We performed a comparison between Apache Spark and AWS Fargate based on real PeerSpot user reviews.
Find out in this report how the two Compute Service solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"The main feature that we find valuable is that it is very fast."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."
"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"The most valuable feature of Fargate is that it's self-managed. You don't have to configure your own clusters or deploy any Kubernetes clusters. This simplifies the initial deployment and scaling process."
"The most valuable feature of AWS Fargate is its ease of use."
"If you create your deployment with a good set of rules for how to scale in, you can just set it and forget it."
"I like their containerization service. You can use Docker or something similar and deploy quickly without the know-how related to, for example, Kubernetes. If you use AKS or Kubernetes, then you have to have the know-how. But for Fargate, you don't need to have the know-how there. You just deploy the container or the image, and then you have the container, and you can use it as AWS takes care of the rest. This makes it easier for those getting started or if you don't have a strong DevOps team inside your organization."
"We appreciate the simple use of containers within this solution, it makes managing the containers quick and easy."
"Fargate itself is a stable product. We are quite satisfied with its performance."
"AWS Fargate has many valuable services. It does the job with minimal trouble. It's very observable. You can see what's going on and you have logs. You have everything. You can troubleshoot it. It's affordable and it's flexible."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"It requires overcoming a significant learning curve due to its robust and feature-rich nature."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"Apache Spark provides very good performance The tuning phase is still tricky."
"The initial setup was not easy."
"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."
"The solution must improve its performance."
"When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources."
"I heard from my team that it's not easy to predict the cost. That is the only issue we have with AWS Fargate, but I think that's acceptable. AWS Fargate isn't user-friendly. Anything related to Software as a Service or microservice architecture is not easy to implement. You're required to have DevOps from your side to implement the solution. AWS Fargate is just a temporary solution for us. When we grow to a certain level, we may use AKS for better control."
"AWS Fargate could improve the privileged mode containers. We had some problems and they were not able to run."
"We faced challenges in vertically scaling our workload."
"I would like to see the older dashboard instead of the newer version. I don't like the new dashboard."
"The main area for improvement is the cost, which could be lowered to be more competitive with other major cloud providers."
"If there are any options to manage containers, that would be good. That relates more to the cost point. For example, over the next three months, I'll be making a comparison between solutions like CAST AI and other software-as-a-service platforms that offer Kubernetes management with an emphasis on cost reduction."
"We would like to see some improvement in the process documents that are provided with this product, particularly for auto-scaling and other configuration tools that are a bit complicated."
Apache Spark is ranked 5th in Compute Service with 60 reviews while AWS Fargate is ranked 6th in Compute Service with 7 reviews. Apache Spark is rated 8.4, while AWS Fargate is rated 8.8. The top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". On the other hand, the top reviewer of AWS Fargate writes "Efficiently auto-scales and good performance". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Amazon EC2, whereas AWS Fargate is most compared with Amazon EC2 Auto Scaling, Amazon EC2, AWS Lambda, AWS Batch and Apache NiFi. See our AWS Fargate vs. Apache Spark report.
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