Data Warehouse and Data Management Strategies
The Continuous Development of Data Warehouses
In data management the past 10 years have been characterized by paradigm shifts, a multiplication of the amount of data to be managed and the evolution of generalization, automation, and virtualization, as in many other IT areas. Big data, data vaults, and cloud offerings for BI solutions have enabled new courses of action and architectural approaches. This has also transformed the requirements for and the nature of collaboration between IT specialists and business departments. At the same time, the complexity of data management solutions has increased due to the multitude of sources and data formats, as well as the high demands on data integrity and timeliness.
cimt is a project partner with many years of enterprise experience in a wide variety of industries. As an expert in big data solutions, data volumes ranging from terabytes to petabytes are part of our daily business. Open source technologies, such as Talend, offer maximum efficiency and our proven cimt job framework, a robust data warehouse architecture, provides the basis for this.
Requirements for a Modern Data Management Architecture
Data management architectures are constantly evolving – and yet the current development stage is not necessarily the right one for your process landscape. Based on your requirements, we determine the right approach and procedure for a modern data management architecture.
Whether it is the planning of a data warehouse with Data Vault, the combination of data lake and data hub for a smooth distribution of data, integration of Kappa or Lambda architecture or hybrid solutions in the combination of cloud and on-premise technologies – we will work with you to develop the right solution approach and will also be happy to implement it in collaboration with you.
Scalability, reusability of solutions, agility in implementation and the pursuit of a best-of-breed approach are a matter of course for us.
This is How We Work
- Conducting workshops to develop architectural approaches, proof-of-concept implementations, and ETL infrastructures
- Best-practice patterns for data vault management: Management framework or job framework (cimt job framework)
- Continuous development of frameworks and data pipelines for on-premises/cloud deployments
- Project management using agile methods such as Scrum
- Extensive project experience with solutions based on Talend, Python and Java
- Adaptation of Talend components to in-house special cases (Teradata)
- Use of Big Data components (Spark, Hive etc.) in conjunction with classic DWH approaches
- Implementation, test and QA of ETL jobs
Our Services at a Glance
- Architecture design and platform decision for data management and data warehouse
- Derivation of organizational measures for data mesh, data lake and data governance
- Selection of suitable tools
- Data modeling and building data warehouse with Data Vault or classic methods
- Implementation of management, interfaces and business logic
Would You Like To Know More About Data Warehouses?
We would be happy to discuss your concerns with you in an introductory meeting and show you the best options for further development with Data Warehouses. Arrange an appointment with us right away. We look forward to meeting you!
Five benefits of a successful restructuring of your DWH:
Avoiding cost explosions
through the use of open source-based technologies.
Increasing the speed
and flexible design of data provision.
Fast and uncomplicated connection
of new data sources.
Increase scalability and build a base
for Big Data processing through redesign with robust architecture.
Highly up-to-date data
through the reduction of cycles.