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"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
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Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory
A new compute engine that enables you to use containers as a fundamental compute primitive without having to manage the underlying instances. With Fargate, you don’t need to provision, configure, or scale virtual machines in your clusters to run containers. Fargate can be used with Amazon ECS today, with plans to support Amazon Elastic Container Service for Kubernetes (Amazon EKS) in the future.
Fargate has flexible configuration options so you can closely match your application needs and granular, per-second billing.
Apache Spark is ranked 1st in Compute Service with 13 reviews while AWS Fargate is ranked 8th in Compute Service. Apache Spark is rated 8.2, while AWS Fargate is rated 0.0. The top reviewer of Apache Spark writes "Good Streaming features enable to enter data and analysis within Spark Stream". On the other hand, Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Batch, SAP HANA and IBM Streams, whereas AWS Fargate is most compared with AWS Batch, Amazon Elastic Inference, Amazon EC2 Auto Scaling, Apache NiFi and Oracle Compute Cloud Service.
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