Organizations managing massive volumes of log data face a difficult choice: accept unsustainable storage costs or implement practices like sampling, which permanently destroys data resolution. Many traditional data platforms push sampling as a practical solution to petabyte-scale analytics, forcing businesses to sacrifice data fidelity for economic viability.
But what if you didn’t have to choose? What if you could maintain full-resolution data indefinitely while achieving better economics than sampled alternatives?
The sampling trade-off has become so ingrained in modern architectures that teams accept information loss as inevitable. Yet every data point lost to sampling represents a possible lost opportunity for insight, a missing anomaly that could have prevented an outage, or a subtle pattern that might have revealed fraud. Modern platforms that build on cost-effective object storage makes full-resolution storage economically viable at petabyte scale, challenging the outdated assumption that sampling is necessary for reducing costs.
What Is Sampling (And Why Is It Still Common)?
Sampling is the process of retaining only a percentage of incoming data. Traditionally, the samples were randomly selected, but more customization is now possible with modern approaches. Organizations implement sampling when storage costs and application performance make full-resolution retention impractical.
The process seems logical: if you’re analyzing yearly trends or simply need a quick overview of how your systems are operating, why store second-level data? If dashboards only show hourly metrics, why keep raw logs that have millisecond precision? But for use cases like incident mitigation, root cause analysis, and detecting breaches, you need the underlying raw logs, and the more that data is sampled, the bigger the gaps will be.
Traditional observability and even many modern solutions rely on sampling because their storage architectures weren’t designed for petabyte-scale data. When storage costs scale linearly with data volume and query performance degrades with dataset size, sampling becomes a necessary evil rather than an optimization choice. Otherwise, enterprises simply end up paying too much for poor performance. But while they end up with lower costs and better query performance, the end result is significant gaps in the underlying data.
The True Cost of Data Loss Through Sampling
Irreversible Information Destruction
Once data is sampled, the original resolution is gone forever. That spike in latency that lasted 30 seconds? Individual logs that could help resolve the issue may already be gone. The brief sensor anomaly indicating equipment failure? Unnoticed until downstream impacts begin to pile up. The subtle pattern that could have predicted the outage? Good luck building anomaly detections with low-fidelity data.
Lost Forensic and Compliance Capabilities
Security investigations and regulatory audits often require examining historical data at original resolution. When suspicious activity is discovered months later, it’s challenging to perform root cause analysis and get an accurate understanding of events with sampled data. Compliance requirements increasingly mandate data preservation at original fidelity, making aggressive sampling a regulatory risk.
Machine Learning and AI Model Degradation
Machine learning models are more effective when they are trained on large, high-fidelity data sets. Sampling can be a very effective tool for making model training more effective, but teams need to have full control. That means the ability to sample a full-fidelity dataset to best train the models they are working with.
When data is sampled at ingest time, it may be missing interesting outliers and can lead to inaccurate models due to low-fidelity data. And models in production may also be less effective when trying to detect patterns in sampled data. For example, predicting a DDoS attack may require analyzing large volumes of data to differentiate it from spikes in user traffic.
The feedback loop of poorly-trained models can be particularly damaging as models provide inaccurate predictions that lead to business decisions that generate more problematic data, further degrading future model performance.
Changing Analytical Requirements
Business questions evolve faster than data retention policies. The data you sample today based on current requirements becomes inadequate when new analytical needs emerge. Data science and business intelligence teams will have business-critical questions next year that may rely on historical data, and if the data you’re collecting today is heavily sampled, it likely won’t be as useful or accurate for answering those questions.
Organizations often discover they need historical data at higher resolution only after implementing sampling policies. By then, the original data is gone, and the analytical opportunity is lost.
Why Traditional Architectures Force Sampling
Traditional analytical platforms have challenges handling the sheer volume of incoming data from distributed systems, and retaining full-fidelity data comes with high costs. Let’s take a look at why.
Tightly-Coupled Storage and Compute
In order to provide real-time analytics, platforms traditionally have stored data close to compute. The physics are simple: the less distance data has to travel, the faster that queries will be. However, this results in vertical scaling of hardware like SSDs, which quickly becomes very expensive and simply isn’t viable for large volumes of data.
Query Performance Degradation
As data volumes grow, so does the time needed to query that data. For platforms that aren’t designed for petabyte-scale datasets (or databases that aren’t properly optimized), users often have to deal with high query latency and query timeouts. And due to inefficiencies in querying, these long-running queries also come with high costs.
Sampling combined with aggregations can lead to fast dashboards with low query latency, but the underlying raw data is less reliable, if it’s even queryable at all.
Ingest Pipeline Latency
High throughput can be a major challenge when dealing with spikes in log data or just consistently high volumes of log data in general. For enterprises ingesting terabytes of log data every day, traditional pipelines can get bogged down, leading to slow time-to-insight and even ingest failures. Head-based sampling, where data is sampled and discarded without processing, can alleviate these challenges and bring down costs, but once again at the expense of having full-fidelity data.
The Modern Alternative: Efficient Full-Fidelity Data
Modern solutions are designed for petabyte-scale datasets. They address the challenges of high data volume by increasing efficiency and reducing costs. And they do so by maximizing the performance of cost-effective object storage.
Decoupled Storage and Compute
As discussed in the previous section, many solutions have relied on tightly-coupled storage and compute to keep query latency low. But the downside to this approach is that vertical scaling is astronomically expensive for large volumes of data. By decoupling storage and compute (such as by using stateless, Kubernetes-based systems), solutions can build on object storage, which is distributed, scales horizontally, and uses cheap commodity hardware. Decoupled systems make it easier to autoscale resources (no more overprovisioning) and allow enterprises to retain full-fidelity data long-term at a much lower cost.
Maximizing Object Storage Performance
On its own, object storage isn’t suitable for real-time analytics. But with the right optimizations, including parallelism, high compression, partitioning, and indexing, it can be an effective choice for near real-time. Solutions like Hydrolix can achieve a time to insight of under ten seconds even when ingesting hundreds of terabytes of data per day. This typically involves building pipelines designed to maximize the ingest throughput of object storage and minimize the amount of data retrieved during queries. For ingestion, strategies include using multiple object storage prefixes, partitioning to avoid transferring many small files, and maximizing compression to minimize the amount of data that’s transferred. And on the query side, combining compression, advanced indexing strategies, and techniques like predicate pushdown allow queries to pull only narrow-byte ranges from object storage, maximizing query performance. Altogether, these strategies make cost-effective object storage viable for real-time analytics, allowing enterprises to retain massive, high-fidelity datasets for much longer at a fraction of the cost.
High Compression for Log Data
Modern solutions like Hydrolix can achieve 20-50x lossless compression ratios on log data without losing any information. High-density compression is critical both for reducing overall costs and unlocking the performance benefits of object storage.
Read more about the benefits of compression and why they are critical for maximizing the performance of object storage.
Advanced compression algorithms can maximize columnar compression based on data type, the range of data stored in a column, and other factors. And because many log data attributes have highly repetitive values (for example, a boolean column has two possible values) making them ideal candidates for compression. To maximize this compression, solutions use columnar storage, highly adaptive algorithms, and techniques like dictionary encoding and run-length encoding.
Making the Transition: From Sampling to Full Fidelity
Assess Your Current Data Loss
Start by quantifying what sampling is actually costing you:
- What questions can’t you answer because data is being sampled?
- Which investigations failed due to insufficient data resolution?
- What machine learning improvements could full-resolution data enable?
Calculate the True Economics
Compare the total cost of ownership between sampling and modern full-resolution storage:
- Storage costs: Modern compression often makes full resolution cheaper than traditional sampled approaches.
- Operational overhead: Eliminate the complexity of managing sampling pipelines and aggregation rules
- Opportunity cost: Quantify the value of insights lost to sampled data. Don’t forget to consider whether sampled data is impacting your organization’s AI readiness.
- Risk mitigation: Consider the compliance and forensic advantages of complete data.
Consider a Modern Solution Built on Object Storage
If you’re paying too much to ingest, store, and analyze log data, it’s likely that you’re using a traditional solution (such as for observability) that’s built on legacy architecture. Instead consider a modern solution that maximizes the benefits of object storage. Gradually migrate historical workloads as you validate the economics and performance benefits. Or consider a proof of concept (such as the one Hydrolix offers) and by the time the trial is complete, you’ll have enough data in your new solution that you can migrate much more easily, especially if you already were storing incomplete data due to sampling (or discarding it altogether after a short retention window).
Key Takeaways: Why Sampling Is an Anti-Practice
Sampling is the wrong solution to the problem of legacy architectures. You shouldn’t have to sacrifice your data because you are reliant on a legacy architecture that isn’t designed for the volumes of log data that many enterprises are dealing with today.
Every sampled dataset point is a lost opportunity. And with enterprises focused on AI readiness, full-fidelity data is more important than ever to ensure that machine learning models are accurate.
The economics have fundamentally changed. With 20-50x compression ratios and solutions built on object storage, maintaining full resolution often costs less than traditional sampled architectures. You’re no longer trading data fidelity for storage economics. Instead, you can have both.
Compliance and regulatory mandates increasingly require original data preservation. Machine learning models demand high-resolution training data. Forensic investigations need complete audit trails. Sampling creates risk in all these areas.
The question isn’t whether you can afford to keep full-resolution data. It’s whether you can afford not to. In an era where data drives competitive advantage, deliberately destroying information through sampling is a strategic mistake that modern technology makes unnecessary.
Next Steps
Learn how Hydrolix can help you reduce your log analytics costs and keep all your data with a managed trial.
