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 Sales data validations have helped manage our justifications in the past, especially with regard to new product development and new business introduction. It has also been helpful in identifying trends with business impact and direction specific to quarter and monthly sales from ERP data as well as decisions to purchase equipment of staffing based on run rates and product demand.
One thing that can get out of hand is data output - if you aren't careful in your query, you may be overloaded with data dumps and drown in the amount of info you have to filter through. This is a user caution, not a comment on the software itself.
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 QlikView has a simple, relational data model that's REALLY fast. Filtering and changing data is dead simple results are almost immediately available. The free version of Qlikview is almost completely featured, so you roll a pro-level product out to an entire department for really cheap. QlikView is really flexible--if you can imagine it, you can build it. 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 We found that QlikView can be a bit slow in supporting some forms of encryption. It is web-based and we needed to upgrade all of our server to not support the older SSL and TLS 1 protocols, only support TLS 1.2 and TLS 1.3. However, QlikView could not run with TLS 1.2 and TLS 1.3. We had to wait over six months to get a version that would handle the newer TLS versions. There are so many options with QlikView that you can get lost when developing a visualization. There are still items I have not yet figured out, such as labeling a graph with the name of a selected detail item. QlikView works by pulling the data it is going to use for visualization into its database. I am a security reviewer and I need to make certain that PII and PHI is not pulled by QlikView for a visualization, otherwise this could become a reportable indecent. Read full review Likelihood to Renew Capacity of computing data in cluster and fast speed.
Steven Li Senior Software Developer (Consultant)
Read full review Ease of use, ability to load from pretty much any data source. today I created an application that loaded time sheets from excel that are not in a table format. With Qlik's "enable transformation steps" I was able to automate loads of multiple spreadsheets and multiple tabs easily. Could not do that with any other tool.
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 QlikView is very easy to implement. The installation is very straight forward. QlikView has several different data connectors that can connect to different data sources very smoothly. The user interface to build the reports is very easy to understand. This helps to have a smaller learning curve. Something very helpful is that QlikView is a browser application for the end users. So, you don't need to install any applications on the user's computer.
Read full review Reliability and Availability We have not had any downtime issues with the product nor uncovered any significant bugs
Read full review Performance It is not a SAAS product.
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 My experience with the Qlik support team has been somewhat limited, but every interaction I have had with them has been very professional and I received a response quickly. Typically if there is a technical issue, our IT team will follow up. My inquiries are specific to product functionality, and Qlik has been very helpful in clarifying any questions I might have.
Read full review In-Person Training My team attended, but I cannot myself rate, but I think it was good as they've successfully launched a training program at our company themselves for users. It was 3-4 day training.
Read full review Online Training Training was as expected. The demo environments tend to be more fully featured that our own environment, but the training was clear and well delivered.
Read full review Implementation Rating "Implementation" can mean a few things... so I'm not sure that this is the answer you want.... but here it goes: To me, implementation means: "Is the user interface intuitive and can I produce meaningful reports with ease?" On that score, I'd say YES. The amount of training required was minimal and the results were powerful. The desktop implementation is a simple, "blank" interface just waiting for your creativity. The pre-populated templates give you a reasonable start to any project -- and a good set of objects to "play around with" if you're just getting started. Finally, note that the "implementation" I used was baked into QuickBooks 2016 Enterprise -- called "Advanced Reporting"..... That integration makes it ultra useful and simple.
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 The only other vendor product that I have worked with that provides a similar experience to Qlikview is
Tableau . I would recommend
Tableau if your use case is to build a fixed dashboard. You can share reports for free without needing to buy additional licenses. I would recommend Qlikview if your users are looking for a more interactive experience. They can create new objects to represent the data which can't be accomplished as easily in
Tableau 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 Generate quick reports for requirements that don't require complex calculations. ROI was fine, but Tableau software was much more intuitive for non-technical users on our team When putting Qlikview reports side by side with Tableau, we ended up delivering Tableau reports since they were quicker to generate and required no technical expertise Read full review ScreenShots