Introduction
As we keep moving into the digitized world, the amount of data generated is exploding at an exponential rate. This has brought about issues of data storage, maintenance, and analysis. As a result, data analytics has become a crucial part of any business today. When it comes to data analytics, the data sources may have daily or even hourly updates, and you want to capture these updates and include them in your analysis. Here comes the question - incremental loading vs. full refresh. In this blog post, we will discuss these techniques and how they differ.
Full Refresh
With a full refresh, the entire dataset is loaded every time an update is available. In other words, the entire data is replaced with a new one at every load. Full refresh has the advantage of being simple to implement and eliminates any errors, duplication or inconsistencies issues that may have arisen. It's more like cleaning a dirty slate and starting afresh.
However, the disadvantages of full refresh are significant. The time it takes to load the entire dataset is usually quite long when dealing with large data. Plus, the resources, hardware, and bandwidth required in doing this can lead to increased costs on the organization.
Incremental Loading
On the other hand, incremental loading is a technique that only adds new data that has not been previously loaded onto the existing dataset. It allows for regular and quicker updates of data-analytic dashboards. This incremental process is usually done in smaller portions, making it cheaper and less resource-intensive. Since it usually takes less time and resources to update the data, more frequent data updates are usually done.
However, incremental loading has its disadvantages. If not correctly implemented, it can lead to inconsistencies in the dataset, duplication of entries, and incomplete data. The process usually involves identifying changes or updates, and if the identification process is wrong at any point, it can lead to errors.
Comparison
Which technique is better for a data-analytics project - incremental loading or full refresh? Well, it depends on what data you are working with, the urgency of updates, and the resources available. Full refresh is usually implemented when the entire data set needs to be rebuilt or updated. It's usually done after an overnight batch, and the data can be processed, tested, and can be published by the start of the business day.
Whereas incremental loading is necessary if you want to capture the small, regular updates made to the dataset. It involves adding extra data to a specific table, which can be done during the day, and it doesn’t take up that much time.
Conclusion
In conclusion, both techniques have their advantages and disadvantages. Full refresh provides a more comprehensive process, eliminating any inconsistencies that may exist in the dataset. While incremental loading is a more dynamic and regular process and cheaper to implement. Organizations should decide which process best suits their needs with the resources available.
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