5 reasons why operating streaming ingestion as a service is difficult

Streaming data ingestion has become a hot topic in the data engineering world today. As more businesses recognize the power of real-time analytics, the need for reliable, scalable systems to ingest data streams into long-term storage (such as a data lake) has skyrocketed. Typically, stream processing systems are used to drive applications like clickstream analysis for real-time recommendations, monitor Internet of Things (IoT) sensor data, or analyze financial transactions to detect fraud. However, these systems aren’t suitable for historical analysis or training AI models because they only deal with very recent data. This is where streaming ingest comes in — ingesting the data into a data lake allows for storage and analytics for this data over long periods.

As much as streaming ingestion represents the cool new thing to demo, what many people don’t talk about is the sheer effort it takes to take a streaming ingestion pipeline from a proof of concept (PoC) to a fully-fledged, reliable 24x7x365 production service. Simply put, ingestion is difficult, and understanding that difficulty as it extends into actual use cases built around real-time data, is an important milestone in ensuring your success.

To help, let’s break down this difficulty from beginning to end. I’ll start with the easy part: building a streaming ingestion demo.

The prototype stage: It’s easy to get started

It’s worth starting with a disclaimer. In recent years, the barrier to entry needed to build a streaming ingestion system has lowered dramatically. With tools like Kafka Connect, Spark Streaming, and Flink, it’s easy to set up a basic streaming ingestion pipeline and see it work in a matter of hours. Ingestion services and frameworks have changed the game. 

And this isn’t a secret. There are already tons of blog posts and tutorials showing you how to quickly configure these tools to stream data from Apache Kafka into a data lake. 

Once these pieces are in place, you can show a working prototype to stakeholders, and everyone will be impressed with your approach to ingestion. It feels like you’ve accomplished something significant. 

But what happens when it’s time to scale that demo to handle real-world traffic and become a mission-critical, 24×7 service? That’s where the problems start. 

5 challenges when moving your streaming service from demo to production

The journey from a demo to a production-grade service is where the real challenges begin. Building a demo is easy; building a reliable, scalable, fault-tolerant service is hard—and it’s a lot of work. In fact, it’s the exact reason SaaS companies exist: to take the complexity of managing these services off your plate.

Here’s what you need to consider when turning your streaming ingestion system into a production service:

1. Monitoring & alerting requires constant vigilance

In a production environment, things frequently go wrong. If your ingestion pipeline stops processing data for any reason, you need to know about it before it causes significant issues. To deal with this, you need to have robust monitoring and alerting in place, with automated response mechanisms designed to detect and mitigate failures. What’s more, you need to stay on top of them.

Streaming ingestion systems need to be watched constantly to ensure they’re performing as expected. The stakes are higher than in traditional batch ingestion: if your ingestion pipeline has a failure or a bug, you only have a short window (typically several hours or at most a few days) to detect, fix, and reprocess the data. After this point, you lose data permanently. 

2. Streaming ingestion services require regular online updates and automated deployments

Streaming ingestion pipelines are dynamic systems. To stay reliable, you need to keep things up to date—patching security vulnerabilities, optimizing performance, and updating dependencies. That means regular updates and automated deployment pipelines are essential for maintaining the health of the system. Due to the time-sensitive nature of streaming ingest, your deployment processes need to handle online upgrades to minimize or eliminate any downtime. 

3. You will need to invest in an on-call/SRE team

Unlike a simple batch-processing pipeline that can afford some downtime, streaming ingestion systems must run 24×7 without interruption. Dataflow is constant. To deal with this requires an on-call team—often a Site Reliability Engineering (SRE) team—that can quickly address any issues when they arise. They need to be ready to respond to system failures, performance degradation, or unexpected spikes in traffic. This ongoing operational effort is what many organizations overlook when they first start experimenting with streaming ingestion.

4. Data maintenance will be an ongoing process 

Data maintenance is another common issue. Addressing it depends on your data architecture. The best solution for a data warehouse will not be the same as a data lake. In fact, in a data lake, the story doesn’t end once the data has landed in storage — it needs to be easily accessible for querying and analysis due to the schema on read process. 

Modern data lake table formats like Apache Iceberg help with this to some extent, but for optimal query performance and to control storage costs, you still need to perform periodic maintenance. This includes consolidating small files, removing unused objects, and repartitioning the data as your needs evolve. 

Data maintenance of this type is especially important for data ingested from streams, as the need for landing data in near real-time directly conflicts with the optimal storage and data layout strategy needed for efficient queries. If you neglect this critical aspect, it becomes increasingly difficult to derive value from all the precious data that you ingested.

5. Scaling a streaming ingestion service is hard

Streaming data is often bursty and variable in nature. This means that certain times of the day or year have lots of spikes. You have to design your system architecture to handle this variability gracefully. As the volume of your datasets grows, your system needs to scale both compute and storage to keep up. What’s more, you need to optimize these resources and consider pricing at scale. Beyond this, all of your other processes need to scale too, including data maintenance, query processing, and the SRE team. Managing this growth—without introducing latency or bottlenecks into your data processing pipeline—becomes increasingly complex.

Data ingestion as a service: It’s about effortless reliability

So, you’ve figured out that scaling and operating a streaming ingestion service is a massive effort. But at the end of the day, you don’t want to be managing all of these operational complexities yourself. What should you do? 

That’s where Starburst’s Data Ingest as a Service comes in.

Comparing streaming ingestion services

So how do you get started selecting a streaming ingestion service? Well, the best place to start is to recognize that not all SaaS offerings are equal. A good SaaS product should eliminate the operational burden from your plate entirely. If your SaaS provider requires you to manage any part of the pipeline — even something as small as running a connector, managing a cluster, or running a periodic job for data maintenance — you’re still on the hook for all the operational overhead. Meanwhile, if that piece fails, your entire pipeline fails. If you have to manage parts of the service yourself, then what’s the point of paying for a SaaS solution? You might as well build it in-house. After all, if you’re responsible for handling operational complexities, you’re essentially replicating everything a SaaS company would do, with no added benefit. 

Starburst Galaxy as a white-glove streaming ingestion service

This is where the distinction becomes clear: a true SaaS solution must be white-glove. With a white-glove service, you, as the customer, shouldn’t need to manage any component of the infrastructure. The provider takes care of everything from hardware provisioning to scaling, monitoring, alerting, updates, data maintenance, and on-call support. This is, in fact, the very reason we ended up building our Data Ingest SaaS offering — every other solution and vendor offering we explored for our own ingest needs didn’t offer a true white-glove solution. 

That’s why I can honestly say I’m proud of Starburst’s streaming ingestion. It allows your team to focus on what matters most — building value for your customers — without worrying about any aspect of your streaming data pipeline.

Conclusion: Don’t settle for half a solution

Streaming ingestion is an incredibly powerful tool for businesses that need to analyze streaming data over long periods. But turning a simple demo into a reliable, scalable, and 24×7 service is a significant effort. If you’re looking for a truly effective solution, be sure to choose a provider that offers a full white-glove experience, one that removes the operational burden from your team and ensures that your pipeline is running smoothly at all times. 

In the world of streaming data, the stakes are high, and the reliability demands are relentless. Make sure you choose a partner who can meet those challenges, so you can focus on building your product, not managing infrastructure.

For more information about Starburst streaming ingestion as a service, check out this video.