Likelihood to Recommend 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.
Read full review [NGINX] is very well suited for high performance. I have seen it used on servers with 1k current connections with no issues. Despite seeing it used in many environments I've never seen software developers use it over apache, express, IIS in local dev environments so it may be more difficult to setup. I've also seen it used to load balance again without issues.
Read full review Pros 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. Read full review Very low memory usage. Can handle many more connections than alternatives (like Apache HTTPD) due to low overhead. (event-based architecture). Great at serving static content. Scales very well. Easy to host multiple Nginx servers to promote high availability. Open-Source (no cost)! Read full review Cons 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 Read full review Customer support can be strangely condescending, perhaps it's a language issue? I find it a little weird how the release versions used for Nginx+ aren't the same as for open source version. It can be very confusing to determine the cross-compatibility of modules, etc., because of this. It seems like some (most?) modules on their own site are ancient and no longer supported, so their documentation in this area needs work. It's difficult to navigate between nginx.com commercial site and customer support. They need to be integrated together. I'd love to see more work done on nginx+ monitoring without requiring logging every request. I understand that many statistics can only be derived from logs, but plenty should work without that. Logging is not an option in many environments. Read full review Likelihood to Renew Capacity of computing data in cluster and fast speed.
Steven Li Senior Software Developer (Consultant)
Read full review Great value for the product
Read full review Usability 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.
Read full review Front end proxy and reverse proxy of Nginx is always useful. I always prefer to Nginx in overall usability when you have application server and database or multiple application servers and single database i.e. clustered application . Nginx provides really good features and flexibility which helps the system administrator in case of troubleshooting and also from the administration perspective . Also, Nginx doesn't delay any request because of internal performance issues.
Read full review Support Rating 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.
Read full review John Reeve Principal, Lead developer, Lead designer
Read full review Alternatives Considered 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
Read full review We have used Traffic, Apache, Google Cloud Load Balancing and other managed cloud-based load balancers. When it comes to scale and customization nothing beats Nginx. We selected Nginx over the others because
we have a large number of services and we can manage a single Nginx instance for all of them we have high impact services and Nginx never breaks a sweat under load individual services have special considerations and Nginx lets us configure each one uniquely Read full review Return on Investment 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. Read full review Nginx has decreased the burden of web server administration and maintenance, and we are spending less time on server issues than when we were using Apache. Nginx has allowed more people in our company to get involved with configuring things on the web server, so there's no longer a single point of failure ("the Apache guy"). Nginx has given us the ability to handle a larger number of requests without scaling up in hardware quite so quickly. Read full review ScreenShots