Data warehouse modernization
Collecting datasets in an adequate quality – ideally in real time – and then evaluating and analyzing them are the functions performed by modern data warehouse (DWH) solutions. However, data warehouses that were set up five, ten or more years ago are not equipped for the digital transformation. They are limited in function, cumbersome and difficult to administer. They are also becoming increasingly expensive to maintain and unable to meet present-day requirements of agility, speed and flexibility. Integration in operational processes, short latency, access to external sources, self-service business intelligence are just a few of the criteria that a modern DWH has to meet.
Various approaches to modernization
The most important goal in the modernization of a data warehouse is to make the architecture fundamentally viable for the future. In fact, due to the high complexity there is not one ideal solution, but several approaches to bringing data warehouses up to date. In order to be able to assess objectively whether the existing system is really completely obsolete and whether investment in a new system is necessary, decision-makers should first define clear requirements for analytical data management solutions. Only then can the right approach be determined.
It's just like in real life. The data warehouse has to deliver. But unlike a restaurant, it has to deliver immediately without any wait times.
The Trivadis approach uses big data technologies and extended DWH concepts that complement each other
It is becoming increasingly difficult to implement new technical and organizational requirements with standard data warehouse solutions. A responsible transition to a more agile and flexible DWH is often a matter of supplementing, extending and enriching existing systems and architectures rather than replacing them. The key to success is to enhance the data warehouse’s traditional functions with the capabilities to meet new requirements, such as larger volumes of data, shorter latency or complex data structures, while taking new technologies into account.
We are therefore approaching a viable solution from both sides: The data warehouse opens up to new needs and technologies, and the big data world adapts tried and tested methods to the orderly domain of data warehousing. The tried and tested means for this is called: Abstraction through more SQL and more generation, agility through more self-service and faster development cycles as well as performance through in-memory, better scalability and shorter latencies.
Overall, a modern data warehouse should fulfill the following core functions:
- Agile technology and processes to support new business requirements.
- Flexibility to simplify adaption capability.
- Support of self-service functions in the company.
- Enabling right-time processes (near-real-time) for more up-to-date data compared to rich batch processes.
- Scalability to support more data, new sources and additional use cases.
- Simplified modelling, solution development and quality assurance.
Meeting three challenges for digital transformation and big data
A modernized data warehouse can effectively meet the three major challenges of digitization in connection with Big Data: the efficient handling of large amounts of data, the rapid processing of data streams and the mastery of complex analyses. This is the only way for companies to extract information from data and make it usable for business success.