Legacy Architecture of Chartio Dashboards

How Chartio Operated

Chartio connected directly to databases like PostgreSQL, MySQL, Redshift, and BigQuery. It allowed users to write raw SQL or use Visual SQL (VSQL) to construct queries, applying transformations and joins through its GUI. Dashboards often consisted of multiple layered charts referencing shared datasets or parameters.

Points of Fragility

• Custom SQL fragments scattered across dashboards
• Use of calculated columns not replicated in source data
• Dependency on Chartio-specific filters and controls

Common Failures and Their Root Causes

1. Broken Exports or Stale Data

Teams often encounter scheduled exports that silently fail post-sunset or after changes to database schemas. Since Chartio cached results, stale visualizations can persist without warning.

2. Access Control and Role Drift

Chartio's user roles were managed within the app, but often overlapped with DB-level permissions. When team structures changed, inconsistencies arose between who could view versus who could query data.

3. SQL Errors After Source System Changes

Schema evolution in upstream databases breaks raw SQL charts. Without centralized versioning of queries, updates must be manually tracked and applied.

SELECT orders.id, customers.name
FROM orders
JOIN customers ON orders.customer_id = customers.id  -- breaks if column renamed or removed

Migration Strategy and Troubleshooting Approach

Step 1: Audit Existing Assets

• Export dashboard JSON via Chartio API (if previously saved)
• Identify all visualizations, filters, and their SQL sources
• Group dashboards by data source and business unit

Step 2: Extract Reusable Logic

Pull SQL and calculated fields into a central repository. Use templating to make filters and parameters explicit. Reuse logic in tools like dbt or Looker views for maintainability.

Step 3: Validate with Stakeholders

Replicate key dashboards in the target BI tool (e.g., Metabase, Redash, Mode). Validate output correctness with business users before retiring legacy views.

Debugging Dashboard-Level Failures

Visual SQL Compatibility

Visual SQL does not always translate 1:1 to raw SQL. Understand how Chartio layered transformations—especially aggregations, filters, and derived columns. These must be re-implemented in SQL or the new BI tool's logic layer.

Chart Filters Not Syncing

Global filters in Chartio dashboards often relied on parameter tokens. If improperly scoped, these would fail to apply across all charts. New tools may need explicit cross-filter wiring.

WHERE orders.created_at BETWEEN {{start_date}} AND {{end_date}}

Long-Term Best Practices for BI System Resilience

Version Control and Documentation

• Maintain SQL logic in versioned repositories, separate from visualization layers
• Use dbt to build and test models upstream
• Document filter logic, metrics, and data sources in a centralized data catalog

Tool-Agnostic Design Principles

Design dashboards to be portable by minimizing dependence on proprietary transformations. Favor using database views or materialized tables for derived metrics rather than inline SQL in charts.

Conclusion

Chartio's deprecation has created a significant burden for organizations that built mission-critical reporting on its platform. The path to resolution involves not just dashboard replication, but architectural reform: auditing data logic, improving modularity, and decoupling visualization from transformation layers. By embracing version control, stakeholder alignment, and tool-agnostic modeling, teams can achieve a more stable and future-proof BI ecosystem.

FAQs

1. Can I still access my old Chartio dashboards?

Only if they were exported before the Chartio shutdown. JSON or CSV exports may be available if backed up via API or internal archiving.

2. What tools are best suited for replacing Chartio?

Looker, Metabase, Redash, and Mode are popular replacements. Choose based on team size, technical skill, and data modeling needs.

3. How do I migrate Visual SQL dashboards?

Translate each layer of the VSQL logic into raw SQL or view models in your target tool. Validate behavior with real data before publishing.

4. What if I lost the SQL behind my charts?

If you no longer have access, reverse-engineer logic based on dashboard exports and known outputs. Interview business users to recover intent.

5. How can I prevent similar vendor lock-in in the future?

Maintain data logic in external, versioned tools like dbt. Avoid embedding critical logic solely in BI platforms. Prioritize open standards and exportability.