It’s a challenge humans have been struggling to overcome since the dawn of time. Building a solution that is fast, but also reliable. In Formula 1 auto racing, teams strive to build the fastest car possible. However, if their car never makes it to the finish line due to technical failures, it doesn’t matter how fast it is.

Organizations face a similar dilemma when it comes to data pipeline scaling. OK, without all the glitz, glamour, and champagne of F1, but… please indulge me a bit.  :-)

As data pipelines grow to handle higher volumes and increasingly complex workloads, teams face a recurring question: should we optimize for speed or reliability?

The answer, of course, is not one or the other, but a careful balance supported by practical engineering decisions.

1. Segment workloads by latency needs.

Not every job requires millisecond-level responses. For example, trading models demand real-time streaming, while monthly performance reports can tolerate batch processing. Your teams can segment workloads into real-time, near real-time, and batch layers. This allows infrastructure to be optimized: streaming platforms for critical signals, and more cost-effective batch frameworks for analytical aggregates.

2. Automate quality checks within pipelines.

Speed often breaks down when bad data propagates unchecked. Embedding validation rules directly into the pipeline (e.g. schema enforcement, outlier detection, null percentage thresholds) catches errors early. Tools like nLite scan all of your data to generate metadata and insights about your data very quickly to prevent unreliable data from reaching downstream users. This reduces firefighting while enabling teams to move faster overall.

3. Build elastic scaling into infrastructure.

Traffic is rarely constant. Batch jobs might spike at month-end, while streaming workloads surge during periods of high trading volume. Cloud-native solutions make it possible to automatically scale compute resources in response. Configuring scaling policies carefully lets teams absorb variability without overprovisioning. For example, setting minimum nodes to ensure baseline reliability and maximum caps to control costs.

4. Decouple pipelines for resilience.

A single Directed Acyclic Graph (DAG) that orchestrates everything may look neat, but it creates a brittle system. Breaking pipelines into smaller, modular units connected by durable storage (object stores, message queues, or data warehouses) improves fault isolation. For example, if enrichment fails, ingestion can still run. Decoupling enables faster recovery and targeted retries, which preserves both speed and reliability.

5. Align monitoring with business Service Level Agreements (SLAs).

Technical metrics like CPU utilization or lag are useful, but your teams should tie monitoring to actual business outcomes. For example, if a daily Compliance dashboard needs to refresh by 7 a.m., alerting should be based on pipeline completion by 6:30 a.m., not just infrastructure health. Framing reliability in business terms keeps your teams focused on what matters most.

The Takeaway.

Data pipeline scaling is not only about adding horsepower. It’s about deliberate choices, like prioritizing workloads, embedding safeguards, and designing for elasticity and resilience. By treating reliability as a driver of speed rather than its opposite, your data teams can deliver trustworthy insights at scale without unnecessary overhead.

What about you? How do you and your colleagues scale your data pipelines for speed and reliability? Please comment – I’d love to hear your thoughts.

Also, please connect with DIH on LinkedIn.

Thanks,
Tom Myers