Data loading is a critical job for all data engineers and this task often comes with one question: What kind of strategy should I use? In this article, we are going to look at the two most common strategies Full Load and Incremental Load. We will discover what each of these does, how each works, as well as situations where they can be used best so that you have enough facts to choose from.

Full Load

In this strategy, the entire source table data is loaded into a target warehouse in a full load, which is also known as destructive loading because it deletes and replaces the previous destination records.

• When to Use Full Load?

  • Initial Data Loading: It is suitable for initializing a data warehouse or a data lake for the first time.

  • Small Datasets: It works best with smaller datasets that may not put much strain on the system.

  • Periodic Overhauls: Use it if you need periodic complete data refreshes.

• Pros of Full Load:

  • Simplicity: Easier to implement and manage.

  • Consistency: Ensures the entire dataset is synchronized and consistent.

  • Data Integrity: Minimizes risks of data corruption since the entire dataset is reloaded.

• Cons of Full Load:

  • Resource Intensive: High demand for system resources and longer processing times.

  • Downtime: This may require downtime to avoid conflicts during the load process.

Incremental Load

Only the newly arrived or changed data is synced to the destination warehouse in an Incremental Load. This method is more efficient for handling large datasets.

• When to Use Incremental Load?

  • Large Datasets: Optimal for systems with vast amounts of data where full loads would be impractical.

  • Frequent Updates: Ideal for environments with frequent data changes or updates.

  • Real-Time Analytics: Supports near real-time data processing and analytics.

• Pros of Incremental Load

  • Efficiency: Reduced resource consumption and faster load times.

  • Minimized Downtime: Lower impact on system performance during the load process.

  • Scalability: Better suited for scaling with growing datasets.

• Cons of Incremental Load

  • Complexity: More complex to implement and maintain. You need at least one cursor field. We have talked more about it in the Cursor Field Section.

  • Data Consistency: Requires robust mechanisms to ensure data consistency and handle conflicts. Example: Conflicts can be data type changes of a column.

What is a Cursor Field and Which One to choose:

The cursor field is a column that keeps changing when you add a new record, this helps to make sure if there is any change in data present or not. At Datazip we mostly recommend using updated_at as a cursor field because it is going to be updated each time when a record gets updated or created.

If the use case is not to get an updated row or there is no update to prev data in the table you can go with the primary key as well.

The cursor field is used whenever a sync starts a query is fired with the cursor field in where condition to retrieve data updated after a bookmarked cursor (State in Case of Datazip).

Some Of The Methods that are being used in Incremental Strategy

1. **Time Stamp or Versioning Based:
   **In this method, a timestamp or version is used as a cursor field to retrieve the updated records. For example updated_at.

2. **Change Data Capture (CDC)/ Log Based:
   **In this method, we have to rely on the transaction logs written by the database itself. Our connector has to read the logs and update the warehouse accordingly. Read More​

CDC is a better choice if you do not have a cursor field or you want to know which records have been deleted.

3. Differential Load:
In the Differential Load method, there is a comparison between the previous snapshot and the current snapshot to get the updated records. This is an inefficient method as you are doing a full snapshot which is similar to a full load and also comparing it to a previously taken snapshot.

Comparison of Full Load and Incremental Load Strategy:

Conclusion:

Choosing between Full Load and Incremental Load strategies depends on your specific ETL needs and the characteristics of your data environment.

Full Load is a straightforward and easy-to-setup method that ensures complete data consistency, making it ideal for initial data loads, small datasets, and periodic overhauls. However, it can be time-consuming and resource-intensive, making it less suitable for environments with frequent updates or large datasets.

Incremental Load, by contrast, offers improved efficiency by processing only new or changed data, resulting in faster updates and reduced strain on resources. This approach is particularly beneficial for large datasets and real-time analytics. However, it requires careful tracking of changes and robust mechanisms to maintain data integrity, adding complexity to the implementation.

By understanding these trade-offs, you can select the strategy that best aligns with your operational requirements and data goals. Datazip supports both Full Load and Incremental Load from over 100+ sources, providing the flexibility you need to optimize your data-loading processes. Explore our solutions and see how they can benefit your organization at datazip.io.

Author: Ankit Sharma, Software Engineer at Datazip who loves to simplify things.

FAQ:

1. What are the key differences between Full Load and Incremental Load strategies?

   Full Load overwrites all data in the target, best for smaller datasets or initial setups, ensuring uniformity but high resource consumption. Incremental Load syncs only new or changed data, ideal for large datasets or dynamic environments, enhancing efficiency but requiring complex change tracking mechanisms.

2. How does the choice of data loading strategy impact data consistency and system performance?

   Full Load maximizes data consistency at the cost of higher resource use and potential downtime. Incremental Load boosts system performance by reducing load times and resource demands, though it necessitates advanced strategies to preserve data consistency across updates.

3. What are cursor fields, and how do they function in Incremental Load strategies?

   Cursor fields track the latest data sync point, essential in Incremental Loads for pinpointing new or updated entries. Utilizing a last_modified field in transactional databases, for instance, ensures only recent changes are processed, streamlining data updates.