Database documentation forms the foundation for critical initiatives like onboarding, collaboration, compliance, system maintenance and more. But manually capturing schema details, data flows, metrics and other metadata is tedious and costly.
This comprehensive guide explores the database documentation landscape – from adoption trends to leading solutions. Discover how modern tools can automate the capturing and dissemination of database knowledge to boost productivity.
The High Costs of Poor Database Documentation
92% of IT decision makers rank documentation as very important or crucial for their organization, according to a recent Simple Texting survey. Yet nearly two-thirds report their documentation is lacking.
Why does documentation matter so much? And what pains do teams face without it?
Onboarding & Training Headaches
- 74% of IT managers say onboarding and training new hires is difficult without documentation, wasting over 300 productive hours per new engineer.
Unplanned Downtime
- 90% of unplanned outages stem from poorly understood systems, costing over $260K per incident.
Security & Compliance Risks
- 65% of database-related data breaches tie back to misconfigurations that documentation could have prevented.
Spend on Understanding Systems
- Engineers waste 25-50% of time trying to understand undocumented systems – that’s up to $20K per engineer per year.
Rebuilding Institutional Knowledge
- 500+% 5-year ROI realized from documentation tools via knowledge retained after employee turnover.
The Bottom Line: Spreadsheets and Word docs cannot scale documentation needs in the era of rapidly evolving, complex business systems. Teams need specialized tools.
Core Capabilities of Database Documentation Tools
The most well-rounded documentation solutions on the market enable teams to:
Auto-Generate Technical Docs
- Scan database schema and automatically output architecture diagrams, dictionary definitions, code annotations and more without tedious copy-pasting.
Customize Outputs
- Support flexible documentation formats like HTML, PDF, Markdown and more. Adjust logos, color schemes and text.
Visualize Complex Relationships
- Auto-create detailed entity relationship diagrams, data flows, logic maps and more.
Embed Metadata
- Centralize glossaries, data dictionaries, schema details, metrics and other important metadata.
Update in Real-Time
- Synchronize documentation changes with underlying database changes so information stays current.
Enable Teamwork
- Provide role-based access controls, activity logs, collaboration workflows so multiple people can jointly improve content.
Integrate with Existing Systems
- Offer open API’s, version control system integrations, CI/CD pipeline hooks and extensibility options to connect documentation into broader IT ecosystems.
Leading Database Documentation Solutions
The database documentation landscape ranges from free open source tools to full-scale commercial platforms. Here we analyze the top solutions across the spectrum.
SQLDoc – Simple SQL Server Documentation
SQLDoc focuses squarely on auto-generating clean, customizable documentation from SQL Server systems.
Started as a Microsoft-sponsored open source project in 2009, SQLDoc offers a free community edition along with paid Pro ($199 per user) and Team ($399 per user) options. Over 5,000 organizations use the tool including Canon, Xerox and TimeX.
Key Features: connect to live databases or static files, customizable HTML/CSS, schema diagrams, document comparisons
Figure 1: SQLDoc dashboard showing database schema details
Consider SQLDoc if you want automated SQL Server documentation on a budget from a lightweight, trusted tool.
ApexSQL Doc – Broad SQL Dev Platform
ApexSQL Doc covers both documentation and broader SQL development needs for teams with more complex toolchain requirements including:
- SQL code refactoring
- Version control
- Data masking
- Permission management
Well-funded with $6 million in Series A financing, over 5,000 companies use ApexSQL products across industries like healthcare, finance and manufacturing.
Key Features: highly customizable documentation, cloud-based access, advanced DevOps integrations
Figure 2: Documentation view within ApexSQL showing schema details
Consider ApexSQL if you want robust documentation alongside other leading SQL tooling in a unified interface.
Dataedo – Metadata Catalog
Dataedo bills itself as a "single source of data truth" – consolidating metadata context across data sources into an organized catalog.
Engineers can map object definitions into a knowledge graph-like interface showing connections between hundreds of data objects. Embed custom metrics, code snippets and other artifacts directly into object pages.
Key Features: unified metadata catalog, data lineage maps, granular access controls, REST API
Figure 3: Data lineage view within Dataedo‘s catalog
Dataedo integrates with leading data platforms like Snowflake, Databricks, MongoDB and MySQL. Over 300 customers use the product including Lyft, Samsung and IBM.
Consider Dataedo if centralizing disparate metadata from different data systems is your top priority.
DbVisualizer – Documentation Meets IDE
DbVisualizer combines database administration IDE capabilities with auto-generated schema documentation. Over 5 million developers actively use DbVis for tasks like:
- Query development
- Database access
- Schema visualization
- Reporting
DbVis documents over 300 database brands – both on premises and cloud-based products – with diagrams generated in multiple graphical notations like IE, Barker, and UML.
Key Features: connects to all major databases; documents schema changes in real-time; share visualization online.
Consider DbVisualizer if you want robust documentation features baked into a leading database IDE.
Hive – Made for Big Data Systems
Most documentation tools focus on traditional relational databases like SQL Server and Oracle. Hive tailors specifically for big data systems like Spark, Kafka and Hadoop.
Features like cluster usage statistics, query failure tracking and pipeline lineage diagrams provide much-needed observability into inherently complex big data topologies spanning on-prem data centers and multi-cloud.
Figure 4: Hive dashboard showing cluster usage metrics
Hive automatically crawls clusters to build a knowledge graph detailing 700+ metadata entities across tech like Databricks, Snowflake, dbt and more.
Key Features: auto-generated data pipeline documentation, cluster usage statistics, query failure alerts, access controls
Well-funded with $85 million in capital, Hive serves leading brands like Doordash, NBCUniversal and FINRA to unify understanding across complex, distributed big data systems.
Consider Hive if your biggest documentation headache stems from tangled big data pipelines spanning legacy systems and cloud platforms.
Apache Drill – Open Source Big Data Query Engine
Part of the Apache Software Foundation ecosystem, Drill provides a distributed SQL query layer across both structured and semi-structured data stored anywhere – from MySQL and Mongo to cloud object stores like S3 and Azure Blob.
The built-in Drill web interface documents schemas and enables customized SQL queries without needing to code batch jobs. Best paired with a visualization layer like Apache Superset.
Key Features: ANSI SQL interface for big data exploration, schema discovery across disparate data, browser-based IDE
Figure 5: Schema documentation within Apache Drill‘s browser IDE
As an open source project, Drill appeals most to organizations willing to invest in more hands-on tool management in return for avoiding vendor lock-in. More packaged solutions like Hive provide a smoother user experience.
Consider Drill if your developers already actively contribute to Apache projects and want integrated SQL querying with basic documentation features.
Secoda – Data Knowledge Portal
Secoda consolidates data documentation, dictionaries, quality rules and other critical knowledge into a central portal designed specifically for modern BI/data teams.
The graph-based explorer surfaces connections across systems to prevent siloed documentation. Customizable permissions allow granular access control over sensitive items.
Figure 6: Secoda dashboard documenting a Snowflake Analytics Cloud implementation
Secoda auto-generates data model documentation across platforms like Snowflake, Tableau, Looker, Spark and dbt with real-time synchronization as schemas evolve.
Key Features: unified metadata catalog, data lineage maps, granular access controls, REST API
The company has raised $22 million in funding from leading investors like Insight Partners, serving companies like Just Eat Takeaway.com, Auto Trader and Trainline.
Consider Secoda If centralized, consistent data documentation across systems is your top priority.
DBT – Data Transformation Documentation
The dbt data transformation platform focuses on helping analysts and engineers collaborate on modeling, testing and operationalizing business logic code stored in git.
The open source dbt Core auto-generates documentation as users build models encompassing:
- Code definitions
- Data lineage maps
- Model dependency graphs
- Column type metadata
- Statistical profiles
Cloud-hosted dbt Cloud packages additional features like granular permissions, usage metrics and alerting.
Figure 7: Auto-generated documentation from dbt showing model definitions
Over 2,000 companies use dbt including Reddit, AutoTrader UK, and Viasat – reflecting growing popularity of its “analytics engineering” approach.
Consider dbt (especially the dbt Cloud platform) if your team already uses its underlying ELT capabilities and wants integrated documentation.
Key Implementation Best Practices
Getting the most from documentation tools requires planning across areas like permissions, integrations, customization and content maintenance:
Permission Carefully
Consider which teams genuinely need visibility into sensitive systems like production databases. Be judicious granting destructive privileges.
Integrate with CD Pipelines
Trigger documentation updates during CI/CD deployments to keep pace with changes.
Customize for Branding
Tweak templates to match internal terminology conventions, color schemes and logos.
Plan Metadata Maintenance
Clarify processes for maintaining artifacts like data dictionaries as new systems get added.
Train Team on Capabilities
Run demos, workshops and docs to increase tool adoption across technical & non-technical users.
Key Decision Criteria
Use this checklist when evaluating documentation tools against your organization‘s specific needs:
| Supported Databases | Will it document all your key database platforms? |
| Output Formats | Can it generate Word, PDF, HTML etc? |
| Cloud vs On-Prem Support | Must it handle legacy, cloud or hybrid systems? |
| Real-Time Updates | Does documentation synchronize with database changes? |
| Collaboration Features | Can multiple people jointly edit and manage content? |
| Configuration vs Coding | Does it offer low/no code customization via UI? |
| Access Controls | Can you finely control and audit who sees what data? |
| Pricing | Does offered pricing align with budget needs? |
Prioritize must-have aspects when shortlisting tools to evaluate further via demos and free trials.
The Future of Database Documentation
Manual documentation processes will continue vanishing from modern technology stacks much like hand-coded HTML gave way to user-friendly web building platforms.
AI-Assistance Goes Mainstream
In 2023, expect more documentation solutions to incorporate AI that suggests helpful diagrams, queries missing tables to auto-document, proposes hyperlinks and summaries text.
Tighter Governance Integrations
Documentation tools will pipeline metadata into data catalogs, data quality rules, and master data management (MDM) systems to inform governance strategies.
Comprehensive Cloud Observability
As multi-cloud adoption increases, documentation platforms will provide command centers connecting the dots across hosted databases, warehouses, ETL tools and business intelligence to prevent fragmented visibility.
Knowledge Democratization
Solutions will move beyond static PDF reports to interactive portals with embedded coaching content that help upskill technical and non-technical business users alike.
In Summary
Database documentation tools provide the essential foundation for critical initiatives – from preventing outages to accelerating onboarding and enabling company-wide data literacy.
Modern solutions can auto-generate detailed documentation encompassing data schemas, ETL jobs, data dictionaries, platform topologies and more. Automation frees teams from tedious and error-prone manual documentation processes.
This guide explored top commercial and open source documentation solutions along with leading practices around adoption. Reach out to discuss challenges around stewarding critical database and analytics systems as finding the right documentation partner provides the roots for data-driven success.
