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Modernizing your analytics architecture part 3: Continuous right-sizing

Bilde av Terje Vatle
by Terje Vatle

12. Oct 2021, 5 minutes reading time

Modernizing your analytics architecture part 3: Continuous right-sizing

In my previous articles, we looked at key objectives when modernizing your analytics architecture and the different approaches to sizing your architecture for the future. 

In this article, we will examine what a continuous right-sizing approach would look like. Right-sizing implies that you first start out designing your analytics architecture for optimal price-performance based on current requirements. Continuous implies that as business needs change and the price-performance is no longer optimal, you can swap or add/remove corresponding storage and processing components as needed.  

Typical reasons for swapping or adding new storage and processing components in your analytics architecture may be:  

  • Price-performance is no longer optimal, so you need to scale up from a single database to an MPP database 
  • Your business needs change. A different technology may be better suited, such as adding a data lake or moving data that needs stricter governance from a data lake to a database 
  • You would like to try out or leverage new emerging technologies that might be a better match for your needs 

Swapping, adding, or removing components is not flexible or even doable if you would have to rewrite code. Therefore, the storage and processing environment should be fully abstracted away from the code and maintained as configuration. In theory, you may change your connectors to point to a different storage in your cloud native ELT tool, but there can be substantial differences that require extensive recoding. Examples can be different ways of handling indices (or lack thereof), different storage formats, or the need for different data loading procedures.   

If you start out with any of the minimum approaches you should be able to up-size piece by piece towards any of the maximum approaches later. Or you may even down-size parts of your architecture if that yields a better price-performance at a given point in time without impacting the speedy delivery of trusted data.  


Xpert BI enables you to right-size your architecture at any time 

When you design your layered analytics architecture using Xpert BI, you decide where to store your data and where to run your data platform solution. Xpert BI abstracts between code written/generated and the underlying storage technology, so you later can swap storage without rewriting any code.  

Xpert BI provides the ability to change data storage or processing to choose the one that best fits your current business needs. For example, if an MPP database proves too expensive you can downsize to a single database, or up-size as your analytics ambitions as data volumes grow. 


How does Xpert BI work?

Xpert BI is a data automation tool using metadata to create and operate flexible and robust data platforms with minimum risks and resources. The tool enforces automatic documentation, just the right level of standardization, data governance support, and methodology improvements over off-the-shelf ETL- and ELT products. In this way, you can shift time from repetitive and mundane data wrangling tasks to more value-adding tasks. This includes analytics, machine learning, data ecosystems, and user adoption. 


Read more about data warehouse automation in our online guide.

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Terje Vatle

Terje Vatle

Terje Vatle is Chief Technology Officer at BI Builders following global market trends within data & analytics. Terje has a technology and advisory background, and focuses on how to make organizations achieve their goals by becoming more data driven.

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