Data Lake vs. Data Warehouse for Observability

Your observability backend is, at its core, a storage and query system. Logs, metrics, and traces flow in. Engineers query them during incidents, capacity planning, and debugging. The architecture you choose for storing and querying that telemetry determines your cost curve, your retention windows, your query performance, and ultimately how much of your own operational data you actually get to keep.

The industry has converged on three architectural patterns for this problem: data warehouses, data lakes, and lakehouses. Each makes different trade-offs. Picking the wrong one locks you into cost structures and operational constraints that compound over years.

This guide breaks down all three architectures specifically for observability workloads, explains where each excels and where each falls short, and helps you decide which approach fits your team. If you want foundational context on the data lake pattern, start with What Is an Observability Data Lake?.

The Three Architectures, Defined

Before comparing trade-offs, let's establish clear definitions. These terms get used loosely in marketing material, so precision matters.

Data Warehouse

A data warehouse is a tightly coupled system where storage and compute are integrated. Data is ingested, indexed, and stored in a proprietary format optimized for fast analytical queries. The system controls the entire lifecycle: how data is written, how it is compressed, how it is indexed, and how it is queried.

In the observability world, ClickHouse is the canonical example. Tools like ClickStack (ClickHouse's observability distribution) and SigNoz use ClickHouse as their storage and query engine. ClickHouse stores data in its own columnar format, manages its own replication, and provides its own SQL dialect for queries.

The warehouse model prioritizes query speed above all else. ClickHouse is exceptionally fast for point queries and aggregations over recent data. It achieves this by maintaining indexes, sort orders, and compression codecs that are tightly optimized for its query engine.

Data Lake

A data lake stores data on object storage (S3, GCS, Azure Blob) in open file formats like Apache Parquet. Storage and compute are fully decoupled. The data sits in your cloud account, in files that any compatible tool can read. A separate query engine provides search, aggregation, and alerting capabilities on top of the stored data.

The data lake model prioritizes cost, openness, and data ownership. Object storage costs $0.02-0.03/GB/month, compared to $0.10-0.30/GB/month for SSD-backed database storage. The open format means no vendor lock-in at the storage layer: if you switch query engines, your data stays exactly where it is, readable by Spark, DuckDB, Athena, Presto, or any other Parquet-compatible tool.

Lakehouse

A lakehouse combines elements of both. Data lives on object storage in open formats (like a data lake), but the system adds a metadata and catalog layer that enables warehouse-like features: ACID transactions, schema enforcement, time travel, and efficient upserts. Apache Iceberg is the leading table format for this pattern.

The lakehouse model aims to deliver warehouse-class query performance with data lake economics and openness. It is the newest of the three approaches and arguably the most promising for observability, because it addresses the primary weakness of pure data lakes (lack of transactional guarantees and metadata management) without sacrificing cost efficiency or data portability.

Why Architecture Choice Matters More for Observability

Observability workloads are unusual compared to general analytics. They have specific characteristics that amplify the differences between these architectures:

Write-heavy, append-only. Telemetry is almost entirely append-only. You never update a log line or modify a metric data point after ingestion. This means the complex update and merge machinery of traditional databases is wasted overhead.

Extreme volume. A mid-sized Kubernetes cluster generates 5-10 TB of logs per day. Metrics and traces add more. At these volumes, the per-GB cost of storage is not a rounding error — it is the dominant line item in your observability budget.

Time-series access patterns. The vast majority of observability queries filter by time range first. "Show me errors in the last hour" is far more common than "find every occurrence of this error ID across all time." Architectures that partition and prune by time have a structural advantage.

Long-tail retention needs. Compliance, incident postmortems, and trend analysis require months or years of retention. But 95% of queries touch only the last 24-72 hours of data. You need an architecture that makes recent data fast and historical data cheap, not one that applies the same (expensive) storage tier to everything.

Schema evolution. Telemetry schemas change constantly. New microservices emit new fields. OpenTelemetry semantic conventions evolve. Rigid schemas that require ALTER TABLE operations for every new field are a poor fit.

These characteristics tilt the architectural balance. Let's look at how each approach handles them.

Head-to-Head Comparison

Cost

This is where the architectures diverge most dramatically.

ClickHouse and its derivatives require data to live on locally attached SSDs for fast query performance. You can use tiered storage to offload cold data to S3, but queries against the cold tier are significantly slower — often 10-50x slower than queries against the hot tier. In practice, teams keep 7-30 days on SSDs and either drop older data or accept degraded performance for historical queries.

Data lakes and lakehouses store everything on object storage from the start. The query engine compensates for object storage latency with aggressive caching, predicate pushdown, and intelligent prefetching. The cost advantage is 5-10x for raw storage, and it compounds as retention windows grow.

For a detailed analysis of how observability pricing works in practice, see The True Cost of Observability: Why $0.03/GB Is Never $0.03/GB.

Query Performance

Credit where it is due: ClickHouse is fast. For point queries against recent data — "show me the last 100 errors for service X in the past hour" — a properly tuned ClickHouse cluster will return results in milliseconds. Its MergeTree engine, skip indexes, and aggressive vectorized execution give it an edge for these patterns.

But observability queries are not all point queries. Consider the spectrum:

The data lake/lakehouse approach trades a small penalty on hot-path point queries for consistent performance across all time ranges. When your data is on object storage, querying 90-day-old data is architecturally identical to querying 1-hour-old data. There is no performance cliff when data falls off the SSD tier.

Modern query engines built on Apache Arrow DataFusion (like ParseableDB) close the gap on point queries through in-memory caching of recent data, vectorized execution, and efficient Parquet metadata handling. The difference between "2ms" and "200ms" for an interactive query is irrelevant to an engineer debugging an incident.

Data Ownership and Portability

This is where the architectural differences have the most long-term strategic impact.

With a ClickHouse-based system, your data is locked in ClickHouse's native format. If you want to switch to a different observability platform, you need to export billions of rows, transform them, and re-ingest them into the new system. This is a multi-week migration project even at moderate scale.

With a data lake or lakehouse, switching your query engine does not require moving or transforming any data. The Parquet files (or Iceberg tables) stay exactly where they are. This is a fundamentally different relationship with your observability vendor: they earn your business on query experience and features, not on the inertia of data lock-in.

For organizations in regulated industries (healthcare, finance, government), data ownership is not optional. Storing telemetry on ClickHouse nodes managed by a SaaS vendor may not satisfy data residency requirements. A data lake architecture where Parquet files sit in your own S3 bucket, encrypted with your own KMS keys, addresses this cleanly. Read more in Bring Your Own Bucket: Data Ownership in Observability.

Flexibility and Ecosystem

One of the most underappreciated advantages of the data lake/lakehouse approach is ecosystem compatibility.

When your observability data is stored in Apache Parquet on S3, it becomes part of your broader data infrastructure. Your data engineering team can run Spark jobs against the same telemetry data to build SLO dashboards. Your ML engineers can train anomaly detection models directly on the Parquet files. Your finance team can query cost-per-service metrics using Athena without touching the observability platform at all.

ClickHouse-based systems create a separate data silo. Telemetry lives inside ClickHouse, queryable only through ClickHouse's SQL dialect and APIs. Getting data out requires ETL pipelines, scheduled exports, or ClickHouse-specific connectors. Every additional consumer of the data requires new integration work.

For a deeper dive into why Parquet specifically is the right format for observability, see Why Your Observability Data Should Live in Apache Parquet.

Operational Complexity

Running ClickHouse at production scale is non-trivial. A typical deployment includes:

• Multiple shards for horizontal scaling
• Replicas per shard for high availability
• ZooKeeper or ClickHouse Keeper for distributed coordination
• MergeTree table configurations tuned per workload
• Tiered storage policies for cost management
• Schema migrations that require ALTER TABLE across distributed tables

This is a full-time job for a dedicated infrastructure team. ClickHouse's performance advantages come with operational overhead that is easy to underestimate during a proof-of-concept but painful to absorb in production.

Data lake and lakehouse architectures offload storage reliability to your cloud provider's object storage service, which has its own dedicated operations team, 99.999999999% (11 nines) durability, and zero operational burden on your side. The query engine is the only component you manage, and if it is delivered as a managed service or a single binary, the operational surface area shrinks dramatically.

ClickHouse Observability Tools: A Closer Look

Several prominent observability tools are built on ClickHouse. Understanding their architecture clarifies the trade-offs.

ClickStack

ClickHouse's official observability distribution bundles ClickHouse with OpenTelemetry collectors, Grafana dashboards, and pre-configured schemas for logs, metrics, and traces. It is essentially "ClickHouse, packaged for observability."

ClickStack inherits all of ClickHouse's strengths: fast queries, mature SQL engine, and active community. It also inherits all of ClickHouse's operational requirements: you need to manage shards, replicas, Keeper, and storage tiers yourself. Data is stored in ClickHouse's native format, not in open formats.

SigNoz

SigNoz is an open-source observability platform that uses ClickHouse as its storage backend. It provides a polished UI for logs, metrics, and traces, with native OpenTelemetry support. SigNoz is well-designed and has a growing community.

Like ClickStack, SigNoz's architecture means your observability data lives inside ClickHouse. The cost, portability, and operational trade-offs of the warehouse model apply in full. SigNoz Cloud offers a managed version that reduces operational overhead, but the underlying storage architecture remains the same.

Both tools make a reasonable trade-off for teams that prioritize raw query speed on recent data above all else. But if your priorities include long-term retention, data ownership, or ecosystem flexibility, the warehouse foundation works against you.

For a detailed comparison of the ClickHouse warehouse approach versus the data lake approach, see ClickHouse ClickStack vs. Parseable.

The Lakehouse: The Convergence Point

The observability lakehouse is emerging as the architecture that best balances the competing requirements of modern telemetry workloads. It takes the economic and openness advantages of the data lake and adds the structure and query optimization of the warehouse.

The key enabling technology is Apache Iceberg — a table format that provides:

• ACID transactions on top of object storage, enabling safe concurrent writes
• Schema evolution without rewriting data, critical for evolving telemetry schemas
• Partition evolution to change partitioning strategies without data migration
• Time travel to query data as it existed at any point in the past
• Hidden partitioning that decouples physical data layout from the user-facing schema

With Iceberg, your Parquet files on S3 behave like a structured database table rather than a loose collection of files. You get the query planning advantages of a warehouse (predicate pushdown, file pruning, sort-order optimization) with the economics and openness of a data lake.

For a hands-on guide to building this architecture, see Building an Observability Lakehouse with OpenTelemetry.

Where Parseable Fits

Parseable is built on the data lake and lakehouse architecture from the ground up. It is not a ClickHouse wrapper and not a SaaS black box. Here is how it implements the principles discussed in this guide:

Storage layer. All telemetry is stored in Apache Parquet on object storage. On the Enterprise plan with Bring Your Own Bucket (BYOB), those Parquet files sit in your own S3, GCS, or Azure Blob account — fully under your control, queryable by any compatible tool.

Query engine. ParseableDB is built on Apache Arrow DataFusion, a vectorized OLAP engine designed for Parquet. It delivers fast interactive queries with column pruning, predicate pushdown, and in-memory caching of hot data.

Lakehouse support. The Enterprise plan includes Apache Iceberg support, enabling ACID transactions, schema evolution, and time travel on your observability data.

Ingestion. Native OTLP ingestion for logs, metrics, and traces. Works with any OpenTelemetry Collector or SDK. Also supports Syslog, FluentBit, and HTTP API ingestion.

Operational simplicity. Single binary for self-hosted deployments — no ZooKeeper, no Kafka, no separate metadata services. Also available as Parseable Cloud (fully managed) or BYOC (Bring Your Own Cloud).

Web UI. Prism, Parseable's web interface, provides log exploration, dashboards, alerts, and AI-native analysis — everything you need for day-to-day operations without external tooling.

Parseable Plans

Pro plan ($0.39/GB ingested):

• 365 days retention included
• 99.9% uptime SLA
• AI-native analysis and anomaly detection
• Unlimited users, dashboards, alerts, and API access
• Query scanning included up to 10x of monthly ingestion, additional scans at $0.02/GB
• Runs on Parseable Cloud (shared, multi-tenant infrastructure)
• 14-day free trial

Enterprise plan (custom pricing, starting at $15,000/year; $0.25/GB for BYOC, $0.20/GB for self-hosted):

• Everything in Pro
• BYOB (Bring Your Own Bucket) for full data ownership
• Apache Iceberg support
• Premium support
• Flexible deployment: Parseable Cloud, BYOC, or self-hosted
• Custom data residency and compliance configurations

Decision Framework: Which Architecture Should You Choose?

There is no universal answer, but the following framework maps common team profiles to architectures:

For most teams in 2026, the lakehouse approach offers the best balance. You get cost-effective storage, open formats, data ownership, and query performance that is more than sufficient for observability workflows. The warehouse approach makes sense only if you have specific sub-millisecond latency requirements on the hot path and the operational capacity to run ClickHouse at scale.

The SaaS observability model — where a vendor stores your data in a proprietary format on their infrastructure and charges you per GB — is a fourth option, but one that an increasing number of teams are moving away from. For why, see The Traditional SaaS Pricing Model for Observability Is Broken.

Conclusion

The choice between a data warehouse, data lake, and lakehouse for observability is not abstract. It directly impacts how much you spend, how long you retain data, whether you can switch vendors without a multi-month migration, and whether your observability data can be used beyond the observability platform itself.

ClickHouse-based tools deliver fast point queries and are a reasonable choice when short retention and raw speed are the only priorities. But for teams that need long-term retention, cost predictability, data ownership, and ecosystem flexibility — which is most teams, once they move past the proof-of-concept stage — the data lake and lakehouse architectures are structurally superior.

Parseable implements the lakehouse approach as a production-ready platform: Apache Parquet on object storage, ParseableDB on Arrow DataFusion, native OTLP ingestion, and optional Apache Iceberg support on the Enterprise plan. It gives you warehouse-class query performance without the warehouse-class cost or operational overhead.

Start a free 14-day trial on Parseable Cloud to see the lakehouse approach in practice. Pro is $0.39/GB ingested with 365 days of retention, AI-native analysis, and no vendor lock-in. For Enterprise needs including BYOB and Iceberg support, contact the team.