The extensive amount of data, whether arriving in batches or as continuous streams from connected sensors and web sources, has the potential to strain existing data infrastructures to their limits.

For years, ETL has been the backbone of data processing. This approach operates in a batch-oriented way, where data is extracted, transformed, and then loaded into a target system. Yet, as data volumes intensify, traditional ETL pipelines struggle to keep pace, and several limitations become readily apparent:

In this blog post, we explore why traditional ETL chains groan under the pressure of high-volume data and discuss strategies to address these challenges.

Traditional ETL and High-Volume Data: Where ETL Falls Short

1. Scalability

Traditional ETL systems are like freight trains, designed to process data in fixed batches. While this approach works well for low data volumes, it can buckle under the weight of high-volume data streams. Scaling up ETL infrastructure to accommodate this increased load can be both expensive and complex. The inflexibility of batch processing falls short when it comes to adapting to the constantly evolving characteristics of modern data.

2. Latency

As data volumes increase, batch processing introduces delays. Each batch of data must wait in line to be processed. The larger the batch, the longer the queue.

This results in high latency at the overall data pipeline level, which in turn impacts the timeliness of insights and speed of decision-making.

High latency in data pipelines often results in a domino effect on the environment. It affects not only the batches but also the entire analytical environment.

3. Impact on operational systems

Operational systems are the backbone of any organization.Disrupting them with resource-intensive batch processing can have significant consequences. It’s like trying to renovate a house while still living in it – not an ideal scenario.

Operational systems may become bogged down by the data’s sheer size and processing requirements, resulting in performance issues.

4. Infrastructure Costs

Scaling up traditional ETL systems to cope with high-volume data can be a double-edged sword. It certainly allows for greater capacity, but it also comes with increased infrastructure costs. The requirements to scale necessitates extensive investments in hardware upgrades, additional software licensing fees, and ongoing maintenance expenses.

Stream processing: A resolution for managing high-volume data

The exponential growth in data volumes has made the shortcomings of traditional ETL pipelines all too clear. In the face of this challenge, a transformative shift has emerged, one that relies on the notion of “incremental processing”, which is real-time processing.

When faced with substantial data volumes, focusing solely on processing incremental changes (referred to as “the delta”) is a significantly more effective approach compared to handling the entire dataset. This approach has several advantageous implications:

Immediate Insights vs. Batch Processing: Traditional ETL relies on batch processing, where data is accumulated and processed in predefined chunks or intervals. In contrast, real-time processing, as the name suggests, acts on data as soon as it arrives.

This immediate insight is essential in applications such as fraud detection, IoT monitoring, and recommendation engines. With real-time processing, you don’t have to wait for the next batch; you act on data the second it arrives.

Latency Reduction vs. Delayed Action: The analogy of eliminating “traffic jams” in data processing is particularly apt. With a focus on the delta, real-time processing significantly reduces latency, enabling almost instantaneous action on incoming data.This stands in stark contrast to traditional ETL, which inherently introduces delays due to its batch-oriented nature.

Scalability vs. Infrastructure Overhaul: Traditional ETL systems can struggle when data volumes spike. Scaling them often necessitates costly and complex efforts, potentially disrupting ongoing operations. In contrast, real-time processing systems, often built on parallelizable technology, are designed for scalability. Adding more processing power is a matter of adding resources. There’s no need for a disruptive infrastructure overhaul, ensuring smooth operations even during data surges.

Optimised operating costs: By focusing efforts on the changes within the data rather than processing the entire dataset repeatedly, organisations can significantly reduce their operational costs. This cost-effectiveness is particularly pronounced when parallel processing strategies are employed.

Adaptability vs. Customization Hurdles: High-volume data is diverse and comes in different structures. Traditional ETL processes can stumble when confronted with this diversity, often requiring extensive customization for each data source. Real-time processing, on the other hand, is inherently adaptable.

It seamlessly integrates and analyses various data types without the need for extensive customization efforts. Whether it’s text data, images, or sensor data, real-time processing handles it all with ease.

Minimal Impact on Operational Systems: Just as renovating a house while still residing in it can be disruptive, high-volume data processing can burden operational systems. However, the delta-centric approach minimises this impact, maintaining smooth operations and preventing performance issues and system outages.

Considering these significant benefits, it is evident that incremental processing is a key aspect to the streaming solution when managing high-volume. It not only optimises operational costs, scalability and latency, but also maintains the integrity of operational systems. Stream processing offers a profound departure from traditional ETL methods and helps organisations unlock the full potential out of their data no matter how big it is.

Want to learn more about high volume data management challenges and effective strategies to address them ? Discover how our approach can transform the way you manage your data regardless of its size.


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