In analytics, the ultimate currency is insight. The process of distilling actionable intelligence from raw data is essential for informed decision-making and business success. To generate such insights, organisations need to extract data from their operational systems and transform it into usable data assets for analytics.

However, this process gets even more complex when dealing with massive amounts of data and traditional technology most generally falls short.

In this very context, terms like “incremental processing” and “parallel processing” surface in technical discussions. If you are uncertain about what these concepts actually mean and, more specifically, why they are considered as effective approaches to processing high-volume data, you’ve landed in the right spot.

As we navigate the complexities of data analytics, we begin to draw parallels with everyday challenges. Just like you have most likely faced a never-ending to-do list, organisations are struggling with ever-increasing streams of data.

Similarly to a to-do-list with piled up tasks, data streams keep flowing in, making it challenging to stay on top things. So, what can be done here ?

You’ll find the answer lies in two main strategies: treating tasks as they come (incremental processing) and delegating tasks to others (parallel processing). These strategies, which are quite effective in managing daily workloads, also play a critical role in high-volume data processing.

Incremental processing - Treating things as they come

Incremental processing is like tackling your to-do list one item at a time, as tasks arrive, hence preventing them from building up into an unmanageable pile.

In more technical terms, incremental processing is a real-time approach that involves handling data as it comes, piece by piece. Unlike traditional batch processing, incremental processing acts on data immediately. This method is particularly valuable for managing high-velocity data streams.

For example, think about your email inbox. Instead of letting hundreds of unread emails pile up, you can process them as they arrive. This keeps your inbox manageable and prevents you from feeling overwhelmed. In data processing, this method ensures that data is handled efficiently and doesn’t become an insurmountable mountain of information.

The Advantages of Incremental Processing

Real-Time Insights: With incremental processing, organisations can gain insights as data is generated. This means that critical decisions can be made instantly, without the need to wait for batches of data to accumulate. This real-time aspect is invaluable for applications such as fraud detection, sensor data monitoring, and instant customer interactions.

Scalability: Incremental processing is inherently scalable. As the volume of data increases, incremental processing remains efficient and adaptable, making it an ideal choice for scalable data workflows. This scalability ensures that your infrastructure can adapt to expanding data requirements without disruptions.

Cost-Efficiency: By processing data as it arrives, incremental processing optimises resource usage. There’s no need to maintain large data warehouses or invest heavily in batch processing infrastructure. This cost-efficiency can free up resources for other critical initiatives and reduce the total cost of ownership for data processing systems.

Parallel processing - Delegating and working together

Let’s imagine now that you have some tasks that can be managed simultaneously. You decide to delegate some of them to your colleagues. This is similar to parallel processing in data systems.

Parallel processing involves putting more resources to work simultaneously to get things done faster. In IT terms, parallel processing involves breaking down complex data processing tasks into smaller, parallel tasks that can be executed simultaneously. This approach uses the collective power of multiple processing units or cores to accelerate and optimise data analysis.

You can think of it as having a team of people working on different tasks of the same project. The idea is to diminish the project completion time by concurrently working on multiple aspects of it.

Yet, not all tasks can be parallelised. Some depend on others, and trying to do them all at once might lead to some sort of disruption. This is where the famous saying takes all its meaning: “Nine women cannot deliver a child in one month.” Some tasks simply can’t be sped up by adding more resources; they have a natural order and sequence.

In data processing, not all data pipelines or software applications can take advantage of parallelism. It depends on the characteristics of the tasks and the system architecture.

The Advantages of Parallel Processing

Speed and Efficiency: Parallel processing significantly accelerates data processing tasks. By distributing work across multiple processors, you can analyse data faster and complete tasks more efficiently.

Scalability: Parallel processing allows you to add more processing units as needed. This scalability ensures that your infrastructure can cope with growing data volumes without compromising performance.

Optimised Resource Utilisation: It optimises resource usage by ensuring that all available processing power is put to work. This is especially advantageous for computationally intensive tasks, ensuring that resources are used to their maximum potential.

Enhanced Performance: Parallel processing excels in handling computationally intensive tasks that would be impractical with traditional sequential processing. Hence, enabling higher levels of performance.

Complex Data Handling: Parallel processing is well-suited for the diverse landscape of modern data, including structured, unstructured, and semi-structured data. It can seamlessly process and analyse this varied data, making it an ideal choice for complex data environments.

So, there you have it—incremental and parallel processing explained. Incremental processing is like tackling your to-do list one item at a time, preventing tasks from piling up. Parallel processing is about delegating tasks and working together to get things done faster, but it requires the right setup.

Just remember, not everything can be parallelised and understanding when to use incremental or parallel processing is key to optimising data systems and keeping that never-ending to-do list in check.

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