SAP HANA vs Spark SQL comparison

SAP and Apache are both solutions in the Hadoop category. Additionally, 91% of SAP users are willing to recommend the solution, compared to 86% of Apache users who would recommend it.

SAP HANA

Read 93 SAP HANA reviews

8,866 Views
1,064 Comparison Views

91% willing to recommend

Spark SQL

Read 15 Spark SQL reviews

1,242 Views
1,105 Comparison Views

86% willing to recommend

SAP HANA

Spark SQL

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Executive Summary

We performed a comparison between SAP HANA and Spark SQL based on real PeerSpot user reviews.

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop.

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Hadoop

May 2026

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Review summaries and opinions

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

Categories and Ranking

SAP HANA

Average Rating

8.4

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

Data Virtualization (2nd), Embedded Database (1st), Relational Databases Tools (3rd), Database Management Systems (DBMS) (5th)

Spark SQL

Average Rating

7.8

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

Hadoop (5th)

Featured Reviews

Bassem Mohammed

IT Application Manager at TBTD

Integrated business processes have improved real-time analytics and financial performance

Whether I or my customers utilize SAP HANA's real-time analytics depends on the data needed to be displayed, and then choosing how to transfer the data to the analytic object, data source, or database to be displayed in the reports. It may be near real-time, real-time, or running batch every day. There are tools for integration between third-party systems, such as OData or APIs from SAP to get in or push data to other systems. Support for both structured and unstructured data helps organizations unify data sources depending on the needed data to be displayed. There are many ways to store it for report displays, but structured data will be better than unstructured data.

Read full review

Kemal Duman

Team Lead, Data Engineering at Nesine.com

Data pipelines have run faster and support flexible batch and streaming transformations

We do not have any performance problems, but we do have some resource problems. Spark SQL consumes so many resources that we migrated our streaming job from Spark to Apache Flink. Resource management in Spark SQL should be better. It consumes more resources, which is normal. The main reason we switched from Spark is memory and CPU consumption. The major reason is the resource problem because the number of streaming jobs has been increasing in our company. That is why we considered resource management as a priority. Because of the resource consumption, I would say the development of Spark SQL is better. For development purposes, it is a top product and not difficult to work with, but resources are the major problem. We changed to Flink regardless of development time. Development time is less in Spark compared with Flink.

Read full review

Quotes from Members

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

Pros

"It is very stable and very innovative. You can integrate many applications with it."

"The solution's in-memory computing and the efficient response time are very good features."

"The solution is currently very stable."

"This is a feature-rich product and I like all of them."

"We use this solution in conjunction with other prompted solutions primarily for real-time analytics."

"SAP HANA has everything, and that is the advantage of SAP because you can get everything in one suite, whatever you need for your company."

"What's most valuable in SAP HANA is that it covers the business process systems of my company. The solution also helps because it makes almost everything automated. Another valuable feature of SAP HANA is that it can be integrated with third parties and other enhancements needed by the company."

"If you want to scale with new processes and new reports, that's fairly easy."

More SAP HANA pros

"Spark SQL gives us a handful of methods to design queries based on its own syntax and also incorporates the regular SQL syntax within tasks."

"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."

"We use it to gather all the transaction data."

"I find the Thrift connection valuable."

"The scalability of the solution is good."

"The speed of getting data, as our TBs are big and it's a lot of data."

"Speed is the major benefit of using Spark SQL."

"The performance is one of the most important features, and it has an API to process the data in a functional manner."

More Spark SQL pros

Cons

"In a future release, SAP HANA should add a module for taxation, such as income tax and withholding tax."

"They should develop and improve the solution's data management system."

"The inclusion of a well-performing Time Machine is vital."

"I'd just like to see some more improvements done on the training, both on the functional training and technical training sides as a part of the complete solution."

"You cannot apply mulit-join inside the calculation views, for example, when you are joining tables."

"While SAP HANA is good, it would be better if it comes at a more affordable price."

"The solution can be expensive."

"There is room for improvement in the deployment process as it takes much time and is time-consuming and complex."

More SAP HANA cons

"Being a new user, I am not able to find out how to partition it correctly."

"This solution could be improved by adding monitoring and integration for the EMR."

"There are many inconsistencies in syntax for the different querying tasks like selecting columns and joining between two tables so I'd like to see a more consistent syntax."

"Spark SQL consumes so many resources that we migrated our streaming job from Spark to Apache Flink."

"Being a new user, I am not able to find out how to partition it correctly. I probably need more information or knowledge. In other database solutions, you can easily optimize all partitions. I haven't found a quicker way to do that in Spark SQL. It would be good if you don't need a partition here, and the system automatically partitions in the best way. They can also provide more educational resources for new users."

"Anything to improve the GUI would be helpful."

"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."

"There should be better integration with other solutions."

More Spark SQL cons

Pricing and Cost Advice

"The tool’s subscription is yearly."

"Price-wise, the product falls on the higher side of the spectrum. There is no need to pay for maintenance and support additionally. Support is available for bug fixes in the product."

"Set up a consortium of consulting partners and hardware vendors to define your tech. Landscape TCO (total cost of ownership) and then approach the OEM for pricing (on-premise or on cloud or a hybrid model). Check if you can bring your own licenses for some of the existing application licenses on the new platform, to reduce TCO."

"SAP HANA is expensive, but unfortunately, we do not have any alternatives."

"We pay annually for the license of the solution."

"I would rate the pricing of the solution a ten on ten. The pricing for on-prem SAP HANA is on a yearly basis and expensive. I am not aware of the actual pricing but it is based on 64 Gigabytes. For instance, if you consider $10,000 for 64 Gigabytes, then you can calculate the cost for Terabytes in case you have one. You will have to buy expensive hardware as well which is quite a lot."

"The price of SAP HANA is very expensive and it is paid annually."

"The pricing is a bit on the high side."

More SAP HANA pricing and cost advice

"We use the open-source version, so we do not have direct support from Apache."

"There is no license or subscription for this solution."

"The solution is open-sourced and free."

"The solution is bundled with Palantir Foundry at no extra charge."

"The on-premise solution is quite expensive in terms of hardware, setting up the cluster, memory, hardware and resources. It depends on the use case, but in our case with a shared cluster which is quite large, it is quite expensive."

"We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small."

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Top Industries

By visitors reading reviews

Manufacturing Company

15%

Financial Services Firm

11%

Comms Service Provider

Construction Company

Financial Services Firm

21%

University

12%

Healthcare Company

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	26
Midsize Enterprise	12
Large Enterprise	64

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	6
Large Enterprise	4

Questions from the Community

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...

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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...

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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...

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What needs improvement with Spark SQL?

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What is your primary use case for Spark SQL?

Spark SQL has been in our stack for less than one year, though some of our colleagues are using it. It is a useful product for transformation jobs. We generally use Spark SQL for batch processing. ...

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What advice do you have for others considering Spark SQL?

Regarding the Catalyst query optimizer, I think we are using it. We were using it in the past, but I am not certain if we use it now. We used it a long time ago. I rate my experience with Spark SQL...

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Comparisons

Oracle Database vs SAP HANA

Compared 29% of the time

SQL Server vs SAP HANA

Compared 9% of the time

MySQL vs SAP HANA

Compared 6% of the time

IBM Db2 Database vs SAP HANA

Compared 5% of the time

Oracle Database In-Memory vs SAP HANA

Compared 4% of the time

More SAP HANA Competitors

Amazon EMR vs Spark SQL

Compared 22% of the time

Apache Spark vs Spark SQL

Compared 20% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 19% of the time

HPE Data Fabric vs Spark SQL

Compared 12% of the time

Cloudera Distribution for Hadoop vs Spark SQL

Compared 4% of the time

More Spark SQL Competitors

Product Reports

Buyer's Guide

SAP HANA

May 2026

Download SAP HANA product report

Buyer's Guide

Spark SQL

May 2026

Download Spark SQL product report

Also Known As

SAP High-Performance Analytic Appliance, HANA

No data available

Overview

SAP HANA is a high-speed in-memory data platform providing real-time analytics and quick data retrieval while supporting enterprise scalability and integration.

Designed for efficient data processing, SAP HANA offers robust security, ease of use, and advanced analytics through an architecture that ensures flexibility and stability. This allows seamless integration with third-party systems, significantly improving data management and decision-making capabilities. Enterprises benefit from data modeling, compression, comprehensive dashboards, and real-time insights. However, users have noted high costs, speed, and scalability issues, as well as complexity in interface and customizations. There's also a need for better integration with third-party tools, clearer documentation, and enhanced connectivity with Java and other platforms.

What are the key features of SAP HANA?

In-memory Processing: Delivers rapid data retrieval and analytics.
Data Modeling and Compression: Enhances storage efficiency and speed.
Advanced Analytics: Offers comprehensive insights for strategic decisions.
Seamless Integration: Integrates with third-party systems effectively.
Real-time Insights: Supports real-time analytics across data sets.

What benefits should users expect when evaluating SAP HANA?

Improved Decision-making: Informs strategic business choices with advanced analytics.
Operational Efficiency: Enhances productivity by streamlining data management.
Scalability: Supports growth and evolving data needs effectively.
Security: Protects data integrity with robust mechanisms.

SAP HANA is deployed for enterprise resource planning across industries, notably in finance, HR, and sales. It supports real-time analytics, business intelligence, and integrates with SAP ERP and S/4HANA, offering tailored solutions for sectors like supply chain management, CRM, procurement, and financial analysis.

SAP

Spark SQL leverages SQL capabilities to process large datasets, offering high performance, seamless integration with Spark programs, and the ability to run parallel queries. It supports Hive interoperability and facilitates data transformation with DataFrames and Datasets.

Spark SQL enables efficient data engineering, transformation, and analytics for organizations dealing with large-scale data processing. It supports big data queries, builds data pipelines and warehouses, and interfaces with various databases, especially in distributed settings such as Hadoop and Azure. Users employ Spark SQL to establish business logic in Jupyter notebooks and facilitate data loading into SQL Server, enabling analytics with tools like Power BI. The documentation and flexibility to manage extensive data processing are valued by users, although a steep learning curve and documentation clarity are noted challenges. Enhancements for data visualization, GUI, and resource management alongside better integration with tools like Tableau are recommended.

What are the key features of Spark SQL?

Query Language: Supports complex SQL queries for effective large dataset processing.
Seamless Integration: Easily integrates into Spark programs, boosting performance and speed.
Parallel Queries: Capable of running numerous queries in parallel to enhance processing efficiency.
Interoperability with Hive: Facilitates interaction within distributed data ecosystems efficiently.
Data Transformation: Utilizes DataFrames and Datasets for flexible data manipulation.

What benefits or ROI should users consider in reviews?

Performance: Offers high-speed data processing for demanding workloads.
Scalability: Efficiently handles increasing data volumes and processing demands.
Ease of Use: Facilitates simpler SQL-based data management operations.
Flexibility: Adapts to various data sources and formats for broad usability.

In industries, Spark SQL is a critical part of data engineering, transformation, and analytics. It empowers organizations to manage big data processing and analytics in sectors like finance, healthcare, and telecommunications. By enabling seamless data pipeline creation, it supports real-time business decision-making processes and data-driven strategies across sectors.

Apache

Sample Customers

Unilever, NHS 24, adidas Group, CHIO Aachen, Hamburg Port Authority (HPA), Bangkok Airways Public Company Limited

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop. Updated: May 2026.

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