Category: Custom Development

Building Near-Real-Time Data Pipelines

Posted on by Guy Wilnai

Best Practices and Pitfalls

Introduction: Why Near-Real-Time Matters

In today’s data-driven world, businesses rely on timely insights to make informed decisions. But while real-time data processing is often the ideal, it can be costly, complex, and over-engineered for many use cases. Instead, near-real-time data pipelines offer a practical balance between speed, scalability, and cost-effectiveness—delivering insights within seconds or minutes rather than milliseconds.

However, building a reliable near-real-time architecture is not as simple as flipping a switch. Many organizations underestimate the complexities, from data ingestion bottlenecks to governance challenges and scaling issues. In this post, we’ll cover best practices, common pitfalls, and how to choose between off-the-shelf solutions and custom-built architectures.

Defining Near-Real-Time Data Pipelines

  • What does ‘near-real-time’ actually mean? Depending on the use case, near-real-time might mean latencies of 1-5 seconds or up to a few minutes—far faster than traditional batch processing but without the extreme infrastructure demands of true real-time.
  • How it differs from batch and real-time processing:
    • Batch Processing: Data is collected and processed at scheduled intervals (e.g., hourly, daily).
    • Near-Real-Time: Data is processed with minimal delay, often in small micro-batches or event-driven workflows.
    • Real-Time Processing: Data is processed instantly, requiring high-performance, low-latency infrastructure.
  • Common use cases:
    • Streaming analytics – Operational dashboards, fraud detection.
    • IoT monitoring – Smart devices, predictive maintenance.
    • Customer personalization – Real-time recommendations, targeted marketing.
    • Financial transaction monitoring – Fraud detection, risk scoring.

Best Practices for Building Scalable Near-Real-Time Pipelines

✅ Choose the Right Architecture – Event-driven vs. micro-batch processing.

  • Tools like Kafka, Azure Event Hubs, AWS Kinesis for event streaming.
  • Azure Functions, Lambda, Databricks, Flink for processing near-real-time workloads.

✅ Optimize Data Ingestion & Streaming – Minimize latency with efficient message queues and pub-sub models.
✅ Ensure Data Quality & Schema Management – Implement real-time governance, data contracts, and schema enforcement.
✅ Design for Fault Tolerance & Scalability – Implement retries, dead-letter queues, and distributed processing.
✅ Monitor, Measure, and Optimize – Use observability tools like Datadog, Prometheus, OpenTelemetry to track latency and performance.

Pitfalls to Avoid

⚠️ Underestimating Latency Needs – Not all ‘real-time’ requirements are truly real-time. Align business needs with technical feasibility.

⚠️ Over-Engineering the Solution – True real-time processing can introduce unnecessary complexity and costs when near-real-time suffices.

⚠️ Ignoring Data Governance – Ensuring security, lineage, and regulatory compliance in streaming environments is critical.

⚠️ Failure to Scale Efficiently – Costs can spiral if pipelines aren’t designed to handle data spikes gracefully.

Build vs. Buy – Choosing the Right Approach

Organizations must decide between off-the-shelf solutions and custom-built frameworks based on their latency, scalability, and cost needs.

Off-the-Shelf Solutions (Buy)

  • Pros: Faster setup, managed scaling, built-in reliability.
  • Cons: Limited customization, vendor lock-in, and hidden constraints (e.g., throttling, scaling limits).
  • Example: Azure CDC (Preview) appeared promising for a client’s use case but had a throttling limitation that prevented reaching the required 3-5 second latency.

Custom Development (Build)

  • Pros: Optimized performance, tailored to business needs, avoids vendor-imposed constraints.
  • Cons: Requires expertise, ongoing maintenance, and higher initial investment.

Hybrid Approach

  • Many organizations find success combining off-the-shelf tools for ingestion and storage with custom development for processing and governance.

Conclusion: The Right Approach to Near-Real-Time Success

Building near-real-time pipelines is a balancing act—speed vs. complexity vs. cost. The right approach depends on your specific use case, latency requirements, and long-term scalability goals. Organizations that carefully evaluate their needs and leverage a mix of off-the-shelf tools and custom development will achieve the best results.

📩 Looking to optimize your near-real-time data pipelines? Let’s strategize, energize, and datagize your solution.