Bring enterprise data into Snowflake.
Structure it for analytics and AI.
We migrate SAP and other enterprise data, define the models and controls needed for use, and manage cost, performance, security, pipelines, and change after deployment.
Four services cover data foundation design, SAP data migration, pipeline engineering, and ongoing operations
Start with the work required now, whether that means establishing the data foundation, moving SAP data, engineering production pipelines, or managing the Snowflake environment after deployment.
AI-Ready Data Foundation
Use business documents, reports, SQL, and user questions to design an Enterprise Data Model and Semantic Views, then have domain experts review them and test them against real business questions.
View the service →SAP to Snowflake Migration
Assess SAP ECC, S/4HANA, and BW data, history, interfaces, and batch processes, then define the scope, migrate the data, validate the result, and support stabilization.
View the service →Data Engineering & Integration
Integrate ERP, MES, SCM, CRM, and external sources through Batch, CDC, or Streaming pipelines designed around timing, history, consistency, and recovery requirements.
View the service →FinOps & Managed Services
Manage Snowflake cost, performance, access, security, pipelines, incidents, and changes within the operating scope agreed with the client.
View the service →Design the data model around how the business uses the data
We analyze business documents, reports, SQL, and questions from users to identify business objects, relationships, metrics, and calculation rules. Domain experts review the results before they are incorporated into the Enterprise Data Model and Semantic Views.
The model is tested against real business questions and verified results before it is used as the foundation for analytics and AI.
Discuss an AI-Ready Data Foundation →Move SAP data and history with defined validation criteria
We examine SAP ECC, S/4HANA, and BW data and history, together with interfaces and batch operations. We define the migration scope and validation criteria, migrate and validate the data in Snowflake, and support stabilization after the migration.
Discuss SAP Data Migration →Design pipelines around when and how the data is used
We consider when the business needs the data, source-system load, history, recovery, and consistency requirements before choosing Batch, CDC, or Streaming patterns.
AI can help draft mappings and test cases from business documents, SQL, and interface specifications. Engineers validate them against actual data and execution results.
Discuss Data Engineering →Manage cost, performance, security, and data operations after launch
As usage and business needs change, we review cost and performance, access controls, and data pipelines. We track anomalies and improvement opportunities, respond to incidents and technical questions, and manage changes within the agreed service scope.
Discuss FinOps & Managed Services →Design data structures around their intended use
Our experience in BI and data warehousing, Data Governance, SAP ERP, and enterprise systems means the work does not stop at selecting where the data will reside.
We identify who will use the data and for which workflows and analyses, then define the data model, Semantic Layer, quality controls, and operating standards required.
Start with your current data environment and immediate priority
Tell us whether you are considering an SAP data migration, existing data warehouse modernization, a new Snowflake foundation, pipeline engineering, or ongoing operations. We will help identify the first scope to examine.
Discuss a Snowflake Initiative →