Consider the data staging layer as an intermediary layer that sits between the source and the target. The staging approach can add significant value to the data integration process by streamlining it and enhancing data quality.
However, many data integration teams often neglect the staging area while considering complex data integration needs. Especially for the data integration tasks where large volumes of variable data is being processed.
Why Need a Data Staging Layer for Data Integration?
In a direct data integration approach, where the data is extracted, transformed, and then loaded to the new storage, there is no dedicated space available for testing data transformations. A staging area allows the data from the source to be replicated, reformatted, and tested, before it is loaded to the target system.
Today, most businesses have multiple sources from where they want to extract data. The extracted data then needs to be refined and cleaned before loading into the new system, it needs to have the proper schema and structure. This is where a staging area comes in. In this intermediary layer, data is manipulated, replicated per requirements, joined and aggregated if necessary, and then finally cleansed.
Let’s learn how a staging area can help you during the data migration process.
Solve Quality Issues with Data Staging Layer
We know by now that a staging area is where you keep data for further transformations before loading it into a destination. But you may still have some doubts over whether it is needed for your data migration process. The following reasons will help you decide:
- In a staging area, you can perform more complex operations on the data. It allows a complete transformation of semantics and the content. You have complete control over the data because it neither resides within the source nor the destination. Any operations you perform won’t impact other tuples in the data set.
- It allows you to perform both fine-grained or coarse-grained operations on the entire datasets at once.
- You can separate certain datasets and align them on a completely different trajectory that could be further used in extracting business insights down the line.
- A staging area provides flexibility where there may be hardware or network limitations in the source or destination systems. It is also adaptable around business cycles and data processing cycles.
- Most tools that are equipped with staging areas offer complex database functions like replication, aggregation, and cleansing.
Benefits of Data Staging Layer
The effectiveness of using the data staging area highly depends on the business requirements and the environment in which the ETL system operates. Here are a few reasons why you should select tools that offer a staging area for the Data Integration process.
Data should be recovered in case of corrupted operations. So, make sure to stage data once it has been extracted from the source and then once again after every significant transformation is applied to it. The staging steps would serve as the recovery points in case of data corruption during the later stages.
Backups allow you to save, compress, and archive data down to database level. When volumes of data are getting transferred, one problem that frequently occurs is the backup of data on such a large scale. Staging areas allow data to be transferred in chunks that can be easily saved and archived.
Staged data can make the auditing process much more straightforward by simply comparing the original input files (with transformation rules) and the output data files. With increasing complexity, the data linkage is lost between the source and the target during the ETL process which can prolong and complicate the auditing process. Staging approaches allow for a seamless auditing process so that the data lineage stays intact.
Data staging can play various roles in a complete data integration operation and provides much more than just temporary staging files to support the execution. It can also help restart the workflow if such a need arises. Also, the staging file could be used as proof that certain data elements exist during the execution process. That’s why it is essential that when you are deciding to stage data during the ETL process, you have an appropriate data structure.
Data Staging Layer: Common Data Migration Rules
Here are some of the rules you can follow while designing the staging structure:
- The ETL team responsible for the entire process must own the data staging area and the area must not be accessible by anyone else. As the staging files are not created for presentation purposes, it does not include any index or aggregation to support querying. In-short, staging area files do not have service-level agreements.
- Users must not be given access to the staging area.
- No reports should be generated from data withheld in the staging area.
- Only ETL processes must be able to read-and-write on the data files from the staging area.
We hope that you are now aware of why a staging area is important for optimizing the workflow in the ETL process. The data staging layer not only helps with backups and auditing, but it also keeps a track of transformations that have been applied to data.
With that said, multiple ETL tools offer a staging area such as Astera Centerprise. Centerprise allows transformations to occur in a dedicated integration server, thus allowing for robust processing with minimum delay.