by Jerome Kehrli
Posted on Monday Dec 13, 2021 at 12:04PM in Big Data
For forty years we have been building Information Systems in corporations in the same way, with the same architecture, with very little innovations and changes in paradigms:
- On one side the Operational Information System which sustains day-to-day operations and business activities. On the Operational Information Systems, the 3-tiers architecture and the relational database model (RDBMS - Relational Database Management System / SQL) have ruled for nearly 40 years.
- On the other side the Decision Support Information System - or Business Intelligence or Analytical Information System - where the Data Warehouse architecture pattern has ruled for 30 years.
Of course the technologies involved in building these systems have evolved in all these decades, in the 80s COBOL on IBM hosts used to rule the Information Systems world whereas Java emerged quickly as a standard in the 2000s, etc.
But while the technologies used in building these information systems evolved fast, their architecture in the other hand, the way we design and build them, didn't change at all. The relational model ruled for 40 years along the 3-tiers model in the Operational world and in the analytical world, the Data Warehouse pattern was the only way to go for decades.
The relational model is interesting and has been helpful for many decades. its fundamental objective is to optimize storage space by ensuring an entity is stored only once (3rd normal form / normalization). It comes from a time when storage was very expensive.
But then, by imposing normalization and ACID transactions, it prevents horizontal scalability by design. An Oracle database for instance is designed to run on a single machine, it simply can't implement relational references and ACID transactions on a cluster of nodes.
Today storage is everything but expensive but Information Systems still have to deal with RDBMS limitations mostly because ... that's the only way we used to know.
On the Decision Support Information System (BI / Analytical System), the situation is even worst. in Data warehouses, data is pushed along the way and transformed, one step at a time, first in a staging database, then in the Data Warehouse Database and finally in Data Marts, highly specialized towards specific use cases.
For a long time we didn't have much of a choice since implementing such analytics in a pull way (data lake pattern) was impossible, we simply didn't have the proper technology. The only way to support high volumes of data was to push daily increments through these complex transformation steps every night, when the workload on the system is lower.
The problem with this push approach is that it's utmost inflexible. One can't change his mind along the way and quickly come up with a new type of data. Working with daily increments would require waiting 6 months to have a 6 months history. Not to mention that the whole process is amazingly costly to develop, maintain and operate.
So for a long time, RDBMSes and Data Warehouses were all we had.
It took the Internet revolution and the web giants facing limits of these traditional architectures for finally something different to be considered. The Big Data revolution has been the cornerstone of all the evolutions in Information System architecture we have been witnessing over the last 15 years.
The latest evolution in this software architecture evolution (or revolution) would be micro-services, where finally all the benefits that were originally really fit to the analytical information system evolution finally end up overflowing to the operational information system.
Where Big Data was originally a lot about scaling the computing along with the data topology - bringing the code to where the data is (data tier revolution) - we're today scaling everything, from individual components requiring heavy processing to message queues, etc.
In this article, I would want to present and discuss how Information System architectures evolved from the universal 3 tiers (operational) / Data Warehouse (analytical) approach to the Micro-services architecture, covering Hadoop, NoSQL, Data Lakes, Lambda architecture, etc. and introducing all the fundamental concepts along the way.Read More