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Overview

Overview Data management solutions such as data fabric architecture or data warehouses are an important piece of the puzzle on the path to digital transformation. They offer the opportunity to build an integration platform for fast and decentralized data exchange and to expand the ability to accelerate data-based decision-making processes.

The need for such approaches arises from the strategic development of your company: a high degree of automation and a platform for data exchange and analysis are required to make progress with the digital transformation. These complex architectural approaches are based on common patterns and best practices. So why not reuse these patterns and the underlying technology and create a toolbox for data management architecture? Do you want to take full advantage of cloud technology and take your data management to a new level of integration? We will guide you through the process, familiarize you with the relevant tools and support you during implementation.

Enabling and coaching with a proven toolset

Our goal is to build a modern data analytics platform for you. We enable our customers to build their own data warehouse with their own requirements and according to their own criteria. We use the ETL framework from Talend and Snowflake in the AWS cloud. The valuable working time of your IT colleagues can thus be invested in the business logic for retrieving and analyzing information, not in setting up a software infrastructure. Benefit from our proven integration platform as a service (iPaaS), a scalable cloud database system and our framework implementation so you can focus on realizing your business use cases.

Our flagship solution consists of proven tools and the expertise of our employees, which can be supplemented or integrated as required:

  • Talend Cloud as a base toolset for building a distributed integration platform according to your needs

  • Snowflake Cloud Data Warehouse as the basis for implementing the analysis architecture according to your needs

  • Data Vault as an agile modelling approach for your specific business decision backbone

  • Our basic implementation framework for comprehensive data ingestion, metadata management and application performance monitoring

  • AWS as a reliable and scalable cloud platform  

The Perfect Bundle

The optimal mix of BI consulting, cloud services and DI tooling to successfully implement your migration requirements.

cimt
Partnership-based BI consulting company that supports you with many years of in-depth experience in all aspects of your data warehouse projects. cimt Data Vault Framework: a "lightweight" and generic approach to orchestrating the Data Vault model.

AWS
Selected cloud services to provide the necessary and optimal database, runtime environment and much more.

Talend Sophisticated software specialized in data integration, data quality and data trust.

Our implementation approach

Are you already using cloud technology in your company and would now like to fully utilize its strengths and take your data management to a new level of integration? We will guide you through the process, familiarize you with the relevant tools and coach you through the implementation!

In addition, data modelling with Data Vault offers decisive advantages in terms of scalability and flexibility when implementing your analytical processes.

We will be happy to work with you to develop the right architectural approach, model the business logic for your data warehouse and train you to work independently with Talend, Data Vault and Snowflake.

The starting point is a joint analysis meeting, after which we develop the concept for you from a best-of-breed approach, to prove the planning and implementation in an iterative process by means of a proof of concept.

The necessary planning steps for the AWS cloud infrastructure and security aspects are of course part of the process.

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Fulfillment method Professional Services

Pricing Information

This service is priced based on the scope of your request. Please contact seller for pricing details.

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