Efficiently transporting data from multiple sources to a target system, such as a data warehouse, has always been challenging for businesses. The load stage of the ETL (Extract, Transform & Load) process is particularly an area of interest for improving the data migration process. Choosing between full load vs incremental load in ETL largely depends on what you intend to do with the data once it’s loaded into the data warehouse.
To evaluate the performance of Incremental Data Load vs Full Load, some of the parameters considered are data scope, speed, resource utilization, and the time required, among others. In this article, you will learn about the major differences between incremental load and full load.
Understanding Key Differences between Incremental and Full Data Load
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Selecting a data loading method is an essential part of the loading stage of the ETL process. Let’s look into the main differences between incremental load vs full load to help you make an informed choice.
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1. Full Load vs Incremental Load: Data Scope
Full Load: Also known as destructive load, a full load in ETL involves loading the entire dataset from the source system into the target database or warehouse.
Incremental Load: Also known as delta load, an incremental load involves loading only the new or updated data since the last data extraction from the source. This updates the existing dataset incrementally.
2. Incremental Data Load vs Full Load: Speed
Full Load: Opting for a full load on larger datasets will take up a great amount of time and other server resources. Applying a full load every time will slow down the loading process.
Incremental Load: When dealing with larger datasets, incremental load is much faster than full load and consumes relatively fewer resources. Instead of scanning and transferring the entire dataset, incremental loading either appends the newly created records or updates the existing data in the target system.
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3. Incremental Data Load vs Full Load: Resource Utilization
Full Load: Performing full load in ETL is a resource-intensive process in terms of time, storage, and processing power. This is mainly because a full load processes and loads the entire dataset. The increased resource consumption may affect the performance of other applications or systems running concurrently.
Incremental Load: When compared to a full load, an incremental load is more resource-efficient as it processes and loads only the incremental changes to the dataset. This helps reduce the workload significantly, placing less strain on system resources and allowing other operations to run smoothly.
4. Incremental Data Load vs Full Load: Data Consistency
Full Load: Though full load is a resource-intensive task, you can be assured of getting all your data in the destination system’s rows, ensuring it is consistent with the source system. The old data is deleted from the target tables, and the entire dataset from the source table replaces it.
Incremental Load: Incremental changes are applied to the destination system using strategies like inserting new records, updating existing records, or marking records for deletion. However, to achieve the same accuracy as a full load, you have to add complex logic to correctly identify all the new and modified records and then load them.
5. Incremental Data Load vs Full Load: Frequency of Load
Full Load: The frequency of load is lower due to its resource-intensive nature. Full loads are typically scheduled less frequently, such as weekly or overnight, to avoid excessive system resource consumption.
Incremental Load: As incremental load processes smaller amounts of data and has a reduced impact on system resources, it can be performed more frequently, even multiple times a day. More frequent updates can also help maintain data consistency between the source and target systems.
6. Incremental Data Load vs Full Load: Implementation
Full Load: A full load data transfer often involves bulk transfer methods since the entire dataset is moved. This may include bulk insert operations or direct bulk copy mechanisms.
Incremental Load: When compared to full load, more selective transfer methods, such as delta loads or change data capture mechanisms, are used to transfer only the changed or new records.
Incremental Load Implementation
Implementing an incremental load requires the identification of new or updated records in the source system since the last extraction. Commonly used methods depend on the presence or absence of a ‘last updated timestamp.’
- A Last Updated Timestamp column in the source system will record when each row was last modified. This allows you to identify records that were updated after the last extraction timestamp. You can also keep track of both updates and inserts.
- Without the Last Updated Timestamp, a common approach is to maintain a control table to keep track of the last successful extraction. The control table would store metadata like the last extraction timestamp, the number of records loaded, etc.
- To identify new records, a common approach is to use an auto-incrementing ID in the source system. By storing the highest ID value in the control table, the next run would involve extracting records where the ID is greater than the stored value. However, you can’t track any updated records.
- To identify changed records, consider using a surrogate key or a combination of natural keys with a checksum of the entire row. If the source system’s row checksum doesn’t match the target system’s stored checksum for the same key(s), it indicates that the record has changed.
Challenges Associated with Incremental Load
While the initial full load is relatively straightforward, things get more complicated with incremental loads. The common challenges include:
- Schema Evolution: If a new property is added to your data or an existing property is changed, it may lead to the destruction of data or data inconsistency. An example of this scenario is if your data warehouse, which expects to receive integer datatypes, starts receiving string values.
- Processing Order: Data pipelines are distributed systems to handle massive scale with high availability. This may lead to the arriving data points taking different paths through the system and getting processed in a different order than they were received. Processing in the wrong order, especially if data is being updated or deleted, can result in bad data.
- Monitorability: When you have data coming to your data warehouse from a large number of sources, it may be accompanied by failures. Some failure scenarios include the pipeline destination being offline, network congestion, expired API credentials, etc. Such problems may lead to incomplete or wrong data.
Conclusion
Now that you’ve seen the major differences between Incremental Data Load vs Full Load, you can select the one that suits your ETL needs better.
Full load, a simple and easy-to-setup data loading method, ensures complete data consistency. However, it is associated with the drawbacks of being more time-consuming and resource-intensive and might not be suitable for frequent updates.
On the other hand, incremental load involves processing only the changes or new data, resulting in improved efficiency with faster updates and reduced strain on resources. The drawbacks of this approach include the careful tracking of changes and complexities in maintaining data integrity.
As you collect and manage your data across several applications and databases in your business, it is important to consolidate it for a complete performance analysis of your business. However, it is a time-consuming and resource-intensive task to continuously monitor the Data Connectors. To achieve this efficiently, you need to assign a portion of your engineering bandwidth to Integrate data from all sources, Clean & Transform it, and finally, Incrementally Load it to a Cloud Data Warehouse or a destination of your choice for further Business Analytics. All of these challenges can be comfortably solved by a Cloud-based ETL tool such as Hevo Data.
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Sanchit Agarwal is a data analyst at heart with a passion for data, software architecture, and writing technical content. He has experience writing more than 200 articles on data integration and infrastructure. He finds joy in breaking down complex concepts in simple and easy language, especially related to data base migration techniques and challenges in data replication.