SAP Crystal is an analytics and reporting software solution for SMBs. SAP Crystal comprises Crystal Reports for pixel-perfect reporting, and SAP Crystal Server for automated distribution and self-service access to reports, dashboards and data exploration.
$295
per single user license
Pricing
Apache Spark
SAP Crystal
Editions & Modules
No answers on this topic
Upgrade Version to SAP Crystal Reports 2020
$295
per single user license
SAP Crystal Reports 2020 64-Bit
$495
per single user license
SAP Crystal Reports 2016 32-Bit
$495
per single user license
SAP Crystal Server 2020 1 NUL
$869
per user license + first year maintenance to be added
SAP Crystal Server 2020 5 CAL
$8,744
5 concurrent users + first year maintenance to be added
Offerings
Pricing Offerings
Apache Spark
SAP Crystal
Free Trial
No
Yes
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
Yes
Entry-level Setup Fee
No setup fee
Optional
Additional Details
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* SAP Crystal Reports 2020 Viewer allows you to view static data within a SAP Crystal Reports file.
* SAP Crystal Reports 2020 - Full version allows you to create powerful, richly formatted, and dynamic reports from virtually any data source, delivered in dozens of formats, in up to 28 languages. If you already have an older version of Crystal Reports, buy a license for SAP Crystal Reports 2020 - Upgrade at a discounted, upgrade price.
* SAP Crystal Server 2020, 1 NUL includes 1 license of: SAP Crystal Reports 2020 - SAP Crystal Server 2020.
* As an add-on to SAP Crystal Server 2020 1 NUL, SAP Crystal Server 2020 5 CAL allows you to support a wider, intermittent audience of end users, up to five logged on at the same time.
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.
Generation of templated reports is the strong suit of SAP Crystal. Allows users to change formats in templates bases on requirement with minimal effort. Automated report delivery requires the user to be aware of sql which cannot be expected from all users. Should support more document export formats and improve the UI for SAP B1 Users
Crystal Reports allows us to create a consistent template for all of our reports.
Crystal Reports and Server allows us to house a repository for all of our reports to make them easy to find and update when necessary.
Crystal Reports can connect to a wide variety of data sources.
Crystal Reports can be a little daunting when first implementing. There are a lot of nuances in learning how to truly master this software and it can be frustrating at times.
We have been using this product for so many years and it has truly become a cornerstone to our business processes when it comes to developing and distributing information via reports. We currently have over 500 reports developed to date over about 30 systems and that will continue to grow as user needs change.
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.
Crystal is very robust, but not always easy to use. It create wonderful looking reports, and so deserves a high rating. However, I have to take a couple of points off for the simple fact that I cannot hand it to a user and expect them to be able to do development with it.
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 support community can be difficult to navigate. I've also run into issues with my login. The SAP system has a bizarre mechanism for validating users that requires users to have what is called an S-ID. A basic ID may not give you access to all the features in the portal. The limitation may include not being able to perform a simple task like downloading patches and updates. This isn't a big deal for single user license but for teams it can be a pain.
Just like any other implementation: When designing the differing reports, get end users' input, make sure to design the reports so that they display the information that the company requires, in the best and clearest way possible.
Test, test, test, revise when needed, and, particularly, do sufficient training so users are comfortable using Crystal Reports!
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
Crystal reports is useful in case we want to import data from data base . We can write queries in it but Google Charts require to be implemented in our application using code so crystal reports is better than Google Charts.
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.
It is a decent buy for specific departments in terms of reporting capabilities but updates and cost (frequent) demands are higher with the benefits offered.
So long as the requirements are not ever changing, with scheduling functionality, it's a handsome tool.
