Snowflake
Data & Cloud Services
In a nutshell
Snowflake is a cloud-based data platform for storing, integrating, analyzing, and sharing enterprise data. The platform was developed specifically for the cloud and enables companies to centrally consolidate data from various sources and use it for analytics, reporting, data products, and AI applications.
Snowflake positions itself not only as a traditional data warehouse, but as a comprehensive data cloud that connects data, applications, and organizations.
Why is Snowflake relevant?
Many companies have built up their data landscapes over the years. Data is stored in ERP systems, CRM solutions, line-of-business applications, cloud platforms, Excel files, and numerous other sources. At the same time, there is a growing need to make this data available for analytics, planning, self-service reporting, and AI applications.
Traditional data architectures often reach their limits in this context. Data must be copied multiple times, different systems produce different results, and new use cases require ever-increasing levels of integration.
Snowflake takes a different approach. The platform makes it possible to store data centrally while still using it flexibly for a variety of use cases. Snowflake is particularly well known for its ease of scaling, the separation of storage and computing power, and the ability to securely share data with other departments, companies, or partners. As a result, Snowflake is becoming the central data hub for many companies’ analytics and AI strategies.
The platform’s importance has grown significantly in recent years due to the increasing adoption of AI. Today, companies need a reliable foundation to make structured and unstructured data usable for machine learning, generative AI, and AI agents. This is precisely where Snowflake positions itself as a modern data platform for data-driven business models.
In addition, many companies now view Snowflake as an alternative or complement to platforms such as Databricks, Microsoft Fabric, or SAP Business Data Cloud. The key question here is often: How can we create a future-proof data platform for analytics, planning, and AI?
How does celver AG provide support?
Many companies are currently facing the challenge of redefining their future data architecture. Often, this is not about selecting a single technology, but rather about developing an overarching strategy for data, analytics, and AI.
That is why celver AG first helps customers develop a data strategy and a suitable target architecture. The focus is on issues such as data integration, data products, analytics, governance, and the future use of AI.
Typical questions from our customers include:
- How do Snowflake, Microsoft Fabric, and Databricks differ?
- Which platform is compatible with the SAP environment that exists in many cases?
- How can data products be deployed?
- What role do analytics and AI play?
- And how can data from SAP and non-SAP systems be shared?
In this context, we always view Snowflake as part of an overarching architecture rather than as an isolated technology decision.
Our goal is to help companies leverage data as a strategic asset over the long term and lay the foundation for analytics, planning, and AI.
Related Terms
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Frequently Asked Questions
Yes, but that's not all. Snowflake started out as a cloud data warehouse and has since evolved into a comprehensive data platform that supports analytics, data sharing, data engineering, and AI applications.
Snowflake has traditionally placed a strong emphasis on data provision, data management, and analytics. Databricks originally had stronger roots in data engineering, data science, and machine learning. Today, however, the platforms are increasingly overlapping.
Both platforms support modern data and analytics architectures. Microsoft Fabric is particularly tightly integrated into the Microsoft ecosystem, while Snowflake was deliberately designed to be cross-platform. Which solution is a better fit depends heavily on individual requirements and the existing system landscape.
Yes. Many companies use Snowflake to integrate and analyze data from SAP and non-SAP systems. The specific architecture depends on the respective business requirements.
Not exactly. SAP Business Data Cloud and Snowflake have different focuses. However, as part of their data strategies, many companies are evaluating what role SAP, Microsoft, and independent data platforms should play in the future.
Companies particularly value the cloud-native architecture, scalability, ease of collaboration across organizational boundaries, and support for modern data and AI use cases.
Snowflake is continuously expanding its platform with features for machine learning, generative AI, and AI applications. The goal is to make enterprise data directly usable for modern AI scenarios.
