Amazon Web Services vs. Apache Spark

We recommend AWS Lightsail with Plesk Ubuntu Web Admin (free) edition for launching WordPress websites. Pricing starts with 3.5$ per month and they are providing 3 months free. In order to access advanced features of Plesk, consider upgrading to paid Plesk plans that starts with 13.50$ per month (10 websites supported). Up to 40% discount is available for the first year. Anyway, even the free Plesk Web Admin version is by itself self sufficient with free SSL.

Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.

Incentivized

• Starting an instance and accessing it for testing purpose, demo or production deployment its always easy.
• All the things which are available over AWS are pretty well managed and easy to use.
• You might find everything you required for an product and other development over AWS.
• Its suitable for both either an enterprise or an startup
• Various resources and documentation are available in case you struck somewhere.

Incentivized

• Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner.
• Apache Spark does a fairly good job implementing machine learning models for larger data sets.
• Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use.

Incentivized

• If there is one thing I think AWS needs improvement on, it is the administration dashboard. It can be a nightmare to use especially when trying to access billing. This could be made better, honestly, as there should be a simplified way to access simple admin features.
• While AWS was fairly easy to integrate into our solutions, it is not as easy to use without some IT knowledge. The dashboards are complicated and designed for someone who is computer savvy. If you are just want to keep track of billing, for example, you may need to take a course or spend a few hours with someone being walked through the admin console.
• AWS does tend to be slow at times. If you do not have a fast internet connection, it can take time to access services that are hosted on AWS. This is not always the case but we have had clients complain about this if they are trying to access a service from multiple points (IP addresses). The only real fix we found was to make our files cache to another server and only keep current data accessible to clients.

Incentivized

• Memory management. Very weak on that.
• PySpark not as robust as scala with spark.
• spark master HA is needed. Not as HA as it should be.
• Locality should not be a necessity, but does help improvement. But would prefer no locality

Incentivized

We are almost entirely satisfied with the service. In order to move off it, we'd have to build for ourselves many of the services that AWS provides and the cost would be prohibitive. Although there are cost savings and security benefits to returning to the colo facility, we could never afford to do it, and we'd hate to give up the innovation and constant cycle of new features that AWS gives us.

Capacity of computing data in cluster and fast speed.

Our cloud platform architecture was designed in order to collect, analyze, and optimize modern networks, from AWS-powered computing, networking, storage, and more. So, we needed a reliable, scalable, and secure global computing infrastructure. Auto Scaling and Elastic Load Balancing were key features in our evaluation and later on for scalability and high performance. We being a cybersecurity company, we needed to ensure that our cloud provider utilizes an end-to-end approach to secure and harden the infrastructure, including physical, operational, and software measures - which AWS had all in place.

Incentivized

The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.

Incentivized

Availability is very good, with the exception of occasional spectacular outages.

AWS does not provide the raw performance that you can get by building your own custom infrastructure. However, it is often the case that the benefits of specialized, high-performance hardware do not necessarily outweigh the significant extra cost and risk. Performance as perceived by the user is very different from raw throughput.

The customer support of Amazon Web Services are quick in their responses. I appreciate its entire team, which works amazingly, and provides professional support. AWS is a great tool, indeed, to provide customers a suitable way to
immediately search for their compatible software's and also to guide them in a
good direction. Moreover, this product is a good suggestion for every type of
company because of its affordability and ease of use.

Incentivized

1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.

The API's were very well documented and was Janova's main point of entry into the services.

Amazon Web Services is well suited when we have a huge amount of data to store, process, manipulate and get meaningful information out of. It is also suitable when we need very fast data retrieval from the database. They provide a superior product at a fair price which allows us to further our goals and push the limits of what we are capable of as a team / company.

Incentivized

All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python

Incentivized

• AWS has lowered our employee cost, because you don't have to hire Network/Server Admins to manage infrastructure.
• Increased productivity by incorporating Continuous Integration with AWS and our development life cycle.
• Increased customer confidence by being able to provide HIPAA level security in our development and production environments

Incentivized

• Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
• Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
• Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.

Incentivized