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TABLE OF CONTENTS
     
    Delivering analytics to customers might seem like an extension of what internal BI platforms already do—running SQL queries, rendering dashboards, and fetching results quickly. However, speed is only part of the equation: what makes customer-facing workloads truly hard isn’t just latency—it’s latency under pressure, stability under chaos, and performance without trade-offs.
    Here’s why general-purpose data stacks often fail, and what makes customer-facing analytics uniquely demanding.

     

    "Fast on Average" Is Not Enough

    In customer-facing analytics, latency outliers—not averages—define user experience. Dashboards and APIs must remain responsive even under unpredictable access patterns, large data volumes, and sudden spikes in concurrency. A single query breaching the p99 threshold can stall entire workflows or lead to perceived downtime.
    Slow queries in these systems are often driven by:
    • Cache invalidation due to frequent updates or scaling events
    • Outdated optimizer statistics, leading to poor join and scan plans
    • Resource contention across tenants or between ingestion and query workloads
    • Skewed access patterns, where a few large queries monopolize I/O or memory
    These issues aren’t always visible in staging, but they become apparent quickly under real-world load.

     

    Preprocessing Pipelines Introduce Hidden Trade-Offs

    To avoid expensive joins or slow aggregations at query time, many teams rely on preprocessing, which involves denormalizing datasets, pre-aggregating metrics, and reshaping tables to fit the engine's limitations.
    But these pipelines introduce significant costs:
    • Data freshness degrades — update delays range from minutes to hours.
    • Schema flexibility is lost — even minor changes trigger full backfills.
    • Overhead increases — duplicated data inflates storage and governance overhead.
    These pipelines create operational friction and make it harder to deliver timely, queryable data, especially when users expect near-real-time updates and evolving metrics.

     

    The Cost–SLA Tradeoff Is Architectural, Not Just Operational

    Customer-facing analytics often lives in a tension between two conflicting requirements:
    • Meeting SLAs for latency, freshness, and concurrency.
    • Controlling infrastructure costs at scale.
    You can overprovision to ensure headroom during peak traffic—but that means paying for idle capacity most of the time. Or you can run lean to save on cost—but risk timeout spikes when traffic surges or queries become more complex.
    Neither knobs nor autoscaling fully solve the problem. What’s needed is an architecture that:
    • Scales compute independently of storage, without moving or duplicating data.
    • Isolates tenants and workloads without adding operational complexity.
    • Maintains predictable performance, even with skewed access patterns and shifting traffic.

     

    Data Skew Breaks Assumptions About Scale

    In many customer-facing workloads, a small number of large tenants account for the majority of data and query volume. When partitioning is based on uniform assumptions—like evenly sized customer_id ranges—it often results in hot partitions that overload specific backends while others sit idle.
    This leads to:
    • Unbalanced scan workloads.
    • Backend contention on large partitions.
    • Unpredictable tail latency for large or shared queries.
    Addressing skew requires more than just better resource scheduling—it’s a storage problem. Techniques such as multi-level partitioning and bucket-based distribution help distribute heavy workloads across multiple shards, enabling better parallelism and more predictable performance at scale.

     

    Want the Full Breakdown?

    Learn more in our latest white paper where we go deeper into:
    • Why traditional data stacks fall short for customer-facing workloads.
    • The architectural foundations for sub-second, high-concurrency analytics.
    • Two reference architectures: one for real-time customer-facing analytics with low-latency ingestion, and one for lakehouse-native workloads prioritizing governance and open table formats.
    • Real-world solutions from industry leaders like Pinterest and Demandbase.
    If you're designing analytics for external users—dashboards, data APIs, or embedded insights—this white paper outlines what it takes to get it right. Read the full white paper here.

     
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