What Is Data Optimization? A Practical Guide for Observability Teams

What Is Data Optimization and Why Does It Matter?

Data optimization is the process of improving the way data is collected, stored, organized, processed, and delivered so it uses fewer resources while remaining accurate, accessible, and valuable for decision-making. It’s about making data work harder, faster, and smarter, minimizing waste while maximizing its business utility. Data optimization is not just a “nice-to-have” - it’s essential for organizations drowning in rapidly growing datasets. It keeps storage bills manageable, improves operational speed, and ensures that business insights come from accurate, accessible, and efficiently processed data.

There are a number of key elements of data optimization including:

1. Efficient storage
   
   • Using compression, deduplication, and tiered storage to reduce costs.
   • Ensuring hot (frequently accessed) data is stored for speed, while cold data is archived efficiently.
2. Data quality improvements
   
   • Removing duplicates, fixing inconsistencies, and validating formats to keep data accurate and reliable.
3. Data structuring and indexing
   
   • Organizing data in formats and indexes that speed up queries and retrieval.
   • Using optimized schemas for structured data and efficient metadata for unstructured data.
4. Performance tuning
   
   • Streamlining pipelines so data processing runs faster and consumes fewer computing resources.
   • Optimizing query execution plans and parallelizing workloads.
5. Lifecycle management
   
   • Automating data retention policies, archival, and deletion for compliance and efficiency.

Organizations that invest in data optimization experience a number of benefits including performance and speed improvements, greater cost efficiency, gains in scalability, improved data quality and reliability and improved regulatory compliance.

Definition in the Context of Observability

In the context of observability, data optimization means making the collection, storage, processing, and querying of observability data (logs, metrics, traces, events) as efficient, cost-effective, and actionable as possible, without sacrificing visibility or insight. It’s about reducing noise, eliminating waste, and prioritizing the data that actually helps you detect, troubleshoot, and prevent issues.

Observability pipelines often ingest massive volumes of telemetry data. Without optimization, storage costs balloon and query performance can slow. And engineers drown in irrelevant alerts and noise, which can make root-cause analysis more difficult.

Data optimization in observability has a number of key components. Data volume control is responsible for filtering, sampling and aggregation. Data quality and enrichment helps normalize formats across services for consistent analysis and adds relevant context. Tiered storage provides optimization and teams can apply compression and index tuning for faster queries. Intelligent routing directs different types of data to different backends depending on cost, speed and needs. And finally stream processing detects anomalies or generates alerts with storing every single event, boosting efficiency.

Following a data optimization strategy has a number of concrete benefits in the observability sphere including lower costs, faster queries, better signal-to-noise ratio, improved incident response, and scalability.

Data Optimization vs. Data Cleaning

Data optimization and data cleaning are related but distinct steps in the data management lifecycle. They often happen in sequence, but they solve different problems.

Data cleaning is the process of identifying and fixing errors, inconsistencies, and inaccuracies in data to ensure it is correct, complete, and reliable. Its main goal is to Improve data quality so analysis and decision-making are based on accurate, consistent, and trustworthy information. Data cleaning typically removes duplicates, corrects misspellings or formatting issues, fills in missing values, validates data, or resolves inconsistent naming.

Data optimization is the process of improving how data is stored, processed, accessed, and delivered so it is faster, cheaper, and more efficient to work with. Its main goal is to improve data performance, accessibility, and cost efficiency—without sacrificing accuracy. Data optimization typically compresses data, indexes, archives cold data, filters or samples, and aggregates metrics.

Data cleaning and data optimization work together where the cleaning comes first, to ensure the data is accurate and consistent. Optimization follows to guarantee the clean data is stored and processed in the most efficient way.

Key Benefits of Data Optimization in Telemetry Pipelines

Data optimization in telemetry pipelines focuses on making the ingestion, processing, storage, and querying of telemetry data - logs, metrics, traces, events - as fast, cost-efficient, and actionable as possible. When done well, it delivers both operational and business benefits. Optimizing telemetry pipelines means less noise, lower costs, faster insights and greater scalability. It transforms raw telemetry firehoses into streamlined, high-quality, high-value data flows that empower observability without overwhelming teams or budgets.

Lower Data Volumes

Batch processing, compression, and intelligent routing to match data priority with storage performance all help lower data volumes. This frees up compute, network bandwidth, and storage I/O for high-priority workloads.

Faster Time to Insight

Preprocessing data at the edge, dropping unnecessary fields, and using streaming analytics make it possible for teams to understand problems faster and have more proactive issue prevention. Also, using indexing, schema tuning, and pre-aggregated rollups gives engineers faster insights during incident response, reducing MTTR (Mean Time to Resolution).

Reduced Observability Costs

Filtering out unneeded debug logs, applying sampling for traces, and aggregating high-frequency metrics before storage reduces the volume of telemetry sent to expensive storage backends, lowering infrastructure and SaaS observability tool bills.

Improved Decision Accuracy

Deduplication, alert suppression, and noise filtering at the pipeline level prevents alert fatigue, ensuring that engineers focus only on actionable signals.

Core Techniques for Data Optimization

Core techniques for data optimization are strategies and methods used to make data faster to access, cheaper to store, and more useful for analysis without sacrificing accuracy or compliance. They apply across domains like analytics, databases, observability, and telemetry pipelines. These techniques lead to less data, better structure and smarter storage.

Data Filtering and Preprocessing

Reduce overall data volume to minimize processing and storage costs through filtering, sampling, and aggregation/rollups.

Deduplication and Compression

Reduce data size without losing meaning using lossless compression, columnar formats, dictionary encoding, and deduplication.

Real-Time Aggregation and Transformation

Organize data for faster queries and analysis with schema optimization, index tuning and partitioning/sharding. And enhance usefulness and consistency of the data through standardization, metadata tagging and join optimization.

Sampling and Prioritization

Sampling and prioritization are considered core techniques for data optimization because they directly tackle the two biggest challenges in modern data systems - volume and value - by controlling how much data you keep and which data matters most.

Sampling collects and stores only a subset of the total data while still retaining statistical or diagnostic usefulness.

Prioritization ranks or selects data based on importance, relevance, or urgency so high-value data gets processed, stored, and surfaced first.

How Data Optimization Powers Observability at Scale

Data optimization powers observability at scale by making it possible to ingest, process, store, and query massive volumes of telemetry data without drowning teams in costs, noise, or latency.

At large scale, observability systems face two competing realities:

• Data volumes explode as systems, users, and integrations grow.
• Teams still need fast, accurate, actionable insights to detect and resolve issues.

Data optimization is the bridge that makes both possible.

When systems scale, noise grows faster than a useful signal. Optimization techniques - like deduplication, anomaly detection, and alert suppression - ensure teams see what matters most.

At scale, slow queries kill incident resolution. Optimization - through indexing, schema tuning, and caching - keeps queries and dashboards responsive even when datasets are huge.

Data optimization isn’t just a cost-saving measure; it’s a core enabler of observability at scale. Without it, large-scale observability collapses under its own weight.
With it, organizations can monitor more systems, at higher fidelity, for longer periods, without losing speed or clarity.

Telemetry Volume Challenges

At scale, raw observability data can become overwhelming: Without optimization, every event, log line, and metric is ingested at full fidelity: costs spike, queries slow, and alert fatigue sets in. With optimization, sampling, filtering, and aggregation ensure only high-value data flows through high-cost storage and processing layers.

Downstream Tooling Cost Implications

Storing and querying petabytes of telemetry without optimization is unsustainable.
Key cost-control strategies include tiered storage, retention policies and compression. As a result teams can scale observability coverage across hundreds of services without multiplying infrastructure and SaaS costs.

Data Shaping Before Ingestion

Data shaping before ingestion is one of the most important levers in how data optimization powers observability at scale, because it’s the earliest point in the telemetry lifecycle where you can control volume, quality, and relevance before that data consumes expensive pipeline, storage, and query resources.

Think of it as quality control at the factory door - the earlier you act, the cheaper and more effective the optimization is.

How Mezmo Helps Teams Optimize Their Observability Data

Mezmo helps teams optimize their observability data by acting as a centralized, intelligent telemetry pipeline that can shape, route, enrich, and control log, metric, and trace data before it reaches expensive storage and analytics backends. This lets organizations maintain deep visibility into their systems without drowning in cost, noise, or latency.

Mezmo optimizes observability data by giving teams granular control over what data they collect, where it goes, how it’s enriched, and how long it’s kept. This keeps observability scalable, cost-effective, and actionable, even as telemetry volumes grow exponentially.

Smart Pipelines: Filter, Shape, and Route

Mezmo offers data shaping at the source in several ways including:

• Filtering – Drop irrelevant logs, redundant events, or verbose debug output before ingestion.
• Sampling – Keep only representative traces or metrics while retaining 100% of critical errors.
• Field redaction and masking – Remove sensitive or unnecessary fields to reduce payload size and improve compliance.
• Aggregation – Roll up high-frequency data (e.g., metrics from 1s to 1m intervals).

Teams also get unified pipeline management with central policy control, observability data governance and an integration ecosystem for reduced operational complexity and consistency across the observability stack. And intelligent routing offers multi-destination support, dynamic rules and load balancing.

Live Previews and Real-Time Feedback Loops

Teams maximize efficiency with live previews and real-time feedback loops supported by burst handling, incident mode and noise suppression. Real-time data enrichment makes telemetry more actionable and correlation-ready without requiring costly post-processing.

Cost Controls with Precision Delivery

Observability can be affordable and sustainable over the long term with tactical retention policies, compression and tiered storage that keeps recent high-value data “hot” and moves older data to less expensive “cold” storage.

Common Pitfalls and Challenges to Watch For

Data optimization is a balancing act: done right, it powers cost-effective, scalable, and high-fidelity observability. Done poorly, it can strip away visibility, slow incident response, and undermine trust in the data.

Over-filtering and Signal Loss

If teams aggressively drop logs, metrics, or traces to save on storage/ingestion costs, they  may lose critical diagnostic data, making incident analysis incomplete or impossible. Pay attention to gaps in historical data during postmortems, “can’t reproduce” situations, or dashboards missing spikes.

Tooling Overhead

If the optimization pipeline becomes so complex that no one fully understands it, changes become risky, debugging the pipeline is slow, and optimization errors cause data loss. Make sure more than one person “knows how this works” on any given team.

Complexity Without Clear Governance

When teams apply the same filtering rules to all data, treating every log or metric equally, different data types have different operational value; debug logs aren’t as important as error logs, but security events are mission-critical. And if storage and volume are optimized but compliance, retention, or sensitive field masking are forgotten about, GDPR, HIPAA, or internal retention policies can be violated, leading to legal and reputational risks.

Examples and Use Cases of Data Optimization in Action

High-Cardinality Logging

Here’s an example that shows how data optimization works in a high-cardinality logging scenario, something that can easily cripple an observability system at scale if left unchecked.

A multi-tenant SaaS platform logs authentication events like this:

json

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{

  "timestamp": "2025-08-11T12:35:42Z",

  "user_id": "user_783291",

  "tenant_id": "tenant_1245",

  "ip_address": "203.0.113.54",

  "session_id": "sess_891273",

  "auth_status": "success",

  "latency_ms": 85

}

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Problem: High Cardinality

• Fields like user_id, session_id, and ip_address create millions of unique combinations over time.
• If logs are ingested as-is:
  
  • Index sizes explode.
  • Search performance slows down.
  • Costs skyrocket in systems like Elasticsearch, Datadog, or Splunk, which bill on ingested data volume & cardinality.

Data Optimization Approach

1. Field Redaction & Hashing

• Before ingestion, remove or transform high-cardinality fields that aren’t needed for most queries.
• Keep aggregatable fields (like tenant_id and auth_status) and replace others with anonymized or bucketed values.

json

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{

  "timestamp": "2025-08-11T12:35:42Z",

  "tenant_id": "tenant_1245",

  "auth_status": "success",

  "latency_ms": 85,

  "user_id_hash": "a3f2c1"

}

• session_id dropped entirely.
• user_id hashed (for correlation when needed, but not increasing cardinality for dashboards).
• ip_address masked or bucketed by subnet (203.0.113.x).

2. Event Aggregation

• Aggregate at the pipeline level to reduce row count.
• Instead of storing every single login event, store per-minute aggregates:

json

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{

  "timestamp": "2025-08-11T12:35:00Z",

  "tenant_id": "tenant_1245",

  "auth_status": "success",

  "count": 157,

  "avg_latency_ms": 93

}

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• This massively reduces storage volume and index size while keeping operational insight.

3. Intelligent Routing

• High-cardinality raw logs → sent to cheaper cold storage for rare forensic use.
• Aggregated logs → sent to the main observability backend for dashboards and alerting.

Outcome

• Storage cost reduction: 60–80% lower ingestion volume in primary observability tools.
• Query speed improvement: Faster dashboards since indexes contain fewer unique field combinations.
• Visibility maintained: Raw data still exists in cheaper storage if deep investigation is needed.

Kubernetes Observability

Here’s a practical data optimization example for Kubernetes observability, where telemetry volumes can spiral out of control because every pod, container, and node generates logs, metrics, and events.

A Kubernetes cluster runs hundreds of microservices across multiple namespaces. The observability pipeline ingests:

• Container logs (stdout/stderr from every pod)
• Cluster events (e.g., pod scheduling, scaling, failures)
• Metrics from kubelet, kube-proxy, and custom app exporters
• Traces from service-to-service calls

Problem

• High cardinality from pod names (cart-service-7d8c9f4b7b-xyz12) and container IDs.
• Huge ingestion volume from chatty containers that log every HTTP request at INFO level.
• Noise from routine Kubernetes events (e.g., Pulling image, Created container) that add no diagnostic value.
• Costs are spiking, and queries in the observability tool are slowing.

Data Optimization Approach

1. Field Normalization & Reduction

• Strip pod-specific suffixes from pod names to reduce index cardinality.
• Keep service name and namespace as primary identifiers instead of unique pod/container IDs.

Before:

json

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"pod_name": "cart-service-7d8c9f4b7b-xyz12"

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After:

json

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"service_name": "cart-service",

"namespace": "production"

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2. Log Level Filtering

• Drop all DEBUG and most INFO logs in production.
• Retain ERROR, WARN, and critical business logs.
• Route debug logs to low-cost cold storage (e.g., S3) for forensic retrieval.

3. Event Deduplication & Suppression

• Collapse repeating Kubernetes events that occur within a short window.
• Example: Instead of storing 200 Back-off restarting failed container events in 5 minutes, store:
  
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json

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{

  "event_type": "BackOff",

  "count": 200,

  "time_window_sec": 300,

  "pod": "checkout-service"

}

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4. Metric Aggregation

• Aggregate high-frequency metrics before ingestion into the main time-series DB:
  
  • Roll up CPU/memory usage from per-second to per-minute intervals.
  • Aggregate metrics at the service or namespace level instead of per pod.

5. Intelligent Routing

• Critical application error logs → high-speed indexed storage for dashboards/alerts.
• Routine cluster lifecycle events → cheap cold archive.
• Aggregated metrics → Prometheus or other TSDB.
• Raw traces → kept only for high-latency or error transactions.

Outcome

• 50–70% reduction in ingestion volume to primary observability backend.
• Faster dashboard queries (fewer unique label combinations in metrics).
• Lower storage and compute costs while retaining visibility for incident response.
• Engineers see fewer irrelevant Kubernetes events, improving MTTR.

Security Data Enrichment

Here’s a concrete data optimization example in the context of security data enrichment, where the goal is to enhance raw security logs for better detection and response without blowing up storage costs or slowing queries.

A SOC (Security Operations Center) ingests security telemetry from:

• Firewall logs
• IDS/IPS alerts
• Endpoint detection systems
• Cloud audit logs
  Raw data looks like this:
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json

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{

  "timestamp": "2025-08-11T14:22:38Z",

  "src_ip": "203.0.113.54",

  "dst_ip": "198.51.100.72",

  "port": 443,

  "event_type": "connection_attempt",

  "result": "allowed"

}

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Problem

• Raw logs have low context—analysts must pivot across multiple tools for IP reputation, geo-location, or asset details.
• Adding full enrichment at ingestion for every event creates:
  
  • High cardinality (e.g., full WHOIS data per IP).
  • Excessive storage costs from large enriched payloads.
  • Longer ingestion times.

Data Optimization Approach

1. Targeted, On-Demand Enrichment

• Instead of enriching every event, apply enrichment only for:
  
  • High-risk IPs (from threat intel feeds).
  • Failed authentication attempts.
  • Events matching sensitive asset tags.

Example:
Event passes through filter → Matches “high-risk” criteria → Enrich with:

json

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"geo_location": { "country": "RU", "city": "Moscow" },

"threat_score": 92,

"threat_category": "Botnet"

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Non-critical events → stored without enrichment to save space.

2. Field Pruning in Enrichment

• Drop verbose enrichment fields that aren’t needed for detection or alerting (e.g., full WHOIS records, redundant ASN descriptions).
• Keep compact, high-value indicators:

json

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"asn": "AS12345",

"country": "RU",

"threat_score": 92

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3. Enrichment Caching

• Cache lookups for repeated IPs, domains, or hashes within a short window (e.g., 1 hour) to avoid redundant API calls and duplicated payloads.
• Instead of enriching the same IP 500 times, enrich once and tag all related events.

4. Tiered Routing of Enriched Data

• High-risk enriched events → send to SIEM for immediate alerting and investigation.
• Low-risk enriched events → store in cheaper cold storage for audit purposes.

Outcome

• 40–60% reduction in storage footprint for security logs.
• Enriched data focused on high-value alerts, improving SOC analyst efficiency.
• Faster SIEM queries due to lower cardinality and reduced payload sizes.
• Reduced API lookup costs by caching enrichment results.

Conclusion: A Data Strategy for Better Observability

To achieve better observability it’s critical to have a data strategy in place. The right data, in the right format, in the right place and at the right time keeps observability scalable, actionable, and cost-effective.

Collect data intentionally, shape it early, optimize for cost and performance, route intelligently, and finally review and adapt.

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