Trivadis is a consulting company and service provider focusing on the DACH region plus Denmark. It was founded in the late 1990s and has more than 600 employees. To support its customer engagements Trivadis started to develop what is now biGENiUS in 2005, consolidating its various efforts in the data warehousing automation space into a consolidated product by 2011. It has now (2018) formally productised biGENiUS, which is operating as a dedicated product division. Unlike its parent company biGENiUS covers not just the DACH region but the Nordics and France and it has inherited a significant number of clients (more than 60 with over 100 projects) from Trivadis. It plans to expand into other markets in due course. As we shall discuss, biGENiUS is agnostic in terms of the data warehousing environments it supports but it is a certified Microsoft Azure Data Warehouse Automation partner.
biGENiUS provides an environment for automating the creation of various types of analytic repositories and related functions. It is limiting to describe it as a “data warehouse automation” tool, which it is, because it can also be used in the same capacity for data lakes, data marts and, potentially, other architectures. The reason it is able to do this is because it is not just model-driven but also based on design patterns. And one logical model can drive multiple design patterns. The flexibility that this provides means that biGENiUS can support any sort of warehousing architecture, including the increasingly popular data vault. However, it can also be used for other environments that are related to analytics but are not specifically about data warehouses, marts or lakes. For example, one user has employed biGENiUS to migrate from Apache Kafka to Streamsets. For this reason the company refers to its platform as providing “analytic data management automation”.
biGENiUS runs on Windows, Windows Server and Microsoft Azure platforms though it is agnostic as to the warehousing and other environments it targets. The developer environment runs on a Windows desktop. We would like to see the company offer a browser-based interface. Both perpetual license and subscription-based pricing is offered.
Customer Quotes
“I am convinced that the tool reduces the development cost of a data warehouse by at least 50%.” Austria Tabak
“At Smurfit Kappa, we have been using biGENiUS to improve and maintain our reporting system that we have developed significantly over the last ten years. Reduction in development time, simplified loading experience, ease of disparate source addition and traceability are some of the key benefits that we gained from using a data warehouse automation tool.” Smurfit Kappa
The basic principles of biGENiUS are illustrated in Figure 1: you create a business model that maps to a design pattern, which generates what you need for the target environment.
The business model can be created in various ways, being data, model or reference model-driven. You can import graphical models from third party environments though the company has no specific partnerships in this area, as yet.
These models are mapped to design patterns and these contain configuration details about the target environment you are creating or updating, as well as best practices built into the architecture component of the design pattern. In this context, it is worth noting that the deployment objects that are generated can be conformed to comply with regulations such as GDPR. Out of the box the company provides design patterns for Microsoft environments, Oracle, Hadoop, Spark, Kafka and StreamSets but it is relatively easy (the company claims three to five days) to build a new design pattern. We would like to see the company provide an SDK (software development kit) to allow users build their own design patterns, though you can configure existing design patterns, and there are facilities to make template development easier.
Figure 2 – Object Explorer
In so far as automation is concerned, the software will generate all the necessary scripts for loading the data into a staging area, for cleansing and for populating the eventual target. These scripts, which will generate tables, (aggregated) views, load functions and so on, all run on the target (using, in a relational context, the relevant version of SQL and Spark SQL on Hadoop) so that there is no vendor lock-in. Full documentation is generated complete with data lineage and cross-references to data warehouse objects from which you can drill down into individual entities. On an ongoing basis, the product supports slowly changing dimensions and there is an Object Explorer, illustrated in Figure 2, that allows you to discover source metadata and supports version comparisons in the event of a change. As shown, it also allows exploration of projects as well as your data models. Data flow is shown i
Creating data warehouses, marts, lakes and so forth is a time consuming and costly business. Data warehouse automation significantly reduces the time and effort involved in this process. This is true, to a greater or lesser extent, of all products in this space. What biGENiUS adds to the mix is its flexibility: it not only supports SQL-based data warehouses and data marts but also data lakes and streaming environments. Moreover, you can configure the generator to generate only what you need and no more. The product is wizard and template-driven so that it is easy to use. We also like the fact that a single business model can drive multiple design patterns so that you can generate multiple data marts (say) from a single logical model.
The Bottom Line
Unlike many other data warehouse automation tools, biGENiUS was designed to support all sorts of environments and not just warehouses and marts, which is why the company refers to it as providing analytic data management automation. It is the flexibility, which goes throughout the product, that is its greatest strength.
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