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Experts in digital innovation
experts in sap

Digital Innovation

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SAP Solutions empowered
by cloud analytics technologies

Digital technologies enable enterprises to discover new ways to create value, increase revenue, and enhance efficiency.

There is a significant opportunity to expand SAP functionality with public cloud technologies (such as artificial intelligence and Internet of Things) to unleash innovation, produce new customer experiences, create new business models, or redesign operating processes. To make this possible, you need to integrate your SAP data bi-directionally into a Cloud Data Lake.

At Novis, we can help you build and operate your data pipelines to move your data into the cloud and process it to gain insights and guide better business decisions.

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  • Big data / Analytics
  • Machine learning
  • Internet of Things

Data Lake:
for modern

A data lake is a centralized repository that stores all your data, both in its original form and prepared for analysis.

Building a cloud data lake enables your organization to analyze your data and create digital applications efficiently.

We can help your business to integrate diverse data sources into the cloud analytics environment.

Data Lake
Business Aplications
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SAP Data Integration:
taking advantage of
SAP native data sources.

We have developed tools, processes, and methodologies to ingest SAP data into your Cloud Data Lake, accelerating, and reducing risks to your projects.

Our solution can connect to all native SAP data sources, such as operational data provisioning (ODP), delta queues, business APIs (BAPI), ABAP reports, or accessing SAP tables directly.

  • Delta queue
  • odp
  • Bapi
  • table
  • query
  • report
  • bw cube
  • bw hierarchy
  • open hub services
Data lake

Data lifecycle
managed service

We offer a managed service to support the full lifecycle of your data, including data lake ingestion and data processing in the cloud.

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  • Analyze data sources and required transformations.
  • Cloud infrastructure and services architecture.
  • Data model (raw and ready to analyze) and data pipeline.
  • Data quality and security requirements.
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  • Implement cloud infrastructure and services.
  • Configure data sources and connectors.
  • Develop data pipeline.
  • Configure process scheduling and monitoring solution.
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  • Cloud infrastructure and services administration.
  • Data pipeline jobs monitoring and troubleshooting.
  • Data pipeline support and maintenance.
  • IT service management.