What Is an Observability Data Lake? The Definitive Guide

Every engineering team hits the same wall. Your Datadog bill crosses six figures, your retention windows shrink to stay within budget, and the telemetry you actually need during an incident was already aged out. The observability data lake is the architectural pattern that breaks this cycle — storing all your logs, metrics, and traces on cheap object storage in open formats, so you never have to choose between cost and visibility.

This guide covers what an observability data lake is, how it differs from traditional monitoring stacks, why the industry is moving toward it, and how to evaluate whether it's right for your organization.

The Problem: Observability Data Is Growing Faster Than Budgets

Modern distributed systems generate staggering volumes of telemetry. A mid-sized Kubernetes cluster running 200 microservices can easily produce 5-10 TB of logs per day. Add metrics, traces, and events, and you're looking at petabyte-scale data within months.

Traditional observability platforms — Datadog, New Relic, Splunk, Elastic Cloud — charge based on ingestion volume or host count. At scale, this model creates perverse incentives:

• Sampling and dropping data to control costs, which means you're blind during incidents
• Short retention windows (7-15 days) because long-term storage is prohibitively expensive
• Vendor lock-in through proprietary data formats that make migration painful
• Budget unpredictability where a traffic spike or a verbose deployment can blow through spend limits overnight

The result? Teams pay more every year for observability while simultaneously seeing less of their data. The observability data lake exists to fix this fundamental mismatch.

What Is an Observability Data Lake?

An observability data lake is an architecture where all telemetry data — logs, metrics, traces, and events — is stored on object storage (like Amazon S3, Google Cloud Storage, or Azure Blob Storage) in open, columnar file formats like Apache Parquet.

Rather than shipping your data to a SaaS vendor's infrastructure, an observability data lake keeps data in storage you own and control. A query engine sits on top to provide the search, correlation, and alerting capabilities that engineers need during day-to-day operations.

The core components are:

1. Ingestion Layer

Telemetry flows in through standard protocols — primarily OpenTelemetry (OTel) via the OTLP endpoint, but also Syslog, FluentBit, and HTTP APIs. The ingestion layer handles parsing, enrichment, and buffering before writing to storage.

2. Object Storage (The "Lake")

This is the foundation. Object stores like S3, MinIO, GCS, or Azure Blob provide virtually unlimited capacity at a fraction of the cost of block storage or SSD-backed databases. Typical object storage costs range from $0.02-0.03/GB/month, compared to $0.10-0.30/GB/month for database-attached storage.

3. Open File Format

Data is stored in Apache Parquet — a columnar, compressed format originally developed for the Hadoop ecosystem and now the standard for analytical workloads. Parquet gives you:

• 70-90% compression compared to raw JSON logs
• Column pruning — queries only read the columns they need
• Predicate pushdown — filters are applied at the storage layer
• Universal compatibility — readable by Spark, DuckDB, Athena, Presto, and dozens of other tools

4. Query Engine

A purpose-built OLAP (Online Analytical Processing) engine provides fast, interactive queries over the stored data. This is where observability-specific features like full-text search, log correlation, trace visualization, and alerting live.

5. Catalog and Metadata

A metadata layer tracks what data exists, where it's stored, partitioning schemes, and schema evolution. This enables efficient query planning without scanning every file.

How an Observability Data Lake Differs from Traditional Monitoring

The distinction isn't just about where data lives — it's about the entire operational model.

The most significant shift is economic. When storage and compute are decoupled — and storage is object-store cheap — the calculus around data retention changes completely. Instead of asking "how much data can we afford to keep?", teams ask "what data do we actually need to delete?"

The Architecture: How It Works in Practice

Here's what a typical observability data lake deployment looks like:

┌─────────────────────────────────────────────┐
│           Applications & Infrastructure      │
│    (K8s pods, VMs, serverless, databases)    │
└──────────────────┬──────────────────────────┘
                   │ OTel SDK / Collectors
                   ▼
┌─────────────────────────────────────────────┐
│            Ingestion Layer                   │
│   (OTLP endpoint, parsers, enrichment)      │
└──────────────────┬──────────────────────────┘
                   │ Buffered writes
                   ▼
┌─────────────────────────────────────────────┐
│         Object Storage (S3 / MinIO / GCS)   │
│    ┌─────────┐ ┌─────────┐ ┌─────────┐     │
│    │ Parquet │ │ Parquet │ │ Parquet │     │
│    │ (logs)  │ │(metrics)│ │(traces) │     │
│    └─────────┘ └─────────┘ └─────────┘     │
└──────────────────┬──────────────────────────┘
                   │
        ┌──────────┼──────────┐
        ▼          ▼          ▼
   ┌─────────┐ ┌────────┐ ┌────────┐
   │ Query   │ │ Spark/ │ │ Athena/│
   │ Engine  │ │ DuckDB │ │ Presto │
   └─────────┘ └────────┘ └────────┘

The key architectural insight is separation of storage and compute. Your telemetry lands in object storage once and can be queried by multiple engines for different purposes:

• Interactive queries via the built-in query engine for incident response
• Batch analytics via Spark or DuckDB for capacity planning and trend analysis
• Ad-hoc exploration via Athena or Presto for cross-cutting investigations
• ML pipelines that read directly from the same Parquet files for anomaly detection

This is impossible with traditional monitoring tools, where data is locked inside a proprietary system.

Why the Industry Is Moving Toward Observability Data Lakes

Three converging trends are driving adoption:

1. Telemetry Volume Is Exploding

The shift to microservices, containers, and serverless has multiplied the number of telemetry sources by 10-100x compared to monolithic architectures. OpenTelemetry auto-instrumentation makes it trivial to generate traces and metrics for every service call. The volume isn't going down.

At traditional per-GB pricing, this growth is unsustainable. Teams are already spending 30-40% of their cloud infrastructure budget on observability tools. An observability data lake brings storage costs down by 10-50x, making it feasible to keep everything.

2. OpenTelemetry Has Won the Standards War

OpenTelemetry is now the second most active CNCF project after Kubernetes. It provides a vendor-neutral way to instrument applications and ship telemetry. With OTel, the ingestion layer is standardized — what differs between platforms is storage and query.

This standardization means switching your backend no longer requires re-instrumenting every application. It also means you can route the same telemetry to multiple destinations: a data lake for long-term storage and a hot tier for real-time alerting.

3. Apache Parquet Became the Universal Analytical Format

Parquet went from a Hadoop-era format to the lingua franca of data analytics. Every major cloud data warehouse (BigQuery, Redshift, Snowflake) reads Parquet natively. Every major processing engine (Spark, Flink, DuckDB) writes and reads Parquet. Choosing Parquet for observability means your telemetry is instantly compatible with your existing data stack.

The Economics: A Concrete Example

Let's make this tangible. Consider a team ingesting 5 TB/day of logs with 90-day retention.

Traditional SaaS (Datadog Log Management)

• Ingestion: 5 TB/day × 30 days × ~$0.10/GB = $15,000/month (on-demand pricing)
• Retention beyond 15 days requires additional fees
• Total annual: $180,000+ (and that's just logs)

Observability Data Lake (Parseable Cloud)

• Ingestion: 5 TB/day × 30 days = 150 TB/month = 150,000 GB/month
• Parseable Cloud pricing: 150,000 GB × $0.39/GB = $58,500/month at list price
• This includes storage, 365 days of retention, query scanning (up to 10x monthly ingestion), dashboards, alerts, and AI-native analysis — no separate storage or compute charges
• 90 days of retention is well within the included 365-day window, so there's no additional retention cost

At higher volumes, contact sales for volume discounts that can bring costs down significantly. The Enterprise plan with BYOB is also an option for teams that want to manage their own storage — in that case, you pay separately for S3 storage (typically ~$0.023/GB/month) but gain full bucket-level data control and unlimited retention.

The real savings compound over time. With Parseable Cloud, keeping 12 months of searchable data is included in the base price. With SaaS tools, every additional month of retention costs extra.

What an Observability Data Lake Is NOT

Let's clear up common misconceptions:

It's not "just S3 with grep." A raw dump of JSON files on S3 is useless for incident response. An observability data lake includes a purpose-built query engine, indexing, alerting, and dashboarding — all the operational features engineers need. The difference is where the data lives and in what format.

It's not incompatible with real-time alerting. A well-designed observability data lake maintains a hot tier for recent data (minutes to hours) that serves real-time queries and alerts, while the bulk of historical data lives on object storage. You're not sacrificing speed for cost.

It's not just for large enterprises. The economics work at any scale. A startup ingesting 100 GB/day benefits from predictable pricing and data ownership just as much as a Fortune 500 company. In fact, startups may benefit more because they can't afford to be locked into a vendor's pricing model as they grow.

It's not a data swamp. Structure matters. Observability data lakes use schema-on-write (data is structured into Parquet at ingestion time), not schema-on-read. The data is clean, typed, and queryable from the moment it lands.

Evaluating an Observability Data Lake: What to Look For

If you're considering moving to a data lake architecture for observability, here are the key criteria:

Data Ownership

Does the platform store data in your cloud account (Bring Your Own Bucket), or does it copy data to the vendor's infrastructure? True data ownership means your Parquet files sit in an S3 bucket you control, with your IAM policies, your encryption keys, and your compliance posture.

Open Formats

Can you query the stored data with tools other than the vendor's query engine? If the answer is no, you've just traded one form of lock-in for another. Insist on standard Apache Parquet — not a "Parquet-compatible" proprietary variant.

Operational Simplicity

How many components do you need to deploy and manage? Some observability data lakes require separate clusters for ingestion, storage, metadata cataloging, and querying. Look for platforms that minimize operational overhead — ideally a single binary or managed service.

Query Performance

Object storage is high-throughput but high-latency compared to local SSDs. The query engine must compensate with aggressive caching, predicate pushdown, and intelligent partitioning. Test with your actual query patterns: full-text search across 1 billion log lines, trace reconstruction across distributed services, and metric aggregation over 30-day windows.

OpenTelemetry Support

Native OTLP ingestion should be non-negotiable. Bonus points for supporting OTel semantic conventions natively, so fields like service.name, http.status_code, and k8s.pod.name are first-class citizens in the query interface.

Pricing Transparency

The whole point of a data lake architecture is cost predictability. If the platform charges separately for ingestion, storage, compute, queries, and alerting, you're back to the same bill-shock problem. Look for all-inclusive per-GB pricing with no hidden multipliers.

Where Parseable Fits

Parseable is an observability data lake platform purpose-built for this architecture. Here's how it maps to the criteria above:

• Data ownership: Parseable offers two deployment models. Parseable Cloud (Pro plan) is a fully managed service with multi-tenant infrastructure — no setup, no maintenance. For teams that need full data control, the Enterprise plan includes Bring Your Own Bucket (BYOB), where your data stays in your own S3, GCS, or Azure Blob account.
• Open format: All telemetry is stored in Apache Parquet. On Enterprise with BYOB, you can also query your data directly with external tools like Spark, DuckDB, or Athena — since the Parquet files live in your own bucket.
• Operational simplicity: Single binary for self-hosted deployments. No ZooKeeper, no Kafka, no separate metadata services. Choose from Parseable Cloud, BYOC (Bring Your Own Cloud), or self-hosted.
• Query performance: ParseableDB, built on Apache Arrow DataFusion, is a purpose-built OLAP engine optimized for observability query patterns — full-text search, time-range filtering, and high-cardinality field analysis.
• OpenTelemetry native: Built-in OTLP endpoint for logs, metrics, and traces. Works with any OTel Collector or SDK.
• Predictable pricing: $0.39/GB ingested on the Pro plan (Parseable Cloud). Enterprise deployments offer further savings: $0.25/GB for BYOC and $0.20/GB for self-hosted. Query scanning is included up to 10x of your monthly ingestion volume, with additional scans at $0.02/GB. No compute surcharges, no egress charges.
• Pro plan highlights: 365 days retention included, 99.9% uptime SLA, AI-native analysis, anomaly detection, unlimited users, dashboards, alerts, and full API access. 14-day free trial available.
• Enterprise extras: Everything in Pro, plus BYOB for unlimited retention, Apache Iceberg support, premium support, and flexible deployment and data residency options.

Getting Started

If you want to try the observability data lake approach, here's a minimal path:

1. Instrument with OpenTelemetry

If you're not already using OTel, start by deploying the OpenTelemetry Collector in your Kubernetes cluster. It collects logs, metrics, and traces from all your workloads with minimal configuration.

2. Point the Collector at Parseable

Configure the OTel Collector to export via OTLP to Parseable's endpoint:

exporters:
  otlphttp:
    endpoint: "https://your-parseable-instance:8000/v1"
    headers:
      Authorization: "Basic <base64-encoded-credentials>"
 
service:
  pipelines:
    logs:
      exporters: [otlphttp]
    traces:
      exporters: [otlphttp]
    metrics:
      exporters: [otlphttp]

3. Query Your Data

Use Prism (Parseable's web UI) or the API to search, filter, and analyze your telemetry. On the Enterprise plan with BYOB, you also have direct access to the Parquet files in your own bucket, which means you can point external tools like DuckDB or Spark at the same data for deeper analysis.

-- Enterprise BYOB: Query directly from your S3 bucket with DuckDB
SELECT
  service_name,
  count(*) as error_count
FROM read_parquet('s3://your-bucket/logs/2026/02/**/*.parquet')
WHERE severity = 'ERROR'
  AND timestamp > now() - INTERVAL 1 HOUR
GROUP BY service_name
ORDER BY error_count DESC;

What's Next

The observability data lake is not a future concept — teams are running this architecture in production today. The convergence of OpenTelemetry, Apache Parquet, and cheap object storage has made it practical at any scale.

If you're evaluating this approach, the rest of this series dives deeper into specific aspects:

• The True Cost of Observability: Why $0.03/GB Is Never $0.03/GB — A detailed breakdown of how observability pricing actually works, and why headline rates are misleading.
• Why Your Observability Data Should Live in Apache Parquet — A deep dive into why Parquet is the right format for telemetry data.
• Data Lake vs. Data Warehouse for Observability — How to choose between lake, warehouse, and lakehouse architectures.
• Bring Your Own Bucket: Data Ownership in Observability — Why data residency and ownership matter more than you think.
• Building an Observability Lakehouse with OpenTelemetry — A hands-on guide to building this architecture from scratch.

The bottom line: your observability data is one of your most valuable operational assets. It deserves an architecture that gives you full control over it — not one that holds it hostage behind a vendor's paywall.

Ready to try the observability data lake approach? Start your free 14-day trial on Parseable Cloud — $0.39/GB ingested with 365 days retention and AI-native analysis included. For Enterprise needs including BYOB, Iceberg support, and flexible deployment options, contact the team.