Best practices and pitfalls of scaling data ingestion for high-volume sources

When you’ve got users making credit card payments, web applications running in real time, and business teams pulling urgent reports, it’s nerve-wracking to think a data ingestion process could overload the system. This is the reality of working with high-volume data — it’s messy, it’s complex, and the stakes are high.

Scaling data ingestion is a constant balancing act. You’re trying to move massive amounts of data fast (Terabytes), keep everything else working smoothly, and avoid failures that could bring the whole system down. The bigger the volume, the harder it is to manage.

Since the demand for real-time data is only growing, the challenges of high-volume ingestion are getting harder to ignore. Let’s look at the realities of scaling data ingestion, common traps to avoid, and practical steps to make it work smoothly.

The Realities of High-Volume Data Ingestion

When you're moving a lot of data, small inefficiencies quickly turn into big problems. 

The first hurdle is finding a way to extract large volumes of data without interfering with the systems that keep your business running. Pulling data from databases or APIs at scale puts stress on infrastructure. That strain often creates bottlenecks, network interruptions, slowdowns, and delays. 

As systems grow in volume, they inevitably get more complex. Since many ingestion systems struggle to handle these changes, an unnoticed mismatch in schema between your source and destination could derail an entire pipeline. If organizations don’t have good visibility into their data, failed transfers or data quality problems can go unnoticed for days or even weeks. By the time you discover the problem, it’s already had downstream impacts and affected business users.

When you pull high volumes of data, you can also run into hardware limitations. At high volume, memory capacity will limit how much you can handle at once. Even if you keep adding CGPUs/ GPUs and more memory, each machine can only process so much data at a time.

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3 Pitfalls to Avoid when Scaling Data Ingestion

Even with the best intentions, it’s easy to fall into some common traps when scaling data ingestion. Avoiding these challenges will save you costly rework down the line. These are some lessons I’ve personally learned from 5x the headcount on my previous data team in less than 2 years.

Here's what to watch out for:

Pitfall #1: Underestimating Network Complexity

Network failures are a fact of life. Teams that don’t plan for it leave themselves open to unexpected disruptions. 

The first few times a connection drops or you run into unexpected latency may not seem like a big deal. But at scale, these issues can cascade into more extensive failures. 

How to avoid this pitfall: Measure the load your data ingestion adds to the system and build redundancy to ensure you can pull that data quickly without affecting performance. Plan for worst-case scenarios, considering every point of failure: 

• What if a necessary server goes down? 
• What if a connector breaks?
• What if AWS is not responding? 
• What if VPC is not working? 
• What if your SSH server is not responding?

When disaster strikes, you’ll be glad you planned ahead.

Pitfall #2: Building Data Architecture Too Rigidly

I see more and more people using non-columnar data types within columnar databases. 

As an example, an address might be stored like this in a columnar database:

Instead of splitting the address into multiple columns, some people resort to storing a single JSON object in one column.

While this workaround might work for a while, it’s slower and less efficient. Pulling data for a specific field will require special syntax. JSON objects can also get big and test the size limits of columnar database fields. 

Worse, this can cause problems when you move data. Say you are moving a database from PostgreSQL to Snowflake. Since Snowflake has a 16-megabyte VARIANT limitation, you’ll run into a wall if you’ve gone over that limit in PostgreSQL. You’ll need to leave some data behind or leave notes to direct users back to the source database

How to avoid this pitfall: To prevent headaches in the future, adapt to changing needs instead of shoehorning new data types into systems that weren’t made for them. This means using the right database for the data format you’re looking to store and eventually use. Design systems to adjust to future schema changes with minimal manual intervention.

Pitfall #3: Relying Too Much on Manual Monitoring

This is a big one. When your systems lack real-time monitoring capabilities, all you can do is react to problems after they’ve already caused damage. Manual monitoring slows down resolutions and doesn’t scale when data volume balloons. 

If your data  is a black box, you risk wasting valuable time.

I’ve experienced this firsthand. Prior to implementing observability capabilities, I was pushing over 20,000,000 records of essential customer payment data to Snowflake, where it then underwent transformation with dbt. As a global company that scaled very quickly, we were processing payments in over 30 different currencies, which meant adding a new column exchange_rate to store the currency exchange. However, this schema change led to some downstream impacts, and this column was not uniformly pushed to Snowflake. As a result, the final revenue dashboards were completely unusable by the time we got the alert (only set up on our BI tool). It was a rookie mistake - something a simple monitor set up at source and or in snowflake could have prevented.

How to avoid this pitfall: Prioritize tools and processes that enable automation and real-time visibility. Automating schema change detection and monitoring data quality at every stage can catch problems early and prevent them from escalating. Change Data Capture (CDC) is great when it comes to logging database changes however, you will need to implement a protection mechanism based on the logs in order to allow easy recovery to previous versions.

3 Steps to Scale Data Ingestion Without Losing Sleep

There are three keys to success when scaling data ingestion:

Step 1: Use Parallelism to Optimize Speed and Performance

Get data where it needs to go faster by splitting the workload. Pulling smaller chunks of data in parallel allows you to move large volumes without overwhelming any single resource. For instance, Matia will query a database in sections to make sure that no two processes overlap. This avoids bottlenecks and errors, and reduces sync times significantly. . 

Step 2: Invest in Real-Time Observability

Data observability tools give you the ability to track data processes in real time. The right setup lets you monitor progress, catch failures quickly, and even get proactive alerts when something’s off. CDC from Matia can validate your data continuously and see progress while your data is in motion. 

This isn’t just about visibility for engineers, either. Transparency can reduce frustration across the board because it helps non-technical users understand what’s going on, too.

Step 3: Automate for Flexibility and Reliability

You can build in flexibility by automating processes like schema validation. That way, your data pipelines will keep running smoothly in the background, ensuring consistent delivery even as your program evolves. This gives your data team more bandwidth to take on other projects.

Get Help Scaling Data Ingestion

Future-proof scalable data ingestion is about streamlining where you can—making complex systems reliable, efficient, and easily to manage. Given the growing demand for real-time data, automation, and observability aren’t just nice-to-haves; they’re essential. 

At Matia, we’re solving the challenges of high-volume data so organizations can scale confidently. Our unified platform supports 100+ integrations to connect with your entire tech stack. Matia ensures smooth ingestion with parallel processing, automation capabilities, real-time data observability, and continuous data validation. 

With Matia to do the heavy lifting, you can stop worrying about data complexity and focus on using all that high-volume data to drive smarter decisions. Request a demo and start a trial to see how it works. 

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