

SAP Data Services and dbt compete in the data integration and transformation category. SAP Data Services appears more robust in terms of integration with SAP systems, while dbt has the upper hand in speed and simplicity due to its ELT architecture and open-source model.
Features: SAP Data Services is renowned for its seamless integration with SAP systems, offering robust data extraction, transformation, and loading (ETL) capabilities across various databases. It includes powerful data quality management features. dbt is celebrated for its ELT architecture, enabling faster transformations for large datasets, and its SQL orientation provides a streamlined approach for orchestrating data transformations in one place.
Room for Improvement: SAP Data Services users suggest the need for a common repository, better documentation, and a more user-friendly interface for non-technical users. Additionally, improved dynamic job scheduling and broader integration with other systems are desired. dbt users seek improved integration with more platforms, enhanced debugging capabilities, and native support for Python transformations. Better code migration and greater structural flexibility are also requested.
Ease of Deployment and Customer Service: SAP Data Services primarily offers on-premises deployments with some hybrid cloud options, coupled with comprehensive technical support, though response times are sometimes slow. dbt provides flexibility through its public cloud presence and on-premises options. While dbt's customer support is generally well-regarded, some users have encountered challenges during initial setups and technical assistance.
Pricing and ROI: SAP Data Services is often perceived as expensive, making it more suitable for large enterprises, but it offers significant ROI in high-volume environments due to its robust capabilities. dbt's open-source nature makes it cost-effective, with affordable licensing for extended services. The strong community support and easy setup enhance ROI through reduced operational costs and quick deployment cycles.
There is operational efficiency achieved, and data quality and governance have also been achieved with modular SQL and version controlling, which reduced duplication of data and data errors.
I have seen a return on investment as it means we don't have to employ as many people.
Since we migrated from SSIS to dbt model architecture, it takes around four hours only to complete a full refresh.
SAP is indeed good at all this now, with so many components such as SAP Signavio, SAP EINS, SAP Work Zone, SAP ALM, Cloud ALM, SAP public cloud, SAP private cloud, and BTP, all of which are essential to meet the latest cutting-edge technologies.
If you type your question, you will likely find that someone has already asked it, so we do not need to contact their support directly.
I would rate the technical support a nine out of ten.
We ran dbt Core, which is open-source, so there is no direct vendor support.
If you keep a high priority issue, such as a production impact, they certainly come and address it in no time.
The level three support is better because they know what they are doing.
The bottlenecks that we have are not coming from dbt; they are coming from Snowflake.
We were processing large volumes of financial documents, hundreds of trial balances, balance sheets, and invoice sets, and dbt handled the transformation layer without issues.
dbt is quite scalable since it has its own feature set for incorporating business logic.
If I were to rate it from one to 10, I would say it has a nine to 10 for scalability.
Comparing it to tools I have seen in the past, such as Informatica and Alteryx, dbt can easily match up to that rating, specifically for stability.
Every upgrade is a little bit of a risk for us because we do not know if the workarounds that we developed will be available for the next version.
When I conduct dbt tests, the data processed in the data warehouse performs exactly as expected.
I would rate the stability of SAP Data Services as very stable, a ten.
Improvement is needed in the tool itself in terms of the copilot, in terms of covering outages, in terms of testing, and in terms of quality reasons related to governance and collaboration.
The whole data testing field is not very mature. It is not the same as software testing; for example, you have test suites, test tools, and profilers, but for data testing, it is not yet that advanced.
dbt does not have a native concept of multi-tenant or multi-standard project organization.
Now, they are coming up with many pricing options, which is tricky; they offer one thing for free, but charge for nine others.
SAP Data Services does handle integration with third-party systems.
The documentation is not up to the mark.
The course content that dbt provides is free and excellent for anyone starting out.
dbt is open source for its core modules.
I mentioned the cost as one of the advantages, specifically the license cost.
dbt has positively impacted my organization by allowing us to create our data pipelines much faster, going from ingestion of data to creating a data product in weeks instead of months.
There are the benefits of having code, so you have a software development lifecycle; you can use version control, testing, and documentation.
The tests, especially custom tests for financial data like validating that debits equal credits, caught a lot of our data quality issues early.
SAP is indeed good at all this now, with so many components such as SAP Signavio, SAP EINS, SAP Work Zone, SAP ALM, Cloud ALM, SAP public cloud, SAP private cloud, and BTP, all of which are essential to meet the latest cutting-edge technologies.
SAP Data Services is mainly used for extraction of data, and it works with all databases.
It remains a fast data-moving tool, faster than most new ones.
| Product | Mindshare (%) |
|---|---|
| dbt | 1.4% |
| SAP Data Services | 1.5% |
| Other | 97.1% |


| Company Size | Count |
|---|---|
| Small Business | 2 |
| Midsize Enterprise | 3 |
| Large Enterprise | 6 |
| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 5 |
| Large Enterprise | 36 |
dbt is a transformational tool that empowers data teams to quickly build trusted data models, providing a shared language for analysts and engineering teams. Its flexibility and robust feature set make it a popular choice for modern data teams seeking efficiency.
Designed to integrate seamlessly with the data warehouse, dbt enables analytics engineers to transform raw data into reliable datasets for analysis. Its SQL-centric approach reduces the learning curve for users familiar with it, allowing powerful transformations and data modeling without needing a custom backend. While widely beneficial, dbt could improve in areas like version management and support for complex transformations out of the box.
What are the most valuable features of dbt?
What benefits should you expect from using dbt?
In the finance industry, dbt helps in cleansing and preparing transactional data for analysis, leading to more accurate financial reporting. In e-commerce, it empowers teams to rapidly integrate and analyze customer behavior data, optimizing marketing strategies and improving user experience.
SAP Data Services is a comprehensive data integration and management tool known for its robust ETL functionality and seamless data quality management across SAP and non-SAP systems, providing flexibility and effective data handling.
SAP Data Services offers extensive integration capabilities with a range of systems, enabling efficient data migration, warehousing, and quality assurance. Despite challenges in connectivity, SQL optimization, and handling big data, it remains a top choice for data extraction and transformation. Its user-friendly interface and customization options enhance ease of use. The tool is recognized for scalability, performance, customer satisfaction, and supporting complex data transformations for improved analytics.
What are the key features of SAP Data Services?SAP Data Services is widely implemented across industries like banking, telecom, and manufacturing. Companies leverage it to integrate multiple data sources and manage migrations from legacy to modern platforms such as cloud environments and HANA architecture. It supports complex transformations essential for financial, operational, and business intelligence reporting, enhancing insights and decision-making.
We monitor all Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.