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January 2 - January 23, 2023
According to this model, effective modules are deep: a simple public interface encapsulates complex logic. Ineffective modules are shallow: a shallow module’s public interface encapsulates much less complexity than a deep module.
the relationship between microservices and bounded contexts is not symmetric. Although microservices are bounded contexts, not every bounded context is a microservice. Bounded contexts, on the other hand, denote the boundaries of the largest valid monolith.
An event is a message, but a message is not necessarily an event. There are two types of messages: Event A message describing a change that has already happened Command A message describing an operation that has to be carried out
As Andrew Grove put it, only the paranoid survive.3 Use this as a guiding principle when designing event-driven systems: The network is going to be slow. Servers will fail at the most inconvenient moment. Events will arrive out of order. Events will be duplicated.
Operational systems implement real-time transactions that manipulate the system’s data and orchestrate its day-to-day interactions with its environment. These models are the online transactional processing (OLTP) data. Another type of data that deserves attention and proper modeling is online analytical processing (OLAP) data.
The reason for the high normalization of the dimensions is the analytical system’s need to support flexible querying. That’s another difference between operational and analytical models. It’s possible to predict how an operational model will be queried to support the business requirements. The querying patterns of the analytical models are not predictable. The data analysts need flexible ways of looking at the data, and it’s hard to predict what queries will be executed in the future.
The data warehouse (DWH) architecture is relatively straightforward. Extract data from all of the enterprise’s operational systems, transform the source data into an analytical model, and load the resultant data into a data analysis–oriented database. This database is the data warehouse.
A data lake–based system ingests the operational systems’ data. However, instead of being transformed right away into an analytical model, the data is persisted in its raw form, that is, in the original operational model.
The data mesh architecture is based on four core principles: decompose data around domains, data as a product, enable autonomy, and build an ecosystem. Let’s discuss each principle in detail.
There is no sense in talking about the solution before we agree on the problem, and no sense talking about the implementation steps before we agree on the solution. Efrat Goldratt-Ashlag
