Organizations continue to struggle with data management and often lag behind as data requirements grow faster than their ability to deliver. To stay competitive, many are making a conscious choice to implement modern big data methodology and architecture. The approach taking the lead is Data Fabric, which combines several core needs of every organization, including centralized data management, self-service for all, and governed yet easily shared data. 

While Data Mesh may appeal to organizations with strong business units and their own technologists, Data Fabric builds on the best parts of data warehouses, lakes, and lakehouses. For more information on the differences between Data Mesh and Data Fabric, check out the article, Data Mesh vs. Data Fabric Choosing a Path Towards Modern Data Management. 

The centralized nature of Data Fabric makes it a better choice for the average non-technical organization’s capabilities. The pursuit of more data sharing, stronger catalogs, glossaries, and master data is supported by various data models designed based on the needs of the consumer. 

Data Zones for Consumers 

An important feature of Modern Data Architectures for Analytics is the concept of data zones—areas of data storage that satisfy diverse types of data consumption. These zones play a critical role in centralized and governed data management, ensuring that data is organized, accessible, and secure. Let’s explore the key use cases for different data zones: 

The Three Zones Everyone Needs 

1. Raw Data Zone: This is where all incoming data is stored in its raw, unprocessed form. It serves as the foundation for all subsequent data processing and analysis. The raw data zone is crucial for data integrity and flexibility. It preserves the original data so that it can be reprocessed if needed. During data pipeline development, the raw zone allows data engineers to better understand the source data and gives them something to validate logic against. The raw zone also allows data scientists and analysts to access the full, unaltered dataset for distinct types of exploratory analyses. 
2. Refined Data Zone: Here, data is cleaned, transformed, and enriched. Materializing the refined layer allows for full transparency of data transformation and is the foundation for Master Data Management. Standardization in the refined zone ensures that data conforms to specific formats and standards, making it easier to analyze downstream. The refined zone also contributes to efficiency. Processing data once and storing it for multiple uses reduces the computational load and speeds up analysis. 
3. Analytics Data Zone: This zone is optimized for reporting, visualization, and advanced analytics. It includes pre-aggregates to support fast querying and reporting and optimized structures tailored to specific business questions. Remember all the reasons listed in The Case for Golden Datasets? The analytics layer is the perfect place to introduce a golden dataset for reporting purposes.

Figure 1. The three data zones everyone needs

Zones for Governance and Quality Control 

The Governance Zone is dedicated to managing metadata, data catalogs, and data lineage. Utilizing a data governance zone can ensure data usage complies with regulatory requirements and internal policies. Quality Control can also be issued with a zone-based data architecture, providing a structure for data quality monitoring and maintenance, with tools for data profiling, validation, and cleansing. 

Zones for Data Sharing and Exchange 

With the growing emphasis on data literacy across roles and responsibilities, the Exchange Zone becomes crucial for facilitating secure data sharing and collaboration across departments and external partners. This zone enables seamless data exchange between internal teams and external entities while ensuring that shared data adheres to privacy and security standards, prioritizing collaboration, and security. 

Integration Zone for Real-Time Data Access 

The Integration Zone handles data integration from various sources, including real-time data streams, supporting swift processing, and enabling real-time analytics and decision-making. Access to real-time data allows organizations to respond quickly to market changes, adapt to customer needs, and act on emerging opportunities, overall maintaining a competitive edge. 

Sandboxes and Discovery Zones 

The Sandbox Zone offers a secure environment for experimentation and discovery, enabling data scientists and analysts to test new models, algorithms, and analyses without impacting production data. This zone fosters innovation by allowing the exploration of new data sources and hypotheses in a controlled setting, driving advancements and breakthroughs in data analysis and insights. 

Figure 2. Data zones including governance, integration, and discovery 

Data Zone Implementation 

Designing a Modern Data Architecture suited for your needs is dependent on your business objectives. If you have any questions on implementing the right architecture to meet the unique needs of your organization, reach out to our team at Datalere.