Data Loading, Defined
Data loading (the “L” in “ETL” or “ELT”), is quite simply the process of packing up your data and moving it to a designated data warehouse. It is at the beginning of this transitory phase where you can begin planning a roadmap, outlining where you would like to move forward with your data and how you would like to use it. Where is the final destination?
Your ETL Journey, At a Glance
- Extraction: AKA the “E” in “ETL,” is the first step in the process. This is where you begin to retrieve your data from various sources. With extraction being the beginning of your ETL journey, you must proceed with caution. The livelihood of the following transformation and loading phases depend on the success of this first step.
- Transformation: The “T” in “ETL,” this is the second step of ETL where you reformat the data from its current form so that it fits the form of its next host. While you’re transforming your data, you are preparing to send it to be further examined in the data loading phase.
- Loading: You are here. Where to next?
Types of Loading
Soon after your departure from the extraction phase, you will be faced with the decision of which loading process that you would like to deploy. There are two main types of data loading processes: a full load and an incremental load.
- Full Load: This is where all of your data is selected, moved in bulk, and then replaced by new data. Although it is not as complex to navigate through, loading time is much slower. With the overwhelming amount of data being 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 is much faster than its counterpart. However, this speed comes at a cost. Incremental loads are more likely to encounter problems due to the nature of having to manage them as individual batches rather than one big group.
Loading your data can become unorganized, fast. For ETL voyagers, common roadblocks that many encounter early on can be resolved with proper planning and delivery. These challenges include:
- Universal formatting: Before you begin loading your data, make sure that you identify where it is coming from and where you want 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? Tracking the status of all data is critical for a smooth loading process.
- Speed: Are you moving too fast? Although it’s exciting to be closer towards your final destination, do not rush through this phase. Errors are most likely to occur during this time.
Next Steps/How AtScale Can Help
Common hurdles that organizations need to overcome when undertaking the data extraction/loading/transforming process (ELT) process include managing the business disruption caused when data access is limited or completely unavailable, and the complexities of transforming migrated data to serve a broad range of end users.
AtScale allows IT and data engineering to manage data migration via ELT on their own terms by allowing data consumers to continue to access data across sources and formats during the process. There is no business disruption, and the process is invisible to these users.
AtScale also enables business people to leverage autonomous data engineering to achieve a result equivalent to ELT, accessing data from both legacy and modern data warehouses such as Oracle, Teradata, Google BigQuery, and Snowflake and transforming your data through virtualization, modeling, and a Universal Semantic Layer. This effectively frees up time from worrying about mechanics to focus on the higher-order analytics that drive business.