With our Databricks consulting, you build a scalable, AI-ready data platform that combines the data lake and the data warehouse. We support you from architecture and data integration to stable operations – with a clear focus on governance, costs and your existing systems such as SAP.
data, analytics and AI combined
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Databricks combines the flexibility of a data lake with the reliability of a data warehouse. As a result, data engineering, analytics and AI work on the same data foundation – instead of in separate silos. How the lakehouse fits into an overall architecture is shown in our data warehouse consulting.
Data engineering, BI and machine learning access the same data – without copies between systems.
With Delta Lake and Apache Iceberg, your data stays open and independent of individual tools.
Access rights, lineage and auditability are managed centrally for all data and models.
Compute scales automatically with demand. In addition, budgets and monitoring keep costs under control.
From the first architecture decision to productive operation – this is how we make your data platform powerful, scalable and ready for AI.
Lakehouse design with a medallion architecture, workspace structure and Unity Catalog – tailored to your cloud and your requirements.
Batch and streaming with Lakeflow (formerly Delta Live Tables) and Spark Structured Streaming, complemented by Qlik Talend as well as SAP and API connectors.
Replacing SAP BW, classic data warehouses or Hadoop – step by step and without losing reports.
Role-based access, lineage and data quality with Unity Catalog – embedded in your data governance strategy.
MLflow, feature engineering and the integration of large language models for intelligent applications and agentic AI.
CI/CD with Databricks Asset Bundles, GitHub or Azure DevOps, plus monitoring and alerting for stable operations.
Distributed data lakes and data marts are merged into one central, well-maintained platform.
SAP data flows into the lakehouse in a structured way and is available there for analytics and AI.
Streaming data from IoT, logistics or web shops is processed directly – for example in combination with Kafka and Confluent.
Business units access curated data products via Qlik, Power BI or SQL.
Models and agents work on reliable, authorised data instead of isolated solutions.
Existing Databricks environments are analysed and streamlined in compute, storage and pipelines.
Both platforms are well suited for modern data architectures but have different strengths: Databricks excels in data engineering, streaming and machine learning, while Snowflake shines in SQL-centric analytics and ease of use. We advise you independently of any vendor and select the platform that fits your requirements. A detailed comparison can be found in our article Replacing SAP BW on HANA: Snowflake or Databricks?
GEMA used SQL, Excel and manual reconciliation for its reporting. cimt supported the build-up of a lakehouse platform with Databricks, through which business units now receive their data automatically – in under two hours instead of two to three days.
As a partner of Databricks, Snowflake and Qlik, we recommend the platform that suits you – not the one we want to sell.
More than 25 years of data project experience and Qlik Elite Partner – the basis for clean pipelines into any lakehouse.
As an SAP Gold Partner, we know SAP data models as well as modern lakehouse concepts.
Architecture, data engineering, machine learning and operations – without hand-over losses between providers.
ISO 27001-certified processes and well-designed authorisation concepts for sensitive data.
More than 300 employees in the cimt Group in Germany, the Netherlands and Spain.
A lakehouse combines the low-cost, flexible storage of a data lake with the transaction and governance capabilities of a data warehouse. This means structured and unstructured data can be used in one place for BI, data engineering and AI.
Databricks plays to its strengths in data engineering, streaming and machine learning, Snowflake in SQL-centric analytics. The decision therefore depends on your use cases, your team and your existing landscape. We compare both neutrally for your situation.
Yes. SAP data can be transferred to the lakehouse via suitable connectors and integration tools such as Qlik Talend. There it is available together with other sources for analytics and AI – including as a replacement for SAP BW.
Usually with a no-obligation strategy call and a compact architecture assessment. We then implement a first use case so that you see value quickly, and expand the platform step by step.
Through suitable cluster and serverless configuration, budgets, monitoring and efficient pipelines. In existing environments, we also analyse where compute and storage can be saved.
Not necessarily, but it helps: AI needs reliable, authorised data. Databricks provides a common foundation for data, models and governance. Learn more about our AI consulting.
Are you planning to get started with Databricks, optimise an existing platform or replace SAP BW? We will get back to you promptly for a free initial consultation.
Note: This text was created or revised with the support of AI.
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