Hello there! Curious how we evaluated metadata tools for a Natural Language to SQL system? Join us as we take you behind the scenes at IdentifYou Technologies, where we compared four leading metadata management platforms to find the best fit for powering our general-purpose NL2SQL solution. Let’s dive in!
1. Revisiting Our Artifacts
As part of this project, I created two key presentations that highlight the major milestones in our journey. Revisiting them now not only brings back how far we’ve come—it also sets the perfect stage for the tool evaluation ahead. Ready to walk through it with us?
Summary of Building the NL2SQL Model
- Architecture Highlights:
We broke down the flow from user query to SQL: preparing the schema context, invoking the NLP parser, constructing safe SQL, then executing and returning results. - Early Metadata Thoughts:
We toyed with using OpenMetadata for schema storage and exploration, learning its basics and envisioning how it would fit into our pipeline.
NL2SQL Model – Understanding & Approach
- Schema Awareness Options:
Should we embed the schema as a string inside prompts or query the database’s INFORMATION_SCHEMA dynamically? We weighed pros and cons. - Metadata Generation Techniques:
We surveyed methods—from AI-driven taggers to enterprise catalogues and open-source platforms—to generate and maintain useful metadata. - Tool Category Overview:
We identified four broad categories of metadata solutions: introspection via AI models, traditional data catalogues, lineage-specialized tools, and full-fledged open-source frameworks.
These artifacts taught us where metadata fits into NL2SQL. Next, let’s define what we really needed from a metadata tool.
2. Our Scenario & Must-Have Requirements
Imagine you’re building a flexible NL2SQL service that teams across different departments can use. Here’s what we considered essential:
- Seamless Ingestion:
Automatically pull in database schemas and data lineage from sources like MySQL, PostgreSQL, and Kafka—no manual CSV imports. - Rich APIs:
Offer REST or gRPC endpoints so our NL2SQL inference engine can fetch up-to-date metadata on the fly. - Friendly UI:
Give data stewards a polished interface to search, tag, and edit metadata without writing code. - Enterprise Scale & Governance:
Support multiple teams, role-based access control (RBAC), and high availability so production workloads stay smooth.
With these requirements in mind, we shortlisted four contenders.
3. The Contenders: Four Open-Source Platforms
- OpenMetadata:
A growing project with polished Docker and Kubernetes deployments. - Amundsen:
Born at Lyft; known for its simplicity and search-first approach. - DataHub:
LinkedIn’s answer to enterprise metadata; rich connectors and strong governance. - Apache Atlas:
The veteran player, deeply integrated into Hadoop ecosystems.
4. How We Judged Them: Evaluation Criteria
To keep things fair, we used six clear criteria
| Criteria | What We Looked For |
|---|---|
| Deployment & Setup | How many steps? Docker support? Kubernetes Helm chart availability? |
| Ingestion & Lineage | Built-in connectors? Accuracy and depth of lineage graphs? |
| User Experience | Is the UI intuitive? Can a steward easily search and edit metadata? |
| Integration & Extensibility | Are there SDKs or APIs? How easy to write a custom connector or plugin? |
| Community & Documentation | Activity on GitHub, community Slack/Discourse, clarity of tutorials and examples. |
| Scalability & Governance | Multi-tenant support, RBAC, high availability, monitoring tools. |
5. Side-by-Side Comparison
5.1 Deployment & Setup
- OpenMetadata:
Get started in minutes with docker-compose up or a Helm chart—just install Java and Python first. - Amundsen:
You’ll need Redis and Neo4j instances plus metadata, search, and frontend services. More pieces but manageable. - DataHub:
A single binary or Docker Compose setup—moderate configs but solid defaults. - Apache Atlas:
Java-only, heavyweight—typically runs on Hadoop clusters, so not as lightweight.
5.2 Ingestion & Lineage
| Tool | Connectors | Lineage |
|---|---|---|
| OpenMetadata | MySQL, Postgres, Kafka, Snowflake, etc. | Automatic lineage pipelines |
| Amundsen | Hive, RDS, Redshift | Basic lineage graphs |
| DataHub | JDBC, Kafka, Spark, Kubernetes | Golden dataset lineage; schema versioning |
| Apache Atlas | Hadoop ecosystem (Sqoop), custom JDBC | Deep Hadoop lineage; security-focused |
5.3 User Experience & APIs
- OpenMetadata:
Sleek React UI with search box, lineage graph, tag management; REST API plus Python SDK. - Amundsen:
Clean Flask+React UI—search bar front and center; ingestion via Python library; REST endpoints. - DataHub:
Modern React UI, customizable homepages; gRPC & REST APIs; Java/Python clients. - Apache Atlas:
Classic Apache UI with graph views; REST endpoints; Java extension points.
5.4 Community & Governance
- OpenMetadata:
Active Slack community, bi-weekly releases, detailed docs with examples. - Amundsen:
Backed by Lyft; moderate update cadence; community contributions. - DataHub:
Fast-growing LinkedIn-backed project; vibrant Slack, extensive tutorials. - Apache Atlas:
Long-standing Apache project; slower but stable releases; Hadoop-centric docs.
6. Framing Real-World Scenarios
Instead of dumping logs, we turned problems into “situations” and outlined quick checks and fixes:
- Scenario A: Docker image pull hangs in production
Check: DNS resolution, Docker daemon settings, network throttling.
Fix: Use alternate DNS servers, tweak –max-concurrent-downloads, enable pull retries. - Scenario B: Lineage graph chokes on complex joins
Check: Default graph depth, UI timeout thresholds, ingestion frequency.
Fix: Limit lineage depth in UI, pre-generate flattened graphs via API, optimize ingestion schedule.
7. Final Recommendations: Picking the Right Tool
| Tool | Ideal For | Why It Shines |
|---|---|---|
| OpenMetadata | Rapid prototyping & broad ingestion support | Lightning-fast setup, rich connectors, great API coverage |
| Amundsen | Small-to-medium teams focused on search | Minimal dependencies, super-simple UI |
| DataHub | Enterprise-scale, multi-cloud governance | Deep connector set, robust RBAC, scalable |
| Apache Atlas | Hadoop ecosystems & strict security requirements | Native Hadoop lineage, fine-grained access control |
8. Pros & Cons of Our Analysis
Let’s take a closer look at what worked well and where we should stay cautious:
Pros
- Clear, Criteria-Driven Approach
By breaking down our evaluation into six transparent criteria, we made the comparison objective and easy to follow. - Engaging, Scenario-Based Storytelling
Rather than drowning readers in logs or error dumps, we framed challenges as real-world situations—making the content approachable and memorable. - Artifact Integration
Referencing our own presentations and prototypes lent authenticity and showed how practical experience informed our decisions. - Balanced Scope
We covered both lightweight and enterprise-grade tools, so readers from different contexts can find a suitable recommendation.
Cons
- Qualitative Over Quantitative
We focused on descriptive insights—performance benchmarks (e.g., ingestion throughput, UI render times) would add more rigor. - Tool Evolution
Open-source projects frequently release new features. Our analysis represents a snapshot; ongoing reassessment is essential. - Environment Variability
Factors like network latency, on-prem vs. cloud setup, and database size can affect tool behavior in ways not captured here. - Feature Overlap
Some tools blur lines between categories (e.g., DataHub’s lineage vs. catalog capabilities), which can complicate direct comparisons.
9. Key Takeaways
Here are the lessons I’ll carry forward—and hope you find useful, too:
- Start with Your Artifacts:
Prototype slides, blogs, and demos aren’t just record-keeping—they guide your evaluation criteria and ground decisions in real work. - Speak in Scenarios, Not Errors:
Framing tech challenges as “situations” helps non-technical readers grasp the impact and the solution without needing to digest log files. - Mix Qualitative and Quantitative:
Pair objective criteria with key metrics—like average ingestion time or API response benchmarks—to strengthen your case. - Plan for Change:
Metadata platforms evolve quickly. Schedule periodic re-evaluations (every 6–12 months) to ensure your chosen tool still meets emerging requirements. - Balance Simplicity and Scale:
Lightweight tools may speed up prototyping, but enterprise demands often require deeper governance features—pick what fits your growth stage.
Thanks again for joining me on this metadata tool adventure! Happy exploring!
Author — Debanjana Sur
