Apache Spark and AWS Fargate compete in large-scale data processing and cloud management arenas. While both offer compelling features, Apache Spark has the edge in handling big data through its in-memory processing capabilities, whereas AWS Fargate streamlines container management without infrastructure hassles.
Features: Apache Spark offers large-scale data processing with tools such as Spark Streaming for real-time event-driven applications, Spark SQL for low-cost data analysis, and MLlib for machine learning. Its strengths lie in fast performance, scalability, and extensive AI connectors. AWS Fargate simplifies container management by eliminating infrastructure management, providing easy integration with AWS services, and offering a user-friendly pay-as-you-go model.
Room for Improvement: Apache Spark users seek better scalability in real-time workflows, improved documentation, enhanced integration with BI tools, and more robust memory management. AWS Fargate could improve by simplifying dynamic scaling configurations, enhancing cost management features, and providing comprehensive setup documentation.
Ease of Deployment and Customer Service: Apache Spark can be deployed on-premises, in hybrid, or cloud environments, backed by community and optional paid support through Connectors like Cloudera. Customer service largely depends on community engagement. AWS Fargate is a cloud-native solution focusing on ease of use, supported by AWS's robust customer service, which offers direct technical assistance often unavailable in open-source platforms.
Pricing and ROI: Apache Spark is open-source with free use unless paired with products like Cloudera, potentially incurring infrastructure and operational costs. Its long-term efficiencies can translate to significant savings. AWS Fargate uses a pay-as-you-go model, often more costly than some AWS services, but justifies the expense by reducing the complexity of deployment and application management, aligning with varying scaling needs.
The pay-as-you-go pricing model of AWS Fargate was one of the major drivers for us to move there because we reduced costs while increasing the quality of the processing services by about 30%.
Even though we didn't contract support, every two weeks I had a 30-minute meeting with a cloud architect from AWS to help our team use different products of AWS, especially with SageMaker for a forecasting algorithm we were developing.
For a company that does not require complexity or managing Kubernetes clusters, AWS Fargate is a great way to go.
One of the best features of AWS Fargate is that it was useful for us because we didn't require to run container workloads and we didn't need to deal with the management of a Kubernetes cluster directly, and the ability to run those workloads just in a scheduled manner is also a great feature.
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.
We monitor all Compute Service reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.