Difference Between Apache Nifi and Apache Spark
Apache Nifi (which is the short form of NiagaraFiles) is another software project which aims to automate the data flow between software systems. The design is based upon a flow-based programming model that provides features that include operating with clusters ability. It is easy to use, reliable and a powerful system to process and distribute data. It supports scalable directed graphs for data routing, system mediation, and transformation logic. Apache Spark is a cluster computing open-source framework that aims to provide an interface for programming entire set of clusters with implicit fault tolerance and data parallelism. It makes use of RDDs (Resilient Distributed Datasets) and processes the data in the form of Discretized Streams which is further utilized for analytical purposes.
Head to head comparison between Apache Nifi and Apache Spark (Infographics)
Below is the top 9 Comparision Between Apache Nifi vs Apache Spark
Key differences between Apache Nifi and Apache Spark
The differences between Apache Nifi and Apache Spark are explained in the points presented below:
- Apache Nifi is a data ingestion tool which is used to deliver an easy to use, powerful and a reliable system so that processing and distribution of data over resources becomes easy whereas Apache Spark is an extremely fast cluster computing technology which is designed for quicker computation by efficiently making use of interactive queries, in memory management and stream processing capabilities.
- Apache Nifi works in standalone mode and a cluster mode whereas Apache Spark works well in local or the standalone mode, Mesos, Yarn and other kinds of big data cluster modes.
- Features of Apache Nifi includes guaranteed delivery of data, efficient data buffering, Prioritized queuing, Flow Specific QoS, Data Provenance, Roll buffer recovery, Visual command, and control, Flow templates, Security, Parallel Streaming capabilities whereas features of apache spark includes Lightning fast speed processing capability, Multilingual, In-memory computing, efficient utilization of commodity hardware systems, Advanced Analytics, Efficient integration capability.
- Apache Nifi allows better readability and overall understanding of the system by providing visualization capabilities and drag and drop features. The data flow can be easily managed and governed using conventional techniques and processes whereas in the case of Apache Spark in order to view these kinds of visualizations a cluster management system like Ambari is needed. Apache Spark in itself does not provide visualization capabilities and is only good as far as programming is concerned. It is by far a very convenient and stable system for processing huge amounts of data.
- The limitation with Apache Nifi is related to what is its advantage. The only drag and drop feature provides a limitation of not being able to scale and provide robustness when it comes to integrating it with other components and tools whereas in case of Apache Spark the primary limitation comes along with the use of extensive commodity hardware and managing them becomes a tedious task at times. The other reported limitation comes along with its streaming capabilities related to Discretized Stream and Windowed or batch stream where the transformation of RDDs to Data frame and Data Sets provides a cause for instability at times.
Apache Nifi vs Apache Spark Comparision Table
|Basis of comparison||Apache Nifi||Apache Spark|
|What is provided||It provides a graphical user interface like a format for system configuration and monitoring data flows.||Large-scale data processing framework is provided with approximately zero latency at the cost of cheap commodity hardware.|
|Deployment Issues||If the most recent version of Java was not used, configuration and compatibility issues are seen||A well-defined cluster arrangement is required to have a managed environment as an incorrect configuration|
|Scalability and Stability issues||Generally, no issues are reported related to scalability and stability||Achieving stability is difficult as a spark is always dependent upon the streamflow.|
|Benefits provided||It allows a great visualization of data flows to organizations and thereby increasing the understandability of the entire system process end to end||A very convenient and stable framework when it comes to big data. The efficiency is automatically increased when the tasks related to batch and stream processing is executed.|
|Earlier solutions used||Apache Flume could be well used as far as data ingestion is concerned. The only drawback with Flume is lack of graphical visualizations and end to end system processing||Other solutions considered previously were Pig, Hive, and Storm. Using Apache Spark provides the flexibility of utilizing all the features in one tool itself.|
|Limitations||Majorly the limitation is related to provenance indexing rate which becomes the bottleneck when it comes to overall processing of huge data||Limitation for Spark comes in terms of Stability in terms of API as transitioning from RDDs to Data Frames to Data Sets often becomes a complicated task.|
To conclude the post, it can be said that Apache Spark is a heavy warhorse whereas Apache Nifi is a nimble racehorse. Both have their own benefits and limitations to be used in their respective areas. You need to decide the right tool for your business. Stay tuned to our blog for more articles related to newer technologies of big data.
This has been a guide to Apache Nifi vs Apache Spark. Here we discuss Head to head comparison, key differences, comparison table with infographics. You may also look at the following articles to learn more –
- 7 Important Things About Apache Spark (Guide)
- Apache HBase
- What is Apache Spark?
- Apache Spark Architecture