We performed a comparison between Amazon EC2, Apache Spark, and Azure Stream Analytics based on real PeerSpot user reviews.
Find out what your peers are saying about Amazon Web Services (AWS), Apache, Zadara and others in Compute Service."The most valuable features of Amazon EC2 are content delivery and adaptability."
"The product is easy to set up."
"Amazon EC2 is highly scalable."
"The most valuable features are the scalability options, low maintenance, and options to upgrade. AWS support is also pretty good. The generation upgrade is pretty simple and standardized."
"The amount of bandwidth has been most valuable."
"What I found most valuable in Amazon EC2 is that you only pay for what you use, versus an on-premise deployment that requires you to pay for the cost of the server. When it's on-premise, you'll need to meet more specifications and requirements, and the purchasing process even takes time. As Amazon EC2 is cloud-based, you'll only pay when you use the service."
"We find it easy to scale."
"Its ease of use is valuable."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"It provides a scalable machine learning library."
"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."
"We use it for ETL purposes as well as for implementing the full transformation pipelines."
"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."
"It is useful for handling large amounts of data. It is very useful for scientific purposes."
"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."
"We use Azure Stream Analytics for simulation and internal activities."
"The life cycle, report management and crash management features are great."
"Real-time analytics is the most valuable feature of this solution. I can send the collected data to Power BI in real time."
"Provides deep integration with other Azure resources."
"The solution's most valuable feature is its ability to create a query using SQ."
"The solution's technical support is good."
"I appreciate this solution because it leverages open-source technologies. It allows us to utilize the latest streaming solutions and it's easy to develop."
"The way it organizes data into tables and dashboards is very helpful."
"The availability and response time of the free technical support can be improved."
"The IP changes whenever we restart which is frustrating."
"Regarding availability, a noticeable improvement would be the possibility of more load balancing configurations and the deployment of more datacenters, mainly in Latin America."
"The initial setup could be easier because many keys are required for access."
"One of the challenges is the AMI upgrades."
"Its price can be reduced."
"I would like to see more variety in the operating system images used to create test environments in EC2. There should be more versions and releases. Sometimes, you want to test an update from an old release to a higher version, but you can’t do that with the new images available. You have to use your own."
"They have to provide clarity on pricing. It's not transparent."
"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"They could improve the issues related to programming language for the platform."
"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."
"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 product could improve the user interface and make it easier for new users."
"The solution needs to optimize shuffling between workers."
"Apache Spark provides very good performance The tuning phase is still tricky."
"We would like to have centralized platform altogether since we have different kind of options for data ingestion. Sometimes it gets difficult to manage different platforms."
"Easier scalability and more detailed job monitoring features would be helpful."
"It is not complex, but it requires some development skills. When the data is sent from Azure Stream Analytics to Power BI, I don't have the access to modify the data. I can't customize or edit the data or do some queries. All queries need to be done in the Azure Stream Analytics."
"If something goes wrong, it's very hard to investigate what caused it and why."
"Sometimes when we connect Power BI, there is a delay or it throws up some errors, so we're not sure."
"The solution doesn't handle large data packets very efficiently, which could be improved upon."
"The solution could be improved by providing better graphics and including support for UI and UX testing."
"The solution's interface could be simpler to understand for non-technical people."
