Incremental Data Load vs Full Load ETL: 4 Critical Differences

Transporting data from multiple sources to a target system such as a Data warehouse has always been a challenge for businesses across the world. The loading stage of the ETL(Extract, Transform & Load) process is particularly an area of interest for improving the data migration process. You either employ the Full Load method or the Incremental Load method for performing the data loading process.

Comparing Incremental Data Load vs Full Load for your ETL process, you can evaluate their performance based on parameters such as speed, ease of guarantee, the time required, and how the records are synced. Incremental Load is a fast technique that easily handles large datasets. On the other hand, a Full Load is an easy to set up approach for a relatively smaller dataset that guarantees a complete sync with fairly simple logic.

In this article, you will learn about the major differences between Incremental Data Load vs Full Load.  

Table of Contents

• What is Incremental Data Load?
  • Key Benefits of Incremental Data Load
  • Challenges of Incremental Data Load
• What is Full Data Load?
  • Key Benefits of Full Data Load
  • Challenges of Full Data Load
• Understanding Key Differences between Incremental and Full Data Load
  • Incremental Data Load vs Full Load: Speed
  • Incremental Data Load vs Full Load: Ease of Guarantee
  • Incremental Data Load vs Full Load: Time Required
  • Incremental Data Load vs Full Load: Rows Sync
• Conclusion

What is Incremental Data Load?

Incremental load is a selective method of moving data from one system to another. The incremental load model will attempt to compare the incoming data from the source system with the existing data present in the destination. Generally, a column containing unique values for each record is chosen for comparing the 2 datasets for any new or changed data since the loading operation was the last run. This column behaves similarly to a primary key of the dataset. The increased design selectivity often reduces the system costs required for the ETL Incremental Loading process. 

Data to be migrated is selected based on time i.e. when the data was most recently created or updated. Often, it may not be easy to identify new or modified data only in the source, hence there is a need for that data to be compared with data already in the destination.

Incremental loading can be classified into the following 2 categories:

• Stream Incremental Load: For loading small data volumes.
• Batch Incremental Load: For loading large data volumes.

Key Benefits of Incremental Data Load

Implementing Incremental Data Load allows you to leverage the following benefits:

• Faster Processing: Comparing the Incremental Data Load vs Full Load, Incremental Loading works much faster because there is lesser data to connect with in most of the cases. Assuming there are no restrictions, the time required to transmit and transform data is directly associated with the amount of data involved. In many cases, working with half the data will proportionally reduce the execution time by that amount. 
• Better Risk Management: Lesser the data you need to be exposed to, the lesser the chances of any potential surface risks associated with a particular load. Sometimes a certain load can fail or misbehave, leaving the target data in an inconsistent state. The incremental loading technique is a method of fractional loading. This reduces the amount of data you add or change that may need to be corrected if there are any irregularities. Since less data is processed, it also takes less time to validate data and review changes.
• Consistent Performance: With ETL incremental loads, you get consistent performance throughout the course of varying workloads. In general, today’s load still contains more data than yesterday. Therefore when comparing Incremental Data Load vs Full Load, the full-load execution can be a time-consuming method because the time required for processing increases monotonically. Incremental loading moves data only when it changes, thereby enhancing the probability of more constant performance. 
• Storing History: Several of the available source systems regularly delete old data. This can be an issue as you often need to communicate this data to your downstream system. The incremental loading process only needs to load newly created and changed data. This allows you to retain all source data (including data deleted from upstream sources) in your target system.  When processing an OLTP source that is not created to preserve history, a full load will also remove the history from the destination, as a full load will first delete all records. Comparing Incremental Data Load vs Full Load, the full-load won’t let you keep history in datastore.

Challenges of Incremental Data Load

While employing Incremental Data Load in your ETL process, you may face the following obstacles:

• Constant Monitoring: When extracting and merging data from multiple sources, you will notice errors over time. This can happen when your API credentials have expired or you are having trouble connecting with your API. To recognize and fix these errors as quickly as possible, you need to continuously monitor your processes. 
• Incompatibility: You can add new records that invalidate existing data. For instance, give an integer to a column that expects text. This can be an issue when adding data in real-time, which creates a bottleneck, as the end user may query that data for incorrect or inconsistent results and cannot add a new dataset. 
• Order: Data pipelines are typically distributed systems to maximize availability. This can result in the incoming data being processed in a different sequence than when it was received, especially when the data is changed or deleted. 
• Dependencies: When it comes to ETL management, it is important to understand dependencies between processes or subprocesses. For example: if process 1 fails, do you want to run process 2? It becomes more complex as new processes and subprocesses grow. 
• Regulation: The calibration process is necessary to ensure that the ETL data warehouse data is accurate and consistent. This requires you to perform regular ETL testing, however, data warehouse tuning is a continuous process.

What is Full Data Load?

In a Full Data Load, the complete dataset is emptied or loaded and then entirely overwritten (i.e. deleted and replaced) with the newly updated dataset in the next data loading run. While comparing the Incremental Data Load vs Full Load, you also don’t need to maintain extra information such as timestamps to carry out a Full Data Load.

You can consider a simple example of a Shopping Mall that loads all of the total daily sales via the ETL process into a Data Warehouse at the end of each day. Assume that there were 1000 sales done on Monday, thus, you would need to load data on Monday night with a dataset of 1000 records. Then, on Tuesday 700 more sales were done and need to be added. Similarly, on Tuesday night, 1000 Monday records, as well as 700 Tuesday records, will now be dumped in the Data Warehouse via the Full Load method.

Key Benefits of Full Data Load

A Full Data Load is a traditional Data Loading method that offers the following benefits:

• Easy-to-Implement: When comparing the Incremental Data Load vs Full Load, executing a Full Data Load is a straightforward process that simply deletes the whole old table and replaces it with an entire updated dataset.
• Low Maintenance: This technique doesn’t require you to manage the keys and whether some data is up to date or not as every time you reload the table, all data will be updated no matter what. For instance when comparing the Incremental Data Load vs Full Load, dtime_updated, and dtime_inserted  are the most commonly used keys in delta load.
• Simple Design: Based on a particularly easy-to-set uploading process, a Full Data load doesn’t require you to worry about database design and keeping it clean. While comparing Incremental Data Load vs Full Load, you will notice that If an error occurs in a Full Load, you can simply re-run the loading process without having to do much else in the way of data cleanup/preparation.

Challenges of Full Data Load

While applying the Full Data Load approach, you may encounter the following hurdles:

• Unsustainable: It can be an inconvenient data loading method when you only need to update just a handful of records but have to insert millions of records due to its architecture.
• Slow Performance: As you start dealing with massive volumes of data, performing a full data load with a larger dataset is time-consuming and takes up a lot of server resources.
• Unable to Preserve History: With Full Data Load, you can’t keep the historical data as it drops the old data and the new dataset completely replaces it. This old data is often important as in some cases you may want to track the changes in the database.  

Understanding Key Differences between Incremental and Full Data Load

Selecting a data loading method is an essential part of the Loading stage of the ETL process. You can consider the following 4 main differences between Incremental Data Load vs Full Load:

• Incremental Data Load vs Full Load: Speed
• Incremental Data Load vs Full Load: Ease of Guarantee
• Incremental Data Load vs Full Load: Time Required
• Incremental Data Load vs Full Load: Rows Sync

1. Incremental Data Load vs Full Load: Speed

When dealing with larger datasets, Incremental Load is way faster than the Full Load and also consumes relatively fewer resources too. 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. Also, as you progress, you will notice that on a daily basis the source dataset keeps becoming larger and larger. On comparing the Incremental Data Load vs Full Load, applying a Full Load every time will slow down the loading process more and more. 

2. Incremental Data Load vs Full Load: Ease of Guarantee

Comparing the Incremental Data Load vs Full Load, Incremental Load requires a fairly complex logic. For implementing a Full Load, you just need to migrate the entire data from the source to the desired destination. However, for Incremental Data Load, the engineering team needs to place an additional load logic that performs various checks for new or modified data. Also, it is not always easy to identify the new and changed data in the source. You may have to select from one of the may not-as-good options for effectively managing the incremental payload.  

3. Incremental Data Load vs Full Load: Time Required

With comparatively lesser data to interact with, Incremental Data Load requires much lesser time than the Full Load. Incremental Loading is a Fractional loading method, thereby adding or modifying less data. This reduces the amount of data that might need to be fixed if something goes wrong. Also, data validation and change inspection also take less time with fewer data to check.

4. Incremental Data Load vs Full Load: Rows Sync

Though a resource-intensive task, with Full Load you can be assured to get all data in rows of the destination system to be in sync with the source system. This is because the old data is deleted from the target tables and the entire dataset from the source table replaces it. To achieve the same accuracy with the Incremental Data Load, you have to add complex logic to correctly identify all the new and modified records and then load them. 

Conclusion

In this article, you have learned about the major differences between Incremental Data Load vs Full Load. Full Load is a simple data loading method that doesn’t require you to worry about managing any keys. In case of an error, you can also just easily rerun it without the need for data cleaning and preparation. Though when comparing the Incremental Data Load vs Full Load, a Full data Load is an extremely slow and inefficient process in the case of large data sets. Instead of loading the entire dataset, Incremental Loading checks for the newly added or modified data in the source and only loads them. This is significantly faster and also consumes a lot fewer server resources.

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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