Apache Spark vs. SAP HANA Cloud

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.6 out of 10
N/A
N/AN/A
SAP HANA Cloud
Score 8.5 out of 10
N/A
SAP HANA is an application that uses in-memory database technology to process very large amounts of real-time data from relational databases, both SAP and non-SAP, in a very short time. The in-memory computing engine allows HANA to process data stored in RAM as opposed to reading it from a disk which means that the data can be accessed in real time by the applications using HANA. The product is sold both as an appliance and as a cloud-based software solution.
$0.95
per month Capacity Units
Pricing
Apache SparkSAP HANA Cloud
Editions & Modules
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Offerings
Pricing Offerings
Apache SparkSAP HANA Cloud
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeOptional
Additional DetailsIncludes a one year free trial.
More Pricing Information
Community Pulse
Apache SparkSAP HANA Cloud
Considered Both Products
Apache Spark
Chose Apache Spark
Apache Spark is a fast-processing in-memory computing framework. It is 10 times faster than Apache Hadoop. Earlier we were using Apache Hadoop for processing data on the disk but now we are shifted to Apache Spark because of its in-memory computation capability. Also in SAP …
Chose Apache Spark
Databricks uses Spark as a foundation, and is also a great platform. It does bring several add-ons, which we did not feel needed by the time we evaluated - and haven't needed since then. One interesting plus in our opinion was the engineering support, which is great depending …
Chose Apache Spark
We evaluated SAS alongside with Apache Spark but during the course of proof of concept found that Apache Spark was able to support the hadoop eco-system and hadoop file system much better. It was much faster at that time while having the ability to process data quickly for the …
SAP HANA Cloud
Chose SAP HANA Cloud
As SAP HANA is an in-memory database, it can process data swiftly and can provide detailed analysis reports compared to other tools. Another advantage is it supports different data types, so if any application is looking for scalability, performance, security, and risk …
Chose SAP HANA Cloud
We compared Microsoft BI with SAP HANA. The reasons to go with SAP HANA were - 1. ability to ingest data into HANA from a non SAP database 2. in-memory database resulting in faster real time analytics 3. ability to scale up 4. ability to replicate data real time 5. very solid …
Top Pros

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Top Cons

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Best Alternatives
Apache SparkSAP HANA Cloud
Small Businesses

No answers on this topic

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User Ratings
Apache SparkSAP HANA Cloud
Likelihood to Recommend
9.9
(24 ratings)
8.3
(294 ratings)
Likelihood to Renew
10.0
(1 ratings)
9.1
(6 ratings)
Usability
10.0
(3 ratings)
9.1
(10 ratings)
Availability
-
(0 ratings)
3.6
(1 ratings)
Performance
-
(0 ratings)
3.6
(1 ratings)
Support Rating
8.7
(4 ratings)
8.4
(254 ratings)
Implementation Rating
-
(0 ratings)
9.1
(2 ratings)
Configurability
-
(0 ratings)
3.6
(1 ratings)
Ease of integration
-
(0 ratings)
4.5
(1 ratings)
Product Scalability
-
(0 ratings)
4.5
(1 ratings)
Vendor post-sale
-
(0 ratings)
4.5
(1 ratings)
Vendor pre-sale
-
(0 ratings)
3.6
(1 ratings)
User Testimonials
Apache SparkSAP HANA Cloud
Likelihood to Recommend
Apache
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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SAP
It is well organized. One can use it for the company's portfolio management. Various tasks can be done for managerial purposes. One can track the material from start to end product: for example, raw material, packing material & consumable material to formulated bulk and formulated drug product. This can help to manage spending as well as finding costing of the product.
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Pros
Apache
  • 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.
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SAP
  • Real-time reporting and analytics on data: because of its in-memory architecture, it is perfect for businesses that need to make quick decisions based on current information.
  • Managing workload with complex data: it can handle a vast range of data types, including relational, documental, geospatial, graph, vector, and time series data.
  • Developing and deploying intelligent data applications: it provides various tools for such applications and can be used for machine learning and artificial intelligence to automate tasks, gain insights from data, and make predictions.
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Cons
Apache
  • 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
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SAP
  • Requires higher processing power, otherwise it won't fly. How ever computing costs are lower. Incase you are migrating to cloud please do not select the highest config available in that series . Upgrading it later against a reserved instance can cost you dearly with a series change
  • Lack of clarity on licensing is one major challenge
  • Unless S/4 with additional features are enabled mere migration HANA DB is not a rewarding journey. Power is in S/4
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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SAP
At this moment we are not focusing on SAP, however would love to in the future. This is primarily because of our limited ability to generate more revenue to fund for SAP partnerships and products. Our initial tryst with SAP Partneredge open ecosystem didn't go as planned and we have shelved that for now. Hope we can revive in the future
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Usability
Apache
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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SAP
In addition to the points described in the previous parts of the review, I believe that as I gain more experience with the product over time, I will be able to better describe my experience with this tool. Meanwhile, I can confirm that the possibilities presented to my organization by the change to SAP HANA, at the moment, have been very important to evolve the analytical and strategic field towards a new path.
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Support Rating
Apache
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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SAP
One specific example of how the support for SAP HANA Cloud impacted us is in our efforts to troubleshoot and resolve technical issues. Whenever we encountered an issue or had a question, the support team was quick to respond and provided us with clear and actionable guidance. This helped us avoid downtime and keep our analytics operations running smoothly.
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Implementation Rating
Apache
No answers on this topic
SAP
Professional GIS people are some of the most risk-averse there are, and it's difficult to get them to move to HANA in one step. Start with small projects building to 80% use of HANA spatial over time.
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Alternatives Considered
Apache
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
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SAP
I have deep knowledge of other disk based DBMSs. They are venerable technology, but the attempts to extend them to current architectures belie the fact they are built on 40 year old technology. There are some good columnar in-memory databases but they lack the completeness of capability present in the HANA platform.
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Scalability
Apache
No answers on this topic
SAP
Limitation of training deliverable by organization
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Return on Investment
Apache
  • 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.
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SAP
  • ROI has always been high in terms of the functionality that it offers and the security features it comes with.
  • Managing large volumes of data in real-time is not an easy task, but it does it pretty well with faster data processing.
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