache Spark were Pig, Hive, and Storm. Apache Spark provides the flexibility to use all these tools in a single application. One of the significant predecessors to Apache Nifi was Apache Flume, which suffered only one disadvantage- lack of graphical visualizations and end-to-end processing of data.
Overall use of the platform – According to the ease of use of the platform, Apache Nifi enables a better understanding of the overall system through its visualization capabilities. The drag and drop features on the platform make it easily understandable and usable. In Apache Spark, on the other hand, cluster management systems like Ambari are required when there is a need for such visualization.
In conclusion, it can be said that Apache Spark implementation is a robust platform only built for the real coders and programmers who can work without the availability of easy features to derive real-time analytics from fast-moving data. Apache Nifi, on the contrary, is for other applications like data movement and provides for easy and simplistic approaches.
Source: Apache NiFi vs Apache Spark in a Nutshell