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Apache Spark vs SAP HANA comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Apache Spark
Average Rating
8.4
Reviews Sentiment
7.7
Number of Reviews
64
Ranking in other categories
Hadoop (1st), Compute Service (4th), Java Frameworks (2nd)
SAP HANA
Average Rating
8.2
Reviews Sentiment
6.5
Number of Reviews
84
Ranking in other categories
Data Virtualization (2nd), Embedded Database (4th), Relational Databases Tools (4th)
 

Featured Reviews

SurjitChoudhury - PeerSpot reviewer
Offers batch processing of data and in-memory processing in Spark greatly enhances performance
Spark supports real-time data processing through Spark Streaming. It allows for batch processing of data. If you have immediate data, like chat information, that needs to be processed in real-time, Spark Streaming is used. For data that can be evaluated later, batch processing with Apache Spark is suitable. Mostly, batch processing is utilized in our organization, but for streaming data processing, tools like Kafka are often integrated. In-memory processing in Spark greatly enhances performance, making it a hundred times faster than the previous MapReduce methods. This improvement is achieved through optimization techniques like caching, broadcasting, and partitioning, which help in optimizing queries for faster processing.
Md Ashraful  Islam - PeerSpot reviewer
Powerful in-memory processing capabilities and advanced analytics, enhancing real-time decision-making, but with challenges in reaching SAP for support
Continuous improvement is essential for these systems to stay competitive and relevant in the market. Currently, for support, we rely on local implementers who act as intermediaries. This makes it challenging for me to reach out to SAP directly. If, for instance, I encounter any issues during the ongoing project, expanding it to other companies may pose a challenge without direct access to SAP support.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"The deployment of the product is easy."
"The most significant advantage of Spark 3.0 is its support for DataFrame UDF Pandas UDF features."
"The most valuable feature of Apache Spark is its ease of use."
"The solution has been very stable."
"The processing time is very much improved over the data warehouse solution that we were using."
"We use it for ETL purposes as well as for implementing the full transformation pipelines."
"The fault tolerant feature is provided."
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"SAP HANA's best features are its programmability and extensibility - you can size and shape the software however you need."
"The data storage requirement is reduced from the original database to the HANA database."
"SAP HANA is a stable solution."
"The most valuable features are the flexibility and the integration with other solutions in data quality."
"We appreciate that the current, redesigned version of this solution that is much more straightforward for new users, and has been well thought out with industry best practice standards in mind."
"The most valuable feature of SAP HANA is its performance and integration."
"We have found the solution to be customizable and it is beneficial it comes as a bundled package. Additionally, it is user-friendly."
"SAP HANA is vertically and horizontally scalable."
 

Cons

"Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use."
"The main concern is the overhead of Java when distributed processing is not necessary."
"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."
"The solution’s integration with other platforms should be improved."
"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."
"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."
"When you do a report on a non-SAP platform, you face some compatibility problems."
"There are a few areas wherein there could be a patch upgrade, and that can cover up the country-specific payroll areas."
"The performance and integration with other products are areas in need of improvement."
"While new users to this solution have the benefit of the new design, existing ERP users may experience issues with migrating legacy data. We would like to see development of ready-made tools that allow for easy mapping when upgrading."
"In terms of improvement, the speed is not as good as we thought it would be. That is why we are trying different solutions that will be built with different technologies."
"There could be better management for faster updates, last year there were some changes in India to the e-invoicing feature."
"The product is very demanding on memory requirements."
"The documentation can be improved in the future."
 

Pricing and Cost Advice

"Spark is an open-source solution, so there are no licensing costs."
"The product is expensive, considering the setup."
"Apache Spark is an expensive solution."
"Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."
"Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."
"We are using the free version of the solution."
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."
"SAP HANA is very expensive for small to medium businesses."
"The price of licensing is dependent on the size of the project, however, we have found that there is scope to negotiate the cost. If the solution is implemented on-premises there may be some extra costs for hosting etc."
"A monthly or yearly license must be purchased, although its utility will be based on the cost-benefit analysis that is reached by the individual customer."
"The price is high and could be a bit cheaper."
"The licensing could improve."
"We pay $200 on a monthly basis."
"The pricing for SAP HANA is high. You pay a lot for the license, and you also have to pay for some add-ons."
"SAP HANA is very expensive here in China. My company bought the SAP ERP suite, which includes SAP HANA. For others who use SAP HANA as an analytical database, CPU numbers will affect the pricing, so as a solution, it's costly."
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Top Industries

By visitors reading reviews
Financial Services Firm
27%
Computer Software Company
13%
Manufacturing Company
8%
Retailer
5%
Manufacturing Company
15%
Computer Software Company
12%
Financial Services Firm
10%
Energy/Utilities Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Apache Spark?
We use Spark to process data from different data sources.
What is your experience regarding pricing and costs for Apache Spark?
Compared to other solutions like Doc DB, Spark is more costly due to the need for extensive infrastructure. It requires significant investment in infrastructure, which can be expensive. While cloud...
What needs improvement with Apache Spark?
The main concern is the overhead of Java when distributed processing is not necessary. In such cases, operations can often be done on one node, making Spark's distributed mode unnecessary. Conseque...
What are the biggest benefits of using SAP HANA?
Based on my work with SAP HANA, the biggest benefit that it can bring to your business is total data management. This product is by SAP - a company that serves almost all needs a client may have co...
Is SAP HANA’s customer and technical support reliable?
We have been using SAP HANA for a fairly short period of time and have only taken advantage of their customer support. So far, we have not had issues that required specialized help from technical s...
Is SAP HANA difficult to set up and start using?
SAP HANA is fairly easy to set up, however, I do not think a complete beginner can do it. You certainly need some preparation - either you need to have experience with similar solutions, or with ot...
 

Comparisons

 

Also Known As

No data available
SAP High-Performance Analytic Appliance, HANA
 

Learn More

 

Overview

 

Sample Customers

NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
Unilever, NHS 24, adidas Group, CHIO Aachen, Hamburg Port Authority (HPA), Bangkok Airways Public Company Limited
Find out what your peers are saying about Apache Spark vs. SAP HANA and other solutions. Updated: December 2024.
824,053 professionals have used our research since 2012.