We performed a comparison between Amazon Virtual Private Cloud and Apache Spark 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."It is an user-friendly solution."
"The solution's subnetting feature is good and has impacted our network design."
"The product's initial setup phase is simple since my company manages it with the use of Terraform."
"With an AWS virtual private cloud, you're in charge of what you use. It's pay-as-you-go."
"You can get a direct link to AWS to your data even if you are a large organization with a huge data center."
"The main feature I like about Amazon VPC is its security capabilities, including security groups and subnets."
"Stability-wise, I rate the solution a ten out of ten."
"Amazon Virtual Private Cloud isolates networks and offers robust network security. It also adds two network security layers."
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"This solution provides a clear and convenient syntax for our analytical tasks."
"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."
"The solution is very stable."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"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."
"Spark can handle small to huge data and is suitable for any size of company."
"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."
"The tool needs to improve its stability and support which should be faster. The product's pricing is also expensive. When we scale up, we have to pay more."
"There are some differences in the route tables between public and private subnets, which is something that is not properly documented."
"Increasing the subnet count could be an improvement."
"VPC itself is pretty good, but understanding it well is key. One of the challenges for beginners is understanding IP address ranges and subnet concepts."
"One concern is the cost, which can be relatively high compared to other cloud providers like Azure and Google Cloud."
"The overall integration capabilities of Amazon Virtual Private Cloud with third-party tools need to improve."
"The tool is not scalable."
"I recently worked on Transit Gateway, which connects multiple VPCs in one account and enables communication between them. However, I found the documentation unclear, possibly because few people encounter this situation. I figured it out and implemented it, but it required some research. Most people prefer using infrastructure as code rather than the UI for AWS tasks. However, the documentation may not always be up to date."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."
"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."
"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."
"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."
"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."
Amazon Virtual Private Cloud is ranked 7th in Compute Service with 30 reviews while Apache Spark is ranked 5th in Compute Service with 60 reviews. Amazon Virtual Private Cloud is rated 9.0, while Apache Spark is rated 8.4. The top reviewer of Amazon Virtual Private Cloud writes "Easy-to-use product with good access control features". On the other hand, the top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". Amazon Virtual Private Cloud is most compared with , whereas Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Cloudera Distribution for Hadoop. See our Amazon Virtual Private Cloud vs. Apache Spark report.
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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.
