

Microsoft Parallel Data Warehouse and SAP BW4HANA compete in the data warehousing category. Microsoft Parallel Data Warehouse has an advantage in cost efficiency and ease of integration with SQL Server, while SAP BW4HANA leads in real-time analytics and integration with SAP products.
Features: Microsoft Parallel Data Warehouse offers high performance, scalability, and cost efficiency with features like MPP processing, clustered column store indexes, and seamless integration with SQL Server. SAP BW4HANA is valued for its advanced data modeling, real-time analytics, and comprehensive predefined content, supporting a wide range of data sources.
Room for Improvement: Microsoft Parallel Data Warehouse faces challenges with SQL Server compatibility and requires expertise in handling large datasets. It could improve integration with non-Microsoft systems. SAP BW4HANA struggles with high costs, complex migration processes, and issues integrating with non-SAP tools, necessitating a better user interface.
Ease of Deployment and Customer Service: Microsoft Parallel Data Warehouse provides varied deployment options, including cloud environments, with generally solid customer support. SAP BW4HANA primarily offers on-premises deployment, leading to higher complexity, and while support generally meets expectations, there are concerns about responsiveness.
Pricing and ROI: Microsoft Parallel Data Warehouse is cost-effective, particularly for large-scale deployments, with customizable pricing models and satisfactory ROI. SAP BW4HANA, while robust, comes with high costs, additional license fees, and negotiation challenges, impacting its value for investment.
They are responsive and get back to us.
I would rate my experience with technical support around six on a scale of 1 to 10 because I have not had a particular experience with technical support.
In the meantime, I found solutions independently and provided two solutions to my client.
I am satisfied with the response time and quality.
We go from a couple of users to tons of users all the time, and it scales and handles things really well.
I give the scalability an eight out of ten, indicating it scales well for our needs.
As a consultant, we hire additional programmers when we need to scale up certain major projects.
Microsoft Parallel Data Warehouse is stable for us because it is built on SQL Server.
Addressing the cost would be the number one area for improvement.
It would be better to release patches less frequently, maybe once a month or once every two months.
When there are many users or many expensive queries, it can be very slow.
Integration needs improvement.
The integration with AI/ML in SAP BW4HANA is currently very limited, which is definitely an area that needs improvement.
Microsoft Parallel Data Warehouse is very expensive.
The certification cost for SAP BW4HANA in 2025 is expected to be one lakh forty thousand.
The columnstore index enhances data query performance by using less space and achieving faster performance than general indexing.
There's a feature that allows users to set alerts on triggers within reports, enabling timely actions on pending applications and effectively reducing waiting time.
Its scalability is impressive as it scales up and down really well.
The capability to handle a large amount of data and perform ETL operations is most valuable.
The best features include the ability to create data sources directly on tables, and perform mapping without creating info objects.
| Product | Mindshare (%) |
|---|---|
| SAP BW4HANA | 3.2% |
| Microsoft Parallel Data Warehouse | 3.2% |
| Other | 93.6% |

| Company Size | Count |
|---|---|
| Small Business | 16 |
| Midsize Enterprise | 6 |
| Large Enterprise | 22 |
| Company Size | Count |
|---|---|
| Small Business | 16 |
| Midsize Enterprise | 4 |
| Large Enterprise | 29 |
Microsoft Parallel Data Warehouse offers high performance and usability with seamless SQL Server integration, handling large data efficiently with a user-friendly interface. Known for its cost-effectiveness and robust security, it excels in integrating data across Microsoft ecosystem.
Microsoft Parallel Data Warehouse efficiently manages large datasets from diverse sources, supporting a unified data approach. Its integration with SQL Server and compatibility with tools like Qlik enhances data management and decision-making capabilities. With impressive scalability and security features, it is widely used in sectors such as finance, healthcare, and logistics for analytics and reporting. However, users seek improvements in integration with non-Microsoft layers, memory usage, SQL configuration, and scalability.
What are the key features of Microsoft Parallel Data Warehouse?In industries like finance, healthcare, and logistics, Microsoft Parallel Data Warehouse supports analytics, reporting, and decision-making processes. Organizations utilize it to maintain historical data, develop business intelligence models, and create actionable dashboards, benefiting from its integration with key tools and efficient data management.
SAP BW4HANA, a robust enterprise data warehousing and analytics platform, enhances business intelligence with performance and integration capabilities. It supports real-time reporting while integrating with SAP ERP systems.
SAP BW4HANA empowers data-driven decisions with its scalable architecture, providing businesses with extensive modeling and data transformation tools. Known for its powerful dashboard, the platform offers speed and user-friendly operations via the HANA database. It maintains performance while supporting Big Data and allows for customization tailored to specific organizational needs. It integrates effectively with SAP ECC systems, simplifying historical analysis and real-time reporting. Despite needing improvements in integration, better cost structures, and support for AI, its strengths in robust security, enhanced analytics, and extensive KPIs remain unmatched.
What are the key features of SAP BW4HANA?SAP BW4HANA is crucial in industries such as finance and retail, where businesses rely on data-driven insights for sales, financial reports, and operational analytics. Entities use it to generate detailed reports and perform KPI analyses, often integrating with analytical tools like Power BI and Tableau for enhanced data visualization and decision-making processes.
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