Data Integration describes all measures, tools or processes that are necessary to transfer data from source systems into a target system (often data warehouse or data lake).
This usually includes options for connecting to the source system ("connectivity"), different speeds (batch vs. real-time) and logic for transforming the data or bringing it into a uniform schema.
The success of an analytical project is based on an appropriate architecture. After the design of a data model, data integration is the most costly building block in the realization. All analytics is based on it, research shows that 70-80% of the effort goes into the design and implementation of data integration.
Of course, we support each customer individually in his or her requirements: holistically in the context of a data warehouse / data lake project, in the development of an analytical infrastructure including tool selection or concrete implementation of data integration processes. Especially in the context of a larger project, it has proven to be useful to understand the exact requirements in workshops in order to contribute our experience in a targeted way to a solution, to propose an architecture, to implement it and to further develop it together with our customers.
Are you looking for technical support on Data Integration, ETL and Data Virtualization? We work with technologies and commercial solutions from the following vendors:
IBM "Information Server", InfoSphere" and "DataStage" are technologies that have been used for data integration projects for decades. The solutions are comprehensive, mature and highly integrated with each other. Functions for data integration, data governance, data quality and also a data catalog complete the offering, and they are not limited exclusively to data warehouses, which have also been implemented in IBM technology (Db2, NPS and others). In the recent past, IBM has focused mainly on the integration of tools in the AI platform "Cloud Pak for Data", i.e. all technology modernized, containerized and even more tightly integrated. In particular, the functions have been extended by modern approaches, e.g. AI for quality detection or data virtualization.
Learn moreMicrosoft clearly divides the data integration functions into "on-premise" or as a Cloud service within Azure. Within the MS SQL Server, the "Integration Services (SSIS)" have been used for many years, which are completely integrated into the SQL Server and the well-known development interface from Microsoft. With the creation of numerous functions for data integration in Azure, Microsoft has largely fulfilled the demand for a complete solution in the Cloud: the "Azure Data Factory" has comprehensive possibilities for data integration, sensibly in an Azure-centric architecture and embedded in the integrated analytics workbench "Azure Synapse Analytics".
Learn moreTalend has developed into a comprehensive provider of a data integration platform for Big Data in recent years. While Talend was known to most customers a few years ago as an open source variant for simple to moderately complex data integration tasks, today all aspects of modern data integration are served by Talend. In particular, Talend is known for its extensive connectivity to data sources and targets, making it an excellent platform for enterprise application integration (EAI). Tight integration with proprietary data governance tools, such as the Data Catalog, complements the platform.
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