Data loading in a data warehouse refers to the process of physically moving data from source systems into the warehouse environment. It involves transferring, validating, and preparing data so it can be used for analysis and reporting. Depending on the requirement, the loading process can take place in different ways:
- Initial Load: Loading all tables in the data warehouse for the first time.
- Incremental Load: Periodically applying only the changes (inserts, updates, deletes) since the last load. After performing incremental loading, referential integrity between fact and dimension tables must be verified.
- Full Refresh: Completely deleting the existing data in a table and reloading it with fresh data from the source.
Note: The DBA must verify that each record in the fact table is related to one record in each dimension table that will be used in combination with that fact table.
Refresh versus Update
After the initial load, the data warehouse needs to be maintained and updated and this can be done by the following two methods:
- Update: application of incremental changes in the data sources.
- Refresh: complete reloads at specified intervals.
Loading the Dimension Tables
Procedure for maintaining the dimension tables includes two functions: initial loading of the tables and thereafter applying the changes on an ongoing basis. Surrogate keys are used in a data warehouse. The records in the source system have their own keys (often called Natural Keys or Business Keys).
- Therefore, before an initial load or an ongoing load, the Natural Keys must be mapped to Surrogate Keys in the data warehouse.
- Another major issue is related to the application of Slowly Changing Dimension (SCD) techniques, specifically Type 1, Type 2, and Type 3 changes, to the data warehouse.
Loading the Fact tables
- The key in the fact table is the concatenation of keys from the dimension tables.
- So for this reason amplitude records are loaded first.
- A concatenated key is created from the keys of the corresponding dimension tables.
Methods for data loading
- Cloud-based: ETL solutions in the cloud are frequently able to process data in real-time and are designed for speed and scalability. They also contain the vendor's experience and ready-made infrastructure, which may offer advice on best practices for each organization's particular configuration and requirements.
- Batch processing: Data is moved every day or every week via ETL systems that use batch processing. Large data sets and organizations that don't necessarily require real-time access to their data are the greatest candidates for it.
- Open-source: Since their codebases are shared, editable, and publicly available, many open-source ETL systems are extremely affordable. Despite being a decent substitute for commercial solutions, many tools may still need some hand-coding or customization.
ETL Tools
In the present-day market, ETL equipment is of great value, and it is very important to recognize the classified method of extraction, transformation, and loading method.
- Skyvia
- IRI Voracity
- Xtract.io
- Sprinkle
- DBConvert Studio By SLOTIX s.r.o.
- Informatica – PowerCenter
- IBM – Infosphere Information Server
- Oracle Data Integrator
- Microsoft – SQL Server Integrated Services (SSIS)
- Ab Initio
Data loading challenges
- Slow down analysis: Every time a data source is added or changed, the system has to be reconfigured, which is time-consuming and hinders the ability to make quick decisions.
- Increase the likelihood of errors: Changes and reconfigurations open the door to human error, duplicate or missing data, and other problems.
- Require specialized knowledge: In-house IT teams often lack the necessary skills (and bandwidth) to code and monitor ETL tasks.
- Require costly equipment: In addition to investing in the right human resources, organizations have to procure, house and maintain the hardware and other equipment to drive the process on site.