What are the benefits and drawbacks of using a shared data layer across different layers?

1 Data management benefits

One of the main benefits of using a shared data layer is that it can reduce data duplication, inconsistency, and complexity. By centralizing data access and storage in one layer, you can avoid having multiple copies of the same data in different layers, which can lead to data quality problems and synchronization overhead. You can also enforce data integrity and consistency rules in the shared data layer, and abstract the details of data sources and formats from the other layers. This can make data management easier and more efficient, as well as facilitate data integration and migration.

2 Performance benefits

Another benefit of using a shared data layer is that it can improve performance and resource utilization. By having a single point of data access and storage, you can optimize data queries, caching, and indexing strategies, and leverage the capabilities of the underlying data systems. You can also reduce network traffic and latency by minimizing the data transfers between layers, and use batch processing and streaming techniques to handle large volumes of data. Additionally, you can take advantage of data compression, partitioning, and replication to enhance data availability and reliability.

3 Data sharing benefits

A third benefit of using a shared data layer is that it can enable cross-layer data sharing and collaboration. By exposing a common data interface and schema to the other layers, you can allow different layers to access and manipulate the same data, without requiring complex data transformations or mappings. This can support various business scenarios, such as reporting, analytics, decision making, and workflow orchestration. You can also use event-driven mechanisms, such as pub/sub or message queues, to notify and trigger actions in different layers based on data changes.

4 Coupling drawbacks

One of the main drawbacks of using a shared data layer is that it can increase coupling and dependency between layers. By sharing the same data layer, different layers become more tightly bound to the data interface, schema, and logic, which can reduce their modularity, flexibility, and autonomy. Any changes in the shared data layer can affect multiple layers, and require coordination and testing across them. Moreover, different layers may have different data requirements and expectations, which can create conflicts and inconsistencies in the shared data layer.

5 Security drawbacks

Another drawback of using a shared data layer is that it can introduce security risks and challenges. By having a single point of data access and storage, you expose a large surface area for potential attacks and breaches, and increase the impact of any data loss or corruption. You also need to implement and enforce strict data security policies and controls, such as authentication, authorization, encryption, auditing, and backup, across the shared data layer and the other layers. Furthermore, you need to comply with various data privacy and regulatory standards, such as GDPR, HIPAA, or PCI DSS, which can limit your data sharing and processing options.

6 Scalability drawbacks

A third drawback of using a shared data layer is that it can hamper scalability and performance. By relying on a single data layer, you create a bottleneck and a single point of failure for your application, which can affect its availability and responsiveness. You also need to handle the increasing data volume, variety, and velocity, which can strain your data systems and resources. Additionally, you need to balance the trade-offs between consistency, availability, and partition tolerance, which can affect your data quality and reliability.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?