whylogs
whylogs is an open-source data logging library that captures statistical profiles of datasets to detect data quality issues, drift, and performance degradation in ML systems. It provides efficient, mergeable summaries of data distributions and enables constraint-based validation without storing raw data.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | whylabs/whylogs |
| Owner | whylabs |
| Primary language | Jupyter Notebook |
| License | Apache-2.0 — OSI-approved |
| Stars | 2.8k |
| Forks | 143 |
| Open issues | 4 |
| Latest release | v1.6.4 (2024-12-03) |
| Last updated | 2025-01-10 |
| Source | https://github.com/whylabs/whylogs |
What whylogs is
whylogs generates compact, mergeable statistical profiles from dataframes and complex data types, supporting custom metrics, data constraints, and visualization. Profiles can be locally analyzed or sent to the WhyLabs SaaS platform for automated ML monitoring and observability.
Get the whylogs source
Clone the repository and explore it locally.
git clone https://github.com/whylabs/whylogs.gitcd whylogs# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Profiles are mergeable but not queryable like a database; plan storage and retrieval strategy for historical profiles (local, cloud, or WhyLabs).
- Customizable metrics allow tracking domain-specific statistics but require upfront definition of constraints and KPIs.
- Performance depends on dataset size and column cardinality; approximate statistics and sampling strategies can optimize for large-scale data.
- Integration with WhyLabs unlocks alerting and dashboards; decide early whether standalone profiling or managed monitoring aligns with operational model.
- Slack community available but enterprise support / SLAs are Unknown; review WhyLabs commercial terms if production monitoring is required.
When to avoid it — and what to weigh
- Need Row-Level Data Access — whylogs profiles are summaries; individual records are not stored. If you need to inspect or replay raw data, use a data lake or data warehouse.
- Require Real-Time Sub-Second Latency — Profile generation adds computational overhead. For ultra-low-latency streaming (sub-100ms), evaluate lightweight alternatives or pre-aggregation strategies.
- Exclusively Java/Non-Python Environments — Primary language is Jupyter Notebook/Python; Java support exists but may lag feature parity. Multi-language teams should verify integration depth.
- Need Offline, Air-Gapped Deployment — WhyLabs SaaS integration is a core feature; using whylogs in standalone mode is possible but reduces value. Evaluate dependency on cloud connectivity.
License & commercial use
Apache License 2.0 (Apache-2.0) is a permissive OSI-approved open-source license. It allows use in commercial products, with no copyleft requirement, but requires retention of license notices and provides no warranty.
Apache-2.0 permits commercial deployment without royalties or restrictions on derived works. However, WhyLabs integration (core feature) relies on a separate SaaS subscription model; review WhyLabs commercial terms separately. Standalone use is unrestricted by license.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Good |
| Assessment confidence | High |
whylogs is privacy-preserving by design—profiles are statistical summaries, not raw data storage, reducing exposure. However: profile export formats, WhyLabs data transmission, and API key management are security touch points—review credential handling and network egress. No independent security audit referenced in provided data.
Alternatives to consider
Great Expectations
Data validation framework with richer constraint definition and integration with data catalogs; more heavyweight if you need full pipeline data contracts.
Evidently
Similar data drift and model monitoring focus, with built-in dashboards; more tightly coupled to model performance metrics, less modular than whylogs.
Fiddler AI
Managed SaaS ML monitoring platform; consider if you want off-the-shelf hosted solution without building on whylogs + WhyLabs.
Build on whylogs with DEV.co software developers
whylogs enables efficient data profiling and drift detection in ML pipelines. Evaluate integration with your data stack, decide on standalone vs. WhyLabs SaaS, and pilot with a single feature.
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whylogs FAQ
Can I use whylogs without WhyLabs?
What data types does whylogs support?
Is whylogs suitable for streaming data?
How large can profiles be, and does size affect performance?
Custom software development services
Adopting whylogs is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source observability software in production.
Ready to Add Data Observability?
whylogs enables efficient data profiling and drift detection in ML pipelines. Evaluate integration with your data stack, decide on standalone vs. WhyLabs SaaS, and pilot with a single feature.