I am contemplating the appropriate architecture and selection of the database.



In essence, the requirement involves accommodating three extensive databases, which collectively accumulate around 3 GB or more in size every year (each), amounting to a total of 10 GB per year for all three databases.
The selection process must solely rely on digital indexes, with no provision for text search. What kind of system will be capable of handling such a heavy workload?

To handle a heavy workload involving three extensive databases accumulating 10 GB per year, you would require a robust and scalable architecture. Here are a few potential options to consider:

1. Relational Database Management System (RDBMS): RDBMS like MySQL, PostgreSQL, or Oracle can handle large volumes of data while providing efficient indexing capabilities. They support digital indexes and offer powerful query optimization features for improved performance.

2. NoSQL Databases: If the data does not require strict consistency and relational integrity, you could explore NoSQL databases such as MongoDB, Cassandra, or DynamoDB. These databases are designed for scalability and can handle high read/write workloads with ease.

3. Distributed Databases: Distributed databases like Apache Hadoop, Apache Cassandra, or Google Bigtable distribute data across multiple servers, enabling horizontal scaling. This approach can handle massive data growth and provide fault tolerance.

4. Cloud-based solutions: Consider leveraging cloud-based database solutions such as Amazon RDS, Azure SQL Database, or Google Cloud Spanner. These services provide managed instances of popular database systems and can automatically scale to meet your needs.

Ultimately, the choice depends on factors like data structure, access patterns, required consistency guarantees, budget, and scalability requirements. It may be beneficial to consult with a database architect or specialist who can assess your specific needs and recommend the best-suited solution for your workload.


Here are a few more considerations to keep in mind while selecting the appropriate architecture and database for your workload:

1. Data Structure: Evaluate the structure of your data to determine if it fits well with a relational model or if it requires a more flexible schema offered by NoSQL databases. If your data is tabular and requires complex relationships, a relational database may be a better fit.

2. Performance Requirements: Consider the performance requirements of your workload, such as response time, throughput, and concurrency. Look for databases that can handle the expected number of concurrent users and provide features like query optimization, caching, and efficient indexing to meet your performance needs.

3. Scalability: Assess the scalability requirements of your workload. Determine if you need the ability to scale horizontally (adding more machines) or vertically (increasing resources on existing machines). Distributed databases or cloud-based solutions are often better suited for handling scaling requirements.

4. Data Integrity and Consistency: If maintaining strict data integrity and consistency is crucial to your application, relational databases provide strong ACID (Atomicity, Consistency, Isolation, Durability) guarantees. NoSQL databases offer eventual consistency and can handle high write workloads but may have some trade-offs in terms of data consistency.

5. Budget: Consider your budget constraints when selecting a database solution. Cloud-based solutions might incur ongoing costs, whereas open-source options like MySQL or PostgreSQL can be more cost-effective in terms of licensing and maintenance.

6. Future Growth: Evaluate how your data is expected to grow in the future. Ensure that the selected database can accommodate increasing data volumes without compromising performance or requiring significant architectural changes.

The sampling rate is primarily determined by the database schema and the sampling queries. By effectively segmenting and separating the data, it is possible to greatly enhance the speed of the process.

For instance, consider the analogy of "cutting a sausage." It involves storing segmented summary statistics for specific samples for a short duration, allowing for quick retrieval and association with incomplete or current data. This approach enables seamless navigation through historical data without sacrificing speed, even when adjusting the date range during the sampling process.

By implementing such strategies, significant improvements in performance can be accomplished. This not only enhances the efficiency of the sampling process but also facilitates smooth analysis of data across various time periods.

To provide an answer to this inquiry, it is vital to comprehend the intended utilization of this data.
As for the quantity of data, I wouldn't consider + 3GB per year to be significant. However, it is important to ascertain how many records are included within this amount.
Moreover, another crucial aspect to consider is the manner in which the data will be requested.