Data Lakehouse: Hierarchy and Applications of Bronze, Silver, Gold

Recent advancements in data management are significantly influencing companies' decision-making processes. In this context, the data lakehouse architecture stands out as a compelling model.

By 2025, data lakehouses are expected to be among the most preferred structures in data management. This architecture combines the best features of data lakes and data warehouses, addressing both big data analytics and reporting needs. Let's explore the intriguing Bronze, Silver, and Gold levels of this structure together!

What is a Data Lakehouse and How is its Hierarchy Structured?

The data lakehouse is regarded as a combination of data lake and data warehouse architectures. This structure ensures that data is stored in a central location while also accelerating analytical processes. The Bronze, Silver, and Gold levels indicate how data is organized in terms of maturity and usability. These are critical for making better decisions in data management.

For instance, I had the opportunity to use a data lakehouse in a project last year, and it significantly improved the efficiency of data access and analysis. So, how does this process work? Let's take a closer look!

Data Lakehouse Hierarchy

• Bronze Level: At this stage, raw data is collected. No processing or cleaning is performed on the data. For example, data collected from IoT devices is stored here.
• Silver Level: Raw data is processed to a certain extent, making it more suitable for analysis. Data cleaning, transformation, and normalization tasks are carried out at this stage.
• Gold Level: This is the highest level of maturity. Here, data is fully optimized for reporting and analytical processes, providing high-quality, reliable, and analyzable data.

Performance and Comparison

The performance of data lakehouses varies based on the infrastructure used and the data management processes in place. For instance, according to research conducted by 2025, data lakehouses can offer query times that are 30% faster compared to traditional data warehouses. Moreover, the efficiency gained while processing large data sets helps companies save time and costs.

At this point, there are some significant differences between data lakehouses and traditional data warehouses. Recently, when I conducted a comparison, I noticed that factors like data processing and analysis times, as well as data diversity and accessibility, also play a crucial role. So, which elements stand out in this comparison?

Advantages

• Data Diversity: Data from various sources can be easily integrated. This allows companies to have a richer data pool.
• Flexibility: The ability to work with both structured and unstructured data makes business processes more dynamic.

Disadvantages

• Complexity: The data lakehouse architecture can become complex in some instances. Managing large-scale data sets may pose challenges.

"The blurring of boundaries between data lakes and data warehouses marks the beginning of a new era in data management." - Data Scientist Ahmet Yılmaz

Practical Use and Recommendations

Data lakehouses find broad practical applications across various industries. For example, e-commerce companies can analyze customer data to create personalized marketing strategies. Simultaneously, firms in the finance sector can utilize this structure for risk analysis, enabling more robust forecasts. Based on my experiences, the most significant advantage of using a data lakehouse is its ability to simplify and expedite data management processes.

Additionally, there are critical points to consider regarding data security and privacy. Proper processing and protection of data are essential to fully benefit from the advantages this structure offers. I’m curious about your experiences with the use of data lakehouses in your industry. What innovations do you think this structure could bring to data management?

Conclusion

The data lakehouse architecture emerges as a significant step in data management. The organization and presentation of data through the Bronze, Silver, and Gold hierarchy strengthens companies' analytical capabilities. Ultimately, this structure provides a considerable advantage in data-driven decision-making processes.

What do you think about this? Share your thoughts in the comments!