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?

ETL Process
ETL Process

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 considerations 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.

Scale your data integration effortlessly with Hevo’s Fault-Tolerant No Code Data Pipeline

As the ability of businesses to collect data explodes, data teams have a crucial role to play in fueling data-driven decisions. Yet, they struggle to consolidate the data scattered across sources into their warehouse to build a single source of truth. Broken pipelines, data quality issues, bugs and errors, and lack of control and visibility over the data flow make data integration a nightmare.

1000+ data teams rely on Hevo’s Data Pipeline Platform to integrate data from over 150+ sources in a matter of minutes. Billions of data events from sources as varied as SaaS apps, Databases, File Storage and Streaming sources can be replicated in near real-time with Hevo’s fault-tolerant architecture. What’s more – Hevo puts complete control in the hands of data teams with intuitive dashboards for pipeline monitoring, auto-schema management, custom ingestion/loading schedules. 

All of this combined with transparent pricing and 24×7 support makes us the most loved data pipeline software on review sites.

Take our 14-day free trial to experience a better way to manage data pipelines.

What is ETL Full Load?

Initial Load vs Full Load ETL: ETL Full Load
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.

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

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.

All of the capabilities, none of the firefighting

Using manual scripts and custom code to move data into the warehouse is cumbersome. Frequent breakages, pipeline errors, and lack of data flow monitoring make scaling such a system a nightmare. Hevo’s reliable data pipeline platform enables you to set up zero-code and zero-maintenance data pipelines that just work.

  • Reliability at Scale: With Hevo, you get a world-class fault-tolerant architecture that scales with zero data loss and low latency. 
  • Monitoring and Observability: Monitor pipeline health with intuitive dashboards that reveal every stat of pipeline and data flow. Bring real-time visibility into your ELT with Alerts and Activity Logs 
  • Stay in Total Control: When automation isn’t enough, Hevo offers flexibility – data ingestion modes, ingestion, and load frequency, JSON parsing, destination workbench, custom schema management, and much more – for you to have total control.    
  • Auto-Schema Management: Correcting improper schema after the data is loaded into your warehouse is challenging. Hevo automatically maps source schema with destination warehouse so that you don’t face the pain of schema errors.
  • 24×7 Customer Support: With Hevo you get more than just a platform, you get a partner for your pipelines. Discover peace with round the clock “Live Chat” within the platform. What’s more, you get 24×7 support even during the 14-day full-feature free trial.
  • Transparent Pricing: Say goodbye to complex and hidden pricing models. Hevo’s Transparent Pricing brings complete visibility to your ELT spend. Choose a plan based on your business needs. Stay in control with spend alerts and configurable credit limits for unforeseen spikes in data flow. 

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.

Initial Load vs Full Load ETL: Rows Sync
Image Source

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.


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!

Visit our Website to Explore Hevo

Hevo will automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Customer Management, etc. Hevo provides a wide range of sources – 150+ Data Sources (including 40+ Free Sources) – that connect with over 15+ Destinations. It will provide you with a seamless experience and make your work life much easier.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand.

You can also have a look at our unbeatable Hevo pricing that will help you choose the right plan for your business needs!

Content Marketing Manager, Hevo Data

Amit is a Content Marketing Manager at Hevo Data. He enjoys writing about SaaS products and modern data platforms, having authored over 200 articles on these subjects.

All your customer data in one place.