3 Critical Initial Load vs Full Load ETL 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.

Initial Load refers to the preliminary loading of data from disparate sources into the Data Mart. 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.

This blog talks about Initial Load vs Full Load ETL in detail, covering the salient aspects of the two processes. It also gives you key insights into initial load and incremental load in ETL for your business use case.

What is ETL Load?

The loading stage of the ETL process largely depends on what you wish to do with the data once it gets loaded into the Data Warehouse. Typical use cases of the ETL Loading process could be:

• Devising a tool for site search.
• Generating a Machine Learning algorithm to facilitate fraud detection.
• Layering an analytics or business intelligence tool on top of the Data Warehouse.
• Implementing a real-time alerting system.

Irrespective of your end goal, one of the key best practice during the load process is understanding the work you’re requiring of the target environment. Based on your data structure, volume, load type, and target, you could negatively impact the host system when you load the data.

What is Initial Load in ETL?

In the first instance, data gets loaded into the Data Warehouse for the first time. This is referred to as an Initial Load. To prevent running into out-of-memory exceptions due to the lengthy data loading process, you need to control how much data is loaded. To load the data, you need to first prepare it and test it in a production-like environment. The required capacity needs to be prepared to produce. Therefore, it becomes imperative to schedule production downtime for an initial load.

In ETL, Initial Load refers to history tables and transaction tables that are loaded into these data flows. The performance of ETL processes can be vastly improved by setting properties such as load intervals and filters.

Once the initial data load has occurred for a base object, any subsequent load processes are known as incremental loads because for this loading process, only updated or new data is loaded into the base object, and duplicate data gets ignored.

What is ETL Full 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 ETL Full Load

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

• Easy-to-Implement: When comparing the Initial Load vs Full Load ETL, executing a Full Data Load is a straightforward process that simply deletes the whole old table and replaces it with an entire updated dataset.
• 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 Initial Load vs Full Load ETL, 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.
• 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 Initial Load vs Full Load ETL, dtime_updated, and dtime_inserted  are the most commonly used keys in delta load.

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.

Overview of Initial Load vs Full Load ETL

Aspect	Initial Load	Full Load
Definition	The first instance of loading data into a target system from a source. It involves migrating all existing data for the first time.	A subsequent complete data load where all the data (old and new) is transferred from the source to the target system, regardless of previous loads.
Use Cases	– Setting up a new database or data warehouse.- Migrating legacy data to a new system for the first time.	– Periodic complete refresh of the target system.- When data in the target system becomes corrupted and needs a fresh copy.
Benefits	– Ensures that all historical data is captured and transferred at once.- One-time effort to initialize the system with data.	– Guarantees that the target system has the most up-to-date and complete data.- Ensures consistency and data integrity by refreshing all data.
Drawbacks	– May require system downtime during the process.- Data transfer failures can result in incomplete migration.	– May cause higher system downtime due to the size and scope of the operation.- Not efficient if only a portion of the data has changed (since all data is reloaded).
Frequency	Typically performed once, during the initial setup or system deployment.	Performed periodically (weekly, monthly, etc.), depending on the system’s need for complete data refresh.
Data Transfer	Only the existing historical data at the time of loading is transferred.	Transfers all data, including both existing and newly updated data, regardless of previous loads.
Example	Migrating customer data from an old CRM system to a new one.	Reloading all customer data into a CRM system after it was corrupted.

Understanding the Differences between Initial Load and Full Load ETL

Here are the key differences between Initial Load vs Full Load ETL:

• Initial Load vs Full Load ETL: Difficulty
• Initial Load vs Full Load ETL: Time
• Initial Load vs Full Load ETL: Rows Sync

Initial Load vs Full Load ETL: Difficulty

In terms of Initial Load vs Full Load ETL difficulty, executing a Full Load is relatively easy. On the other hand, executing an Initial Load or an incremental load, the ETL pipeline would have to be checked for updated/new rows. Apart from this, the recovery from an issue thrown in an Initial Load would be harder to tackle as compared to a Full Load process.

Initial Load vs Full Load ETL: Time

Full Load takes a larger chunk of time as compared to the Initial Load for ETL.

Initial Load vs Full Load ETL: Rows Sync

In terms of Initial Load vs Full Load ETL, Initial Load can be carried out for all new records from disparate sources. On the other hand, for Full Load ETL, all the rows in source data would get synced.

Shortcomings of Incremental Data Loads

The initial full load is relatively straightforward. However, when you start taking on incremental loads, things might get more complex. Here are three of the most common problem areas:

• Schema Evolution: You need to have an idea about what happens to your existing data when a new property gets added, or an existing property is changed. Some of these changes can be quite destructive or leave data in an inconsistent state. For instance, having an idea about what transpires if your Data Warehouse starts receiving string values for a field that is expected to be an integer datatype. 
• Ordering: To handle massive scale with high availability, Data Pipelines are quite often distributed systems. This means that arriving data points can take different data paths through the system, which means they can be processed in a different order than in which they were received. If data is being deleted or updated, processing in the wrong order could lead to bad data. Auditing and maintaining order are crucial for maintaining the accuracy of data.
• Monitorability: With data coming from a large number of disparate sources, failures are pretty inevitable. A few common failure scenarios are as follows:
  • API credentials might expire.
  • An API is down for maintenance.
  • Network congestion prevents communication with an API.
  • API calls are returning successfully, but do not possess any data.
  • The pipeline destination is offline.

Any of these problems will probably result in data that is either wrong or incomplete. Therefore, recovering from these issues could turn out to be a massive headache.

To know more about Incremental Data Load vs Full Load ETL, check out our blog.

Conclusion

This article talks about the salient differences between Initial Load and Full Load as ETL processes for your data pipeline and explains their salient aspects to give you a better understanding.

However, as a Developer, extracting complex data from a diverse set of data sources like Databases, CRMs, Project management Tools like Jira, Streaming Services, and Marketing Platforms to your Database can seem to be quite challenging. If you are from non-technical background or are new in the game of data warehouse and analytics, Hevo can help!

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