Although there are new technologies pouring in on a daily basis it is not sure to know the right applications. Here are two technologies we are about to discuss Apache Spark and Apache Nifi. A cluster computing open source framework is none other than Apache Spark and its motto is to offer an interface for programming entire set of clusters with fault tolerance and data parallelism.
There is another software project called Apache Nifi and its aim is to automate the flow of data among software systems. The flow-based programming model is the basis for its design which has operations with the cluster’s ability. It is quite easy to rely on and use a powerful system for distributing data and process.
The differences between Apache Nifi and Apache Spark are mentioned below:
- A data ingestion tool called Apache Nifi is used for delivering a simple to use, reliable and powerful system so that distribution and processing of data among resources becomes easier and moreover ApacheSpark is quite a fast cluster computing technology which is created for rapid computation by quickly making the use of queries which are interactive in-stream processing capabilities and memory management.
- In a standalone mode and a cluster mode, Apache Nifi works whereas Apache Spark works well in the standalone mode, Yarn and other kinds of big data cluster modes. Guaranteed delivery of data is present in the features of Apache Nifi with proper data buffering, prioritized queuing, Data Provenance, Visual Command and Control, Security, Parallel streaming capabilities along with features of apache spark with fast speed processing capabilities.
- A better readability and a complete understanding of the system offers visualization capabilities and the features are dragged and dropped by Apache Nifi. It is possible to easily govern and manage the conventional processes and techniques and in case of Apache Spark, these kinds of visualizations are viewed in a management system cluster like Ambari.
- The Apache Nifi is linked with the restriction to its benefit. A restriction is offered by the drag and drop feature of not being scalable and offers robustness when combining with various components and tools with Apache Spark along with the commodity hardware which is extensive and becomes a difficult task at times.
- There is other reported limitation along with the streaming capabilities linked with Discretized Stream and batch or Windowed Stream and the data sets offer a lead for instability at times.
Use Cases of both include;
- Apache Nifi: Data flow management along with visual control data size Arbitrary data routing among disparate systems
- Apache Spark: Streaming Data Machine, learning Interactive Analysis, Fog Computing
You can finish the post by saying that Apache Spark is quite a tough war horse and moreover Apache Nifi is a weak horse. Each of them has their own advantages and disadvantages to being used in their respective areas. Just consider the right tool for your business.
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Reference site: educba