🤖 AI Expert Verdict
A data warehouse can store data using full snapshots or by tracking only changes. Full snapshots are simple but lead to massive, costly data volumes. Storing only changes, often utilizing methodologies like SCD2, ensures data uniqueness and prevents duplication across systems, which is crucial for maintaining an efficient and relevant data warehouse.
- Avoids data duplication at multiple levels (record, table, system).
- Significantly reduces data volume and processing costs.
- Keeps the data warehouse highly relevant and efficient.
- Prevents items from becoming obsolete.
Introduction: Managing Data Storage
You can store data in many ways. You might use full snapshots. Alternatively, you can store only the changes. Each method presents unique benefits and challenges.
Full Snapshots: The Easy, Costly Way
Full snapshots seem straightforward. They do not require special tools. You also avoid advanced technical knowledge. You simply store the entire database as a backup. Restoring it works perfectly well. However, this approach has a big downside. The data volume quickly becomes huge. Processing this growing pile of data costs a lot of money. Companies often delete old data monthly. This manages expenses and storage limits.
Storing Only Changes: The Efficient Path
Storing only the changes dramatically improves things. Many data engineers first consider the SCD2 method. This tracks record validity with “from” and “to” dates. It is not always simple, though. The core goal is avoiding data duplication. This principle applies beyond individual records. It covers tables and even the entire system. Read Our Blog to learn more about system optimization.
You must not store data from one source system in multiple databases. You should not extract and process the same table in different ways. You must extract and store each source system and table only once. You should also detect changes just once. Subsequent operations must ensure that every record remains unique.
The true difference lies in data management. Effective processes prevent duplicate records. This happens outside the source system. This careful management changes everything.
[adrotate group=”1″]When you retrieve data, both methods give identical results. This assumes you filter by the snapshot date. The ingestion process is actually quite similar. Inexperienced data engineers might not expect this.
The Warehouse Analogy
Picture a small warehouse. You deliver data without organization. It will eventually reach its full capacity. Removing items becomes very painful at that point. You face tough decisions about what to discard. Alternatively, approach warehousing with great care. You label every box as it enters the building. When a similar item arrives, you know which box to check first. You can decide if the new item is truly necessary. Maybe it is redundant. If you do not need it, discard it before it enters. You might also replace an old item with the new one. With this mindset, items in the warehouse never become obsolete. Shop Our Products designed for modern data architecture.
Conclusion
Store only the minimal necessary data. This keeps your data warehouse relevant and highly efficient.
Reference: Inspired by content from https://www.linkedin.com/pulse/data-warehousing-more-than-storing-warehouse-matjaz-stajner-xaltf.
