The Advantages of Incremental Data Collection Over Batch Processing

While organisations have developed considerable expertise in batch processing, it’s essential to recognize that every methodology has its limitations. Despite its advantages, batch processing can struggle to keep pace as data volumes and processing requirements become more complex.

Initially, batch processing served as a reliable approach for managing data workflows. Systems were optimised to handle scheduled batch jobs efficiently. However, as data needs expanded, challenges rose that tested the scalability and responsiveness of the batch processing approach.

A significant issue is the high-volume data that needs processing. As datasets expand, batch processing struggles to efficiently handle the increased workload. This inevitably leads to stretched processing times and intensified resource demands, resulting in delays in data availability and analysis.

Additionally, managing dependencies and failures within batch processing workflows becomes increasingly complex as systems expand. The interconnected nature of batch jobs requires intricate scheduling and monitoring, consuming valuable time and resources.

Picture a company heavily dependent on insights drawn from a crucial legacy mainframe system, which forms the backbone of its operations. The importance of making the data available in due time is undeniable, as it directly impacts the company’s ability to make swift, informed decisions. 

To make this data accessible, companies commonly execute numerous jobs scheduled overnight to minimise CPU consumption and reduce pressure on the mainframe during active working hours. These jobs run sequentially, with dependencies between them. It is a frequent occurrence that the maintenance team must investigate, rectify, and rerun certain jobs that encountered failures overnight. This routine introduces considerable delays, impairing the company’s ability to access insights in due time and placing additional strain on the source system during periodic data extractions. 

In some cases, batch collection is even performed without any memory, lacking awareness of alterations in the data since the last run, and leading it to retrieve the entire dataset from the source system each time the batch executed, increasing resources usage, impacting operational systems and slowing down the process. 

Recognizing these challenges, the importance of exploring alternative approaches, such as incremental data collection,  where the focus shifts from periodic large-scale extractions to a continuous, real-time flow of data, becomes evident.

Incremental data collection is a real-time approach that involves handling data as it comes, piece by piece. Unlike traditional batch processing, incremental data collection acts on data immediately. This method is particularly valuable for managing high-velocity data streams, allowing organisations to capture and process data in real-time as it becomes available. It enables continuous synchronisation and analysis, ensuring that insights and decisions are based on the most up-to-date information.

Transitioning to incremental collection represents a paradigm shift, streamlining data movements to the absolute minimum, where only the altered data is collected. Although this shift may seem like a straightforward concept, it provides substantial advantages compared to the conventional batch data collection, even for use cases that do not explicitly require real-time data.