The Hidden Side of Real-Time Data: Storage Challenges You Didn’t Expect

At a time when users expect immediate updates wherever they go, more and more businesses rely on real-time data processing in the hopes of staying competitive. For the most part, they succeed: financial transactions, live analytics, IoT sensors, and other systems help companies make instant fact-based decisions. The price of such speed can be hefty, though, all because of storage challenges.

If they occur, performance might drop all of a sudden, and the costs might rise to the extent that even an established business will struggle to pay. Even the most reliable devices like Mac are not immune to crashes under these conditions. You can see what risks insufficient storage space poses here and get essential tips for clearing your Mac discs. Fail to pay attention to how much free space you have left, and you’ll start running into challenges you never expected to encounter.

Today, we are going to learn about them. See what they involve and find out the ways you can fight them.

Key 3 Challenges of Real-Time Data

There are three biggest storage challenges related to real-time data processing, and we’ll review them one by one.

Challenge 1: Uncontrollable Data Volume Growth

When real-time data processing is at work, the systems capture everything they’re detecting — and that means, quite literally, everything. Needless to say, the volume of this information explodes because every irrelevant and repetitive detail is processed and committed to memory.

Healthy data management becomes impossible at this point, especially if you have limited storage space. This is what such an uncontrollable real-time data growth can lead to:

• Storage costs will skyrocket quickly, while the business value you derive from the data won’t be proportional at all. Most of the info you’ll end up with will be useless junk.
• Query performance will start to slow down because your datasets will be overfilled with meaningless data; processing everything quickly will become an impossibility.
• Data management will suffer increasingly, and control will slip out of your hands as the sheer volume of information will make it impossible to process.

The problem with real-time data is that it comes to you in its raw, initial shape, and without strict control, you’ll face an ocean of duplicates and useless facts.

Challenge 2: High Ingestion Speed

Most people are satisfied with the knowledge that real-time data can be processed instantly, and they don’t think beyond that. However, high ingestion speed comes with its own price tag.

• Systems must write and absorb data at the same speed it is ingested; otherwise, users will face constant bottlenecks and data loss.
• Traditional storage systems rely on disk-based architectures with limited write capabilities. Even the most modern SSDs might struggle if they’re forced to accept millions of data points per second.
• Some events might arrive out of order and systems might fall out of sync; these issues are common when the speed of information surpasses the norm, which is inevitable with real-time processing.

The only way for businesses to deal with this challenge is to focus on scalable ingestion pipelines. We’ll review this solution in more detail a bit further below.

Challenge 3: Need for Constant Access

Another challenge is that real-time data is only useful when it’s actually transmitted in real time. To make sure it remains relevant, systems have to be ready to absorb and deliver it at all times, with no delays or second-long pauses.

Simultaneously, these same systems have to keep older data within reach for comparison or analysis. This creates a set of problems:

• Traditional storages can rarely cope with batch queries, so the more intense workloads you require, the harder it will be for your systems to comply with them.
• Fast storage is expensive, meaning that keeping the growing volumes of data within reach can get costly in no time.
• Systems have to handle reading and writing data at the same time, which is tricky and can slow them down unless you keep them in a perfect state.

To remain effective, businesses have to find a balance between making the data easy to access and keeping the storage costs bearable.

Best Practices for Managing Real-Time Storage Challenges

To counter the costs of real-time data processing and solve the challenges we’ve identified, you need to stick to the tried and tested best management practices. Here they are:

• Use scalable architectures. Plan for your storage to get a hundred times bigger than it is at the moment. Use scalable, distributed architectures that can spread all the data across multiple nodes and reduce the number of inevitable bottlenecks.

• Optimizing data ingestion pipelines. To survive insane ingestion pipelines, you should design them accordingly. Data collection should be separated from storage; if a sudden spike happens, the system has to hold data at least for some time. Most importantly, incoming data should be spread evenly across the pipelines.

• Use tiered storage strategies. Data can be divided into tiers based on its relevance; you can assign colors to it, like green, yellow, and red. Red data is something that requires instant access; yellow data is for less frequent queries, and green should focus on historical data.

• Rely on cloud-based storage. Cloud platforms are excellent because they can provide you with as much storage as you need. They have useful built-in features, too, such as replication, backups, and other capabilities that can help you work with data in real time.

There is a growing demand for cloud-based services, in particular: the revenue in this sector is predicted to reach $1.19 trillion in 2026. If you choose even one of these approaches, you’ll easily secure your productivity.

Use Balanced Approaches to Data Storage

You know what storage challenges might pop up as you work with real-time data now. So, make sure to counter them before they interfere with your operations. Use tiered strategies, turn to the cloud for help, and rely on Mac-specific tools to perform storage optimization with minimal effort.