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Apache Spark vs SAP HANA comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

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

ROI

Sentiment score
6.6
Apache Spark enhances machine learning, cutting operational costs by up to 50%, with efficiency reliant on resources and expertise.
Sentiment score
6.1
SAP HANA users saw financial and speed benefits, but mid-sized businesses faced mixed feelings about integration costs and challenges.
We do not feel we're getting value for the investment due to the additional resources needed for integration and maintenance.
 

Customer Service

Sentiment score
5.9
Apache Spark support feedback varies, with mixed reviews on community forums, vendor support, and documentation adequacy.
Sentiment score
6.0
SAP HANA customer service is well-received but varies; improvements needed in response speed, solution accuracy, and expert access.
The community support is better than the official SAP support.
We often do not know when our ticket will be handled or who is handling it, and we can wait from one to four days for a reply, which is unexpected.
Time to respond to SAP support is an issue, and finding the right person and handling the whole process are problems too.
 

Scalability Issues

Sentiment score
7.5
Apache Spark excels in scalability, efficiently handling large data workloads with ease, though it requires skilled infrastructure management.
Sentiment score
7.8
SAP HANA efficiently scales for various user loads, supports growing data, and manages enterprise transactions across industries.
Our operations have grown from a hundred data operations a day to as many in a couple of seconds.
There is enough scalability offered by SAP to meet our deployment needs.
The scalability rating is based on the ability to expand.
 

Stability Issues

Sentiment score
7.5
Apache Spark is generally stable, trusted by companies; newer versions enhance reliability, though memory issues may arise without proper configuration.
Sentiment score
7.8
SAP HANA is generally stable and reliable, but some users face occasional performance issues linked to external factors.
Apache Spark resolves many problems in the MapReduce solution and Hadoop, such as the inability to run effective Python or machine learning algorithms.
We recently faced customer data loss during the cluster handover or failover fallback.
We have not had any problems in the last seven to eight years.
Regarding stability, they are using legacy systems and have implemented SAP HANA.
 

Room For Improvement

Apache Spark requires improvements in scalability, usability, documentation, memory efficiency, real-time processing, and broader language support for better performance.
SAP HANA faces criticism for high cost, complex deployment, resource limitations, and a challenging interface, affecting adoption and usability.
The setup process and deployment process for SAP HANA is complex.
The main issue is the ecosystem, which lacks the widespread support that SQL enjoys.
The problem is the price; it's too expensive for what it actually delivers.
 

Setup Cost

Apache Spark is cost-effective but may incur expenses from hardware, cloud resources, or commercial support, impacting deployment costs.
SAP HANA's high pricing favors larger enterprises, requiring careful budgeting, with some opting for alternatives due to costs.
It's a recurring subscription model, which is expensive compared to legacy systems with just a maintenance fee.
SAP is not a cheap company, and its licenses are expensive.
I would rate the price for SAP HANA as high.
 

Valuable Features

Apache Spark offers fast in-memory processing, scalable analytics, MLlib for machine learning, SQL support, and seamless integration with languages.
SAP HANA offers real-time analytics, integration, scalability, and a user-friendly interface, supporting large enterprises with efficient data handling.
Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming.
This architecture allows for faster data processing and real-time analytics that were not possible with traditional databases.
The concept enhances speed, allowing the database to serve and move data quickly.
One of our dashboards using Excelsius was previously developed on normal BW on Oracle data, which took 10 minutes to open. After developing the same calculation views using those tables and replacing them with calculation views in Excelsius, the dashboard opened in seconds.
 

Categories and Ranking

Apache Spark
Average Rating
8.4
Reviews Sentiment
7.3
Number of Reviews
67
Ranking in other categories
Hadoop (1st), Compute Service (4th), Java Frameworks (2nd)
SAP HANA
Average Rating
8.2
Reviews Sentiment
6.4
Number of Reviews
89
Ranking in other categories
Data Virtualization (2nd), Embedded Database (4th), Relational Databases Tools (3rd)
 

Featured Reviews

Omar Khaled - PeerSpot reviewer
Empowering data consolidation and fast decision-making with efficient big data processing
I can improve the organization's functions by taking less time to make decisions. To make the right decision, you need the right data, and a solution can provide this by hiring talent and employees who can consolidate data from different sources and organize it. Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming. To make the right decision, you should have both accurate and fast data. Apache Spark itself is similar to the Python programming language. Python is a language with many libraries for mathematics and machine learning. Apache Spark is the solution, and within it, you have PySpark, which is the API for Apache Spark to write and run Python code. Within it, there are many APIs, including SQL APIs, allowing you to write SQL code within a Python function in Apache Spark. You can also use Apache Spark Structured Streaming and machine learning APIs.
Dhiraj Jankar - PeerSpot reviewer
Users appreciate the mix of in-memory architecture and ease of backend management
The most valuable SAP HANA features are that, compared to Oracle, there is less administrative work and less complex work to handle, making it very straightforward. The tool itself takes care of the backend work. The main benefits SAP HANA provides to users include its architecture, which combines both column store and row store capabilities. Some places find the row feature important, whereas others find column store important. It allows some tables to be stored in a column base, others in a row base, and the In-Memory functionalities are mostly the highlight of SAP HANA, especially compared to disk-based memory users. This was the main reason SAP HANA was introduced initially.
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Top Industries

By visitors reading reviews
Financial Services Firm
26%
Computer Software Company
10%
Manufacturing Company
7%
Comms Service Provider
7%
Manufacturing Company
15%
Computer Software Company
9%
Financial Services Firm
9%
Government
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?
Apache Spark is open-source, so it doesn't incur any charges.
What needs improvement with Apache Spark?
There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...
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
 

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: July 2025.
865,164 professionals have used our research since 2012.