What is the Difference between Delta Processing and Full Processing?

The ability to query data, generate compelling data visualisations and insights are all the results of having a solid extract, transform, and load processes as part of your data studio. Of these, full load processing and delta processing are the most prominent ones. Full load processing means that the entire amount of data is imported iteratively the first time a data source is loaded into the data studio. Delta processing, on the other hand, means loading the data incrementally, loading the source data at specific pre-established intervals. 

But how does data preparation work? First, the initial raw data is extracted from various data sources in its original format. Second, it gets transformed into a unified format while performing cleansing tasks such as removing duplicates or faulty data and making sure that data integrity is maintained at all times. The third step is staging, meaning that the data enters a temporary location from which it can be easily rolled back if errors are detected.

We reach the last step when the data is loaded into target tables (or core tables) and becomes part of long-term data histories. The data history includes both existing data and new data that is continuously being loaded. This usually involves rigorous testing across the data pipeline and the application of data processing techniques. 

Various data preparation and processing techniques are in place to make sure that the data that gets into the long-term data histories is complete and free of error. Full load processing and delta processing are the most prominent ones. In this article we’re going to have a look at these two approaches to data preparation and processing in more detail and pinpoint the advantages and disadvantages of both:

 

Full Processing (Full Load) 

Full processing is always executed on the full raw data set. All processing steps behind the raw data layer are considered temporary. They can be recovered from the raw data at any time. Full processing processes include every transformation step, including every historical transformation step. 

The Advantages of the Full Load Approach

• Code versioning corresponds to data versioning.
• Retroactive changes can be implemented with ease.
• The transformation processes are simpler.
• The approach allows for easy traceability back to the raw data.
• AnalysisDB can be skipped if necessary and marts can be created directly from the raw data.

Some Disadvantages of the Full Load Method in Data Processing

• Often, the full load approach means that data processing can get extremely slow and requires high computational load. Because of these limitations, the data processing frequency is often only possible on a weekly basis at best.
• Corrections are implemented slowly as the smallest data error requires a new full load.
• Release-like provisioning process. 
• The raw data history must be permanently held for access.
• If version levels are required for access, multiple analysis DB must be created. This leads to many data redundancies.
• No single source of truth for analysts in the analysis DB.
• In case of direct marterization, many transformations are executed multiple times. This can lead to substantial delays. 
• Ultimately, this approach results in extremely high total data volumes.

Instead of pulling the whole data out of a table time and time again, only the new additions and the modifications are being extracted. 

Delta Processing

How does delta processing work? The target tables are updated only with the modified or entirely new data records. So with delta processing, we do not take the entire data from a table but only the new data that has been added since the last data load. In delta processing, the system recognises which rows within a table have been pulled already, which ones have been modified, and which ones are entirely new.

In delta processing, all processing steps behind the analysis layer are considered temporary. They can be restored from the analysis layer at any time. With this approach, only the current transformation steps are picked up and executed only on the new data packages. Delta processing also provides functions for retroactive correction of the dataset in the analysis layer.

The Advantages of Delta Processing

• Fast loading times with significantly less computational load. 
• Fast corrections and adjustments in the analysis DB. 
• The approach makes it possible to perform high-frequency loading up to stream processing (real-time processing).
• Multiple version statuses can be efficiently mapped in one database.
• Raw data can be archived without loss of history.
• The approach provides a “single source of truth” for all analysts in the analysis DB.
• Ultimately, this approach results in lower total data volumes.

The Disadvantages of the Delta Method

• High complexity: For all its advantages, this method involves complex loading processes as delta matching with inventory data is required. 
• Delta loading processes are not suitable for full processing. Initial loading requires additional technically optimised full processing, otherwise, initial loading is comparatively slow.
• Code versioning is not sufficient and additional data versioning in the analysis DB is required.
• Traceability back to the raw data is somewhat more complex.

In a nutshell, incremental loading is significantly faster than the full processing (full load) approach. At the same time, delta loading is more difficult to maintain. You are not able to re-run the entire load in the case of an error the way you can with full processing. 

All things being equal, companies continue to prefer bulk loads and are focusing more on real-time stream processing dictated by current business objectives.

The goal continues to be simplification, meaning a sustained interest in faster loading times and reducing complexity. 

With the Data Studio by Record Evolution, you extract data from a variety of sources, automate load jobs, and integrate with any of your favorite tools thanks to our RESTful API. Data visualisation, analysis, and building ML models can also take place directly on the platform to generate insights fast while using the data platform as a unified data hub within your organisation.