What is Data Loading?

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Data Loading, Defined 

Data loading (the “L” in “ETL” or “ELT”) is the process of packing up your data and moving it to a designated data warehouse. At the beginning of this transitory phase, you can plan a roadmap, outline where you would like to move forward with your data, and consider how you would like to use it.

Your ETL Journey, At a Glance

  1. Extraction: The “E” in “ETL” is the first step in the process. This is where you start retrieving your data from various sources. You must proceed with caution during extraction. The success of the following two phases depends on this first step. 
  2. Transformation: The “T” in “ETL” is the second step of ETL, where you reformat the data from its current form to fit the form of its next host. Transformation prepares your data to be further examined in the data loading phase. 
  3. Loading (or “Data Loading”): The final stage of ETL focuses on moving the data.

The 2 Types of Loading 

Next, you must decide which loading process to deploy. There are two main types of data loading processes: a full load and an incremental load. 

Full Load

This is where all your data is selected, moved in bulk, and replaced by new data. It’s not as complex as incremental loading, as no specific loading order is required. But it does take longer than incremental loading. Plus, with such an overwhelming amount of data getting moved at once, it is much easier for data to get lost within the big move. 

Incremental Load

This is where you are moving new data in intervals. Due to its intricate nature, delivery time during incredmental loading is much faster than during its counterpart. However, incremental loads are more likely to encounter problems. This is because you must manage them as individual batches rather than one big group.


Data loading can become disorganized and chaotic very quickly, leading to challenges such as: 

  • Business Disruption. During movement, data access often becomes limited or completely unavailable. Many businesses also find it challenging to transform migrated data to serve a broad range of end users.  
  • Universal formatting. Before you begin loading your data, make sure that you identify where it is coming from and where you want it to go. Are your formats the same? Were they cared for during the transformation process? 
  • Loss of data. Did you leave anything behind? Is your data duplicated? Missing? To ensure a smooth loading process, you must track the status of all data. 
  • Speed. Are you moving too fast? Although getting closer to your final destination is exciting, take your time with this phase. Errors are most likely to occur during this time. 

But the good news is that ETL voyagers can resolve these common roadblocks early on with proper planning and delivery.

How AtScale Can Help 

AtScale enables IT and data engineering teams to manage ETL on their own terms. Our universal semantic layer empowers users to access data across sources and formats during migration. There is no business disruption, and the process is invisible to end users.

AtScale also allows teams to leverage autonomous data engineering, achieving a result equivalent to ETL. Our virtualization, modeling, and semantic layer technology empower teams to access data from both legacy and modern data warehouses such as Oracle, Teradata, Google BigQuery, and Snowflake. Ultimately, it frees up teams from worrying about mechanics so they can focus on the higher-order analytics that drive business.

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