Which Modern Data Architecture Should You Choose?

When it comes to data management, there are as many opinions on what is right and what is wrong as there are data managers. Whenever you start wondering what type of data architecture you should choose, there aren’t any good answers or the right principles to follow.  However, you should be aware that modern data architecture can be in many forms. In this article, we will discuss some of the best data architectures and discuss their trade-offs. 

Data architecture is the blueprint that guides all activities across your company’s data systems. A data architect has many roles, which include but are not limited to collecting business requirements of all business processes in the company, devising a data strategy that reflects how to measure the business strategy, and defining conceptual and implementational data lifecycles to support data strategy. With that said, a data architect can run decisions on such things as the choice of data storage, whether the data system will process real-time data or batch it at data ingestion, etc. 

When it comes to choosing the best data architecture patterns, the choice will be reduced to three main paradigms:

  • ETL – extracting data from Facebook Ads API; transforming the data to aggregate impressions, clicks, and ads spent; loading the cleaned data into MySQL relational database. It’s the most common architectural paradigm that is fast to set up. There are many data platforms and tools that help with automation. Along with several benefits, there are several challenges of ETL that you may face.  It includes hard maintenance. It also answers only part of business requirements. 
  • ELT is the data architecture that follows the same steps as ETL, but ties to correct for its disadvantages. ELT extracts all data and loads it into data storage. The use of cloud technologies makes the load super-efficient. It’s the preferred choice of data analysts and business intelligence because of its data sanitation and quality assurance. 
  • Data Mesh recognizes that a single centralized solution might not be the best for every department. Data mesh is consumer-centric. Each stakeholder is responsible for building their own ETL/ELT pipelines with the tools and technologies serving their use cases best. 


If you are looking for a quick-and-dirty MVP, ETL should be the best choice for you. ELT should be the ultimate pick for building a data operation to last and expect standard questions. Data mesh is the best choice for those working with different teams at different speeds. 

Whenever looking for the best modern data architecture paradigm, reliability and resistance to data loss should be always taken into consideration. Relying on the help of high-tech data recovery facilities lets you rest assured that all your data is kept safe and sound.