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.
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We can query the data source and treat multiple databases
as one with IBM Netezza Performance Server. While delivering fast and reliable analytical performance, the IBM Netezza Performance Server requires minimal configuration and ongoing management. To drive organizational performance, Netezza Performance Server automatically simplifies data and AI to centralize all analytics activities on the device, exactly where the data resides. For data processing and application dashboards, IBM Netezza Performance Server is quite beneficial. IBM Netezza Performance Server simplifies event setup by notifying you when a hardware component fails, allowing you to quickly replace it. 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 IBM Netezza Performance Server enables capabilities to integrate with leading ETL solutions. Data can be exported from the IBM Netezza Performance Server to a variety of formats, including Excel. We can prioritize specific users and queries using IBM Netezza Performance Server. 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 More tech support may make it easier as it is only limited to be done via company representatives Overhead cost for replacements and services looks like it is high compared to others Integration with other cloud based application will be great Read full review Likelihood to Renew
Capacity of computing data in cluster and fast speed.
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.
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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.
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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
. Combining it with Jupyter Notebooks (
), one can develop the Spark code in an interactive manner in Scala or Python
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Netezza is sufficient against similar products. It comes down to personal preference, I'd love to have the data objects popping up as I type but some people may not like it.
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 The appliance is all in one so we have all licenses and support contracts under one roof. The cost is expensive so ROI may take time to be realized. If done correctly, then Netezza provides the opportunity for huge ROI. Read full review ScreenShots