Apache Superset for Mining Operations: A Reference Dashboard Set

Table of Contents

1. Why Apache Superset for Mining Operations
2. The Reference Dashboard Architecture
3. Core Data Model and Schema Design
4. Key Metrics and KPI Framework
5. Dashboard Drilldown Patterns
6. Schema Patterns That Survive Scale
7. Implementation Timeline and Deployment
8. Security, Compliance, and Operational Readiness
9. Real-World Mining Operations Scenarios
10. Next Steps: From Reference Set to Production

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Why Apache Superset for Mining Operations

Mining operations generate enormous volumes of data—from pit-to-port logistics, equipment telemetry, ore grades, water usage, and safety metrics—yet most operations teams still rely on fragmented spreadsheets, vendor BI tools with per-seat licensing, or bespoke dashboards that break when data schemas shift. The cost and inflexibility become intolerable at scale.

Apache Superset, the open-source data visualisation and business intelligence platform maintained by the Apache Software Foundation, solves this by offering a lightweight, self-hosted, SQL-native dashboard layer that integrates seamlessly with your existing data warehouse or operational database. Unlike traditional BI platforms, Superset does not require expensive licensing, vendor lock-in, or months of consulting engagements.

For mining operations specifically, Superset excels because it:

• Handles time-series and operational data natively. Mining data is predominantly time-stamped: production rates, equipment uptime, stockpile volumes, water consumption. Superset’s native support for date filtering, rolling aggregations, and multi-granularity views means you build dashboards once and reuse them across daily, weekly, and monthly views without duplicating logic.
• Scales horizontally. A single Superset instance can power dashboards across multiple sites, asset classes, and business units. As your data warehouse grows from gigabytes to terabytes, Superset’s query engine and caching layer keep response times under 2 seconds.
• Integrates with your existing data infrastructure. Whether you’re running ClickHouse, PostgreSQL, Snowflake, or a traditional data warehouse, Superset speaks SQL. No data replication, no ETL lock-in, no vendor-specific query language.
• Reduces licensing and operational overhead. Open-source means no per-seat fees, no annual true-ups, and full control over deployment—whether on-premises, hybrid, or in the cloud. For large operations teams (50+ users), this alone saves $200K+ annually compared to vendor BI tools.
• Supports embedded analytics. If you’re building custom applications for site managers or operational dashboards for edge devices, Superset’s embedding API and REST interface let you bake analytics directly into your applications.

The reference dashboard set we detail here is designed for mid-to-large mining operations (single or multi-site) running daily production planning, equipment monitoring, and compliance reporting. It covers the schema design, metric definitions, and drilldown interaction patterns that have proven robust across open-pit, underground, and processing operations in Australia and internationally.

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The Reference Dashboard Architecture

The reference dashboard set comprises five core dashboards, each addressing a distinct operational domain:

Production and Throughput Dashboard

This dashboard tracks daily, weekly, and monthly ore production, waste removal, and processing throughput. It answers the question: “Are we on track to meet production targets, and where are the bottlenecks?”

Key components:

• Production by pit/zone (bar chart, drillable by equipment type)
• Cumulative production vs. forecast (line chart with confidence bands)
• Throughput by processing stage (waterfall or stacked bar, showing ore loss at each step)
• Equipment utilisation heatmap (rows = equipment, columns = shift, colour = % uptime)

This dashboard connects directly to your operational historian (SCADA, PI System, or equivalent) and your production planning system. The underlying SQL is simple—aggregations on timestamp, site, equipment, and ore type—but the visualisations must support drill-through to daily or shift-level detail without requiring dashboard redesign.

Equipment Health and Maintenance Dashboard

This dashboard aggregates equipment telemetry, failure rates, and maintenance scheduling. It answers: “Which assets are degrading, and when should we schedule maintenance to minimise downtime?”

Key components:

• Mean time between failure (MTBF) by asset class (bar chart, ranked)
• Maintenance backlog by priority and due date (Gantt or timeline)
• Failure rate trend (line chart, month-over-month)
• Spare parts consumption vs. inventory (dual-axis, inventory on right, consumption on left)

This dashboard pulls from your CMMS (Computerised Maintenance Management System) and equipment telemetry streams. The schema design must support both planned and unplanned downtime, with clear categorisation of failure modes (mechanical, electrical, process, human).

Safety and Compliance Dashboard

This dashboard tracks incidents, near-misses, hazard observations, and compliance metrics. It answers: “Are we trending towards or away from our safety targets, and which areas are at risk?”

Key components:

• Total recordable incident rate (TRIR) and lost-time injury frequency rate (LTIFR) (KPI cards, month-to-date and YTD)
• Incident heatmap by location and type (rows = location, columns = incident type, colour = count)
• Hazard observation trend (line chart, cumulative by site)
• Compliance audit status (table, showing due dates, last completion, and remediation items)

This dashboard typically reads from your safety management system (SafetyLync, Enablon, or internal database) and must be read-only to non-safety-officer users. Superset’s role-based access control (RBAC) ensures only authorised personnel can view sensitive safety data.

Water and Environmental Dashboard

This dashboard monitors water consumption, discharge quality, and environmental compliance. It answers: “Are we within water budgets and discharge limits, and what is our environmental footprint?”

Key components:

• Water consumption by source and process stage (stacked bar, daily or weekly)
• Discharge pH, turbidity, and contaminant levels vs. limits (line chart with reference bands)
• Water recycling rate (gauge or KPI card)
• Tailings storage facility (TSF) level and capacity (gauge or line chart)

This dashboard integrates with environmental monitoring systems and water treatment plant SCADA. The schema must support both real-time sensor data (updated every minute) and lab-tested batch results (updated daily or weekly), with clear distinction between the two.

Financial and Cost Dashboard

This dashboard tracks operational costs, unit costs (cost per tonne), and financial performance. It answers: “What is our cost trajectory, and which cost drivers are moving?”

Key components:

• Cost per tonne of ore (CPOT) (line chart, month-to-date vs. budget)
• Cost breakdown by category (pie or donut: labour, energy, consumables, maintenance, other)
• Fuel and energy consumption vs. cost (dual-axis scatter or line)
• Capex spend vs. budget (waterfall or bar, by project)

This dashboard pulls from your ERP (SAP, Oracle, NetSuite) and cost accounting system. The schema design must support both actual costs (from accounting) and estimated costs (from operational planning), with clear audit trails for reconciliation.

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Core Data Model and Schema Design

The success of any Superset deployment hinges on the underlying data model. A poorly designed schema forces dashboard developers to write complex SQL, creates inconsistencies, and breaks when data volumes grow. A well-designed schema is self-documenting, supports fast queries, and scales to billions of rows.

For mining operations, we recommend a dimensional model (also called a star schema) with the following structure:

Fact Tables

fact_production

production_id (PK)
date_id (FK to dim_date)
site_id (FK to dim_site)
pit_id (FK to dim_pit)
equipment_id (FK to dim_equipment)
shift_id (FK to dim_shift)
ore_type_id (FK to dim_ore_type)
ore_tonnes (numeric)
waste_tonnes (numeric)
stripping_ratio (numeric)
downtime_minutes (integer)
downtime_reason_id (FK to dim_downtime_reason)
recorded_at (timestamp)

fact_equipment_telemetry

telemetry_id (PK)
equipment_id (FK to dim_equipment)
date_id (FK to dim_date)
time_id (FK to dim_time, for sub-hourly granularity)
temperature_celsius (numeric)
pressure_bar (numeric)
vibration_mm_s (numeric)
power_consumption_kw (numeric)
status_id (FK to dim_status: running, idle, fault, maintenance)
recorded_at (timestamp)

fact_maintenance

maintenance_id (PK)
equipment_id (FK to dim_equipment)
date_id (FK to dim_date)
maintenance_type_id (FK to dim_maintenance_type: planned, unplanned, emergency)
cost_aud (numeric)
duration_minutes (integer)
maintenance_reason_id (FK to dim_maintenance_reason)
scheduled_date_id (FK to dim_date)
recorded_at (timestamp)

fact_safety_incident

incident_id (PK)
date_id (FK to dim_date)
site_id (FK to dim_site)
incident_type_id (FK to dim_incident_type: injury, near-miss, hazard observation)
severity_id (FK to dim_severity)
people_involved (integer)
lost_time_hours (numeric)
recorded_at (timestamp)

fact_water

water_id (PK)
date_id (FK to dim_date)
site_id (FK to dim_site)
water_source_id (FK to dim_water_source: bore, dam, recycled, town supply)
volume_megalitres (numeric)
discharge_ph (numeric)
discharge_turbidity_ntu (numeric)
discharge_contaminant_ppm (numeric)
recycled_volume_megalitres (numeric)
recorded_at (timestamp)

Dimension Tables

dim_date

date_id (PK, integer, format: YYYYMMDD)
date (date)
year (integer)
quarter (integer)
month (integer)
week (integer)
day_of_month (integer)
day_of_week (integer)
is_weekend (boolean)
is_public_holiday (boolean)
fiscal_year (integer)
fiscal_quarter (integer)

dim_site

site_id (PK)
site_name (varchar)
site_code (varchar, e.g., 'PIT01', 'UG02')
region (varchar, e.g., 'Pilbara', 'Bowen Basin')
state (varchar, e.g., 'WA', 'QLD')
country (varchar)
latitude (numeric)
longitude (numeric)
operating_since (date)
status (varchar: active, suspended, closed)

dim_equipment

equipment_id (PK)
equipment_code (varchar, e.g., 'EXC001', 'DRILL05')
equipment_name (varchar)
equipment_type_id (FK to dim_equipment_type)
manufacturer (varchar)
model (varchar)
serial_number (varchar)
site_id (FK to dim_site)
installation_date (date)
status (varchar: active, idle, decommissioned)
expected_life_years (integer)

dim_pit

pit_id (PK)
pit_name (varchar)
pit_code (varchar)
site_id (FK to dim_site)
pit_type (varchar: open pit, underground, processing)
orebody_id (FK to dim_orebody)
status (varchar: active, development, closed)

dim_ore_type

ore_type_id (PK)
ore_type_name (varchar)
ore_code (varchar)
expected_grade_percent (numeric)
expected_recovery_percent (numeric)
market_price_aud_per_tonne (numeric)

Why This Schema Works at Scale

This dimensional model offers several advantages:

1. Query performance. Fact tables are optimised for aggregation (SUM, AVG, COUNT) along any dimension. A query like “total ore production by pit and month” executes in milliseconds, even on 10 years of daily data.
2. Flexibility. New dimensions (e.g., contractor, shift supervisor) are added without modifying fact tables. Existing dashboards continue to work.
3. Data integrity. Foreign key relationships (fact to dimension) ensure consistency. If a pit is closed, you can see historical data but cannot accidentally assign new production to a defunct pit.
4. Auditability. The recorded_at timestamp on every fact row enables forensic analysis and compliance reporting. You can answer “what did the dashboard show on 15 March?” by querying historical snapshots.
5. Scalability. The schema supports partitioning fact tables by date, site, or equipment type. As data grows, you partition and archive older data without affecting query performance.

For mining operations with multiple sites, add a site_id foreign key to every fact table. This allows roll-up queries (“total production across all sites”) and site-level drill-down without schema changes.

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Key Metrics and KPI Framework

Once your schema is in place, define a consistent set of metrics that every dashboard uses. This ensures consistency and reduces calculation errors.

Production Metrics

Ore Production (tonnes)

SUM(fact_production.ore_tonnes)

Waste Production (tonnes)

SUM(fact_production.waste_tonnes)

Stripping Ratio (waste : ore)

SUM(fact_production.waste_tonnes) / SUM(fact_production.ore_tonnes)

Equipment Utilisation (%)

100 * (1 - SUM(fact_production.downtime_minutes) / (COUNT(DISTINCT fact_production.equipment_id) * 24 * 60))

Equipment Metrics

Mean Time Between Failure (hours)

SUM(CASE WHEN fact_maintenance.maintenance_type_id = 'unplanned' THEN 0 ELSE fact_equipment_telemetry.uptime_hours END)
/ COUNT(DISTINCT fact_maintenance.maintenance_id WHERE fact_maintenance.maintenance_type_id = 'unplanned')

Maintenance Cost per Operating Hour (AUD/hour)

SUM(fact_maintenance.cost_aud) / SUM(fact_equipment_telemetry.uptime_hours)

Safety Metrics

Total Recordable Incident Rate (TRIR)

(COUNT(DISTINCT fact_safety_incident.incident_id WHERE fact_safety_incident.severity_id IN ('injury', 'lost-time')) / SUM(fact_equipment_telemetry.uptime_hours)) * 200000

Lost-Time Injury Frequency Rate (LTIFR)

(COUNT(DISTINCT fact_safety_incident.incident_id WHERE fact_safety_incident.severity_id = 'lost-time') / SUM(fact_equipment_telemetry.uptime_hours)) * 1000000

Cost Metrics

Cost Per Tonne of Ore (CPOT, AUD/tonne)

SUM(fact_maintenance.cost_aud + fact_labour.cost_aud + fact_energy.cost_aud + fact_consumables.cost_aud) / SUM(fact_production.ore_tonnes)

Energy Cost Per Tonne (AUD/tonne)

SUM(fact_energy.cost_aud) / SUM(fact_production.ore_tonnes)

Define these metrics once in Superset using Calculated Columns or Virtual Metrics. Once defined, every dashboard inherits the same logic, eliminating discrepancies and reducing maintenance overhead.

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Dashboard Drilldown Patterns

Drilldown—the ability to click on a summary bar or cell and see underlying detail—is what transforms static dashboards into operational tools. Superset supports drill-through via cross-filter and native query interactions.

Pattern 1: Temporal Drilldown

Example: Click on “Month of May” in the production dashboard to see daily breakdown.

Implementation:

1. Create a summary chart (monthly production by pit).
2. Set the chart’s drill-through URL to:

   /superset/dashboard/production-daily/?pit_id={{ pit_id }}&date_range_start={{ __timestamp.rawValue | dateformat('YYYY-MM-01') }}&date_range_end={{ __timestamp.rawValue | dateformat('YYYY-MM-t') }}

3. The target dashboard (production-daily) filters on pit_id and date_range from URL parameters.

This pattern lets operators move from monthly planning view to daily operational view without losing context.

Pattern 2: Hierarchical Drilldown

Example: Click on “Site A” to drill into pits, then click on “Pit 1” to drill into equipment.

Implementation:

1. Summary chart: production by site.
2. Drill URL: /superset/dashboard/production-by-pit/?site_id={{ site_id }}
3. Target dashboard filters on site_id, shows production by pit.
4. Each pit bar has drill URL: /superset/dashboard/production-by-equipment/?pit_id={{ pit_id }}
5. Target dashboard filters on pit_id, shows production by equipment.

This pattern works best when your schema supports a clear hierarchy (site > pit > equipment or site > department > team).

Pattern 3: Cross-Dashboard Filtering

Example: Click on an equipment asset in the Equipment Health dashboard to see its production contribution in the Production dashboard.

Implementation:

1. In Equipment Health dashboard, create a table of equipment with columns: equipment_name, MTBF, failure_rate.
2. Set the table’s drill URL to:

   /superset/dashboard/production/?equipment_id={{ equipment_id }}&date_range_start={{ __timestamp.rawValue | dateformat('YYYY-01-01') }}

3. The Production dashboard has a filter for equipment_id (set to “required” so the URL parameter populates it).
4. All charts on Production dashboard now show only data for that equipment.

This pattern enables cross-functional investigation: “This excavator has low MTBF—is it underutilised, or is it a lemon?”

Pattern 4: Incident Root-Cause Drilldown

Example: Click on a spike in downtime to see which equipment failed and why.

Implementation:

1. Equipment Utilisation heatmap: rows = equipment, columns = date, colour = downtime minutes.
2. Click on a red cell (high downtime) to drill to:

   /superset/dashboard/maintenance-detail/?equipment_id={{ equipment_id }}&date={{ __timestamp.rawValue | dateformat('YYYY-MM-DD') }}

3. Target dashboard shows maintenance records for that equipment on that date, with reason, duration, and cost.

This pattern is critical for incident investigation and root-cause analysis.

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Schema Patterns That Survive Scale

As your mining operation grows—more sites, more equipment, more frequent data collection—your schema must scale without requiring redesign. Here are proven patterns:

Pattern 1: Time-Series Partitioning

If your fact tables grow beyond 1 billion rows, partition by date:

CREATE TABLE fact_equipment_telemetry_2024_q1 (
  telemetry_id BIGINT,
  equipment_id INT,
  date_id INT,
  time_id INT,
  temperature_celsius DECIMAL(5,2),
  -- ... other columns
) PARTITION BY RANGE (date_id) (
  PARTITION p_2024_01 VALUES LESS THAN (20240201),
  PARTITION p_2024_02 VALUES LESS THAN (20240301),
  PARTITION p_2024_03 VALUES LESS THAN (20240401)
);

Benefits:

• Queries that filter by date automatically scan only relevant partitions.
• Old partitions can be archived to cheaper storage (S3, tape) without deleting.
• Bulk deletes (e.g., removing a site’s data) become partition drops, not row-by-row deletes.

Pattern 2: Slowly Changing Dimensions (SCD Type 2)

Equipment specifications change (e.g., a motor is upgraded). Track changes without losing history:

CREATE TABLE dim_equipment_scd (
  equipment_key INT PRIMARY KEY,  -- Surrogate key
  equipment_id INT,               -- Natural key
  equipment_name VARCHAR,
  equipment_type INT,
  manufacturer VARCHAR,
  model VARCHAR,
  power_rating_kw DECIMAL(8,2),
  valid_from DATE,
  valid_to DATE,
  is_current BOOLEAN
);

-- Example: Motor upgraded on 2024-06-15
INSERT INTO dim_equipment_scd VALUES
  (1001, 'EXC001', 'Excavator 1', 1, 'Komatsu', 'PC8000', 800, '2020-01-01', '2024-06-14', FALSE),
  (1002, 'EXC001', 'Excavator 1', 1, 'Komatsu', 'PC8000-E', 850, '2024-06-15', '9999-12-31', TRUE);

Benefits:

• Historical fact rows link to the equipment specifications that were current at the time.
• You can answer “How did equipment upgrades affect downtime?” by joining facts to the correct SCD version.
• Auditors can verify that equipment specifications match historical records.

Pattern 3: Conformed Dimensions

If multiple fact tables share a dimension (e.g., site, equipment), define it once and reuse:

-- Conformed dimension
CREATE TABLE dim_site (site_id INT PRIMARY KEY, ...)

-- All fact tables reference it
CREATE TABLE fact_production (... site_id INT REFERENCES dim_site ...)
CREATE TABLE fact_water (... site_id INT REFERENCES dim_site ...)
CREATE TABLE fact_safety_incident (... site_id INT REFERENCES dim_site ...)

Benefits:

• Dashboards can slice by site across all operational domains (production, water, safety) without custom logic.
• Adding a new site or site attribute updates all dashboards automatically.

Pattern 4: Aggregate Tables (Materialized Views)

If daily production by pit is queried 100 times per day, pre-compute it:

CREATE MATERIALIZED VIEW agg_production_daily_by_pit AS
SELECT
  fact_production.date_id,
  fact_production.site_id,
  fact_production.pit_id,
  SUM(fact_production.ore_tonnes) AS ore_tonnes_daily,
  SUM(fact_production.waste_tonnes) AS waste_tonnes_daily,
  AVG(fact_production.stripping_ratio) AS avg_stripping_ratio,
  SUM(fact_production.downtime_minutes) AS downtime_minutes_daily
FROM fact_production
GROUP BY fact_production.date_id, fact_production.site_id, fact_production.pit_id;

CREATE INDEX idx_agg_prod_date_pit ON agg_production_daily_by_pit(date_id, pit_id);

Then point your dashboard chart to agg_production_daily_by_pit instead of fact_production. Query time drops from 5 seconds to 50 milliseconds.

Benefits:

• Dashboards load instantly, even during peak operational hours.
• The underlying fact table remains immutable (good for compliance).
• Aggregate tables are refreshed on a schedule (e.g., every 30 minutes) so they stay current.

Pattern 5: Degenerate Dimensions

Some attributes don’t warrant a full dimension table. Store them directly in the fact table:

CREATE TABLE fact_production (
  production_id BIGINT PRIMARY KEY,
  date_id INT,
  site_id INT,
  pit_id INT,
  equipment_id INT,
  ore_tonnes DECIMAL(10,2),
  waste_tonnes DECIMAL(10,2),
  shift_code VARCHAR(10),  -- Degenerate dimension: 'A', 'B', 'C'
  weather_condition VARCHAR(20),  -- Degenerate dimension: 'Clear', 'Rain', 'Dust'
  recorded_at TIMESTAMP
);

Benefits:

• Reduces joins, improving query speed.
• Simplest attributes (shift, weather) don’t need dimension tables.

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Implementation Timeline and Deployment

Deploying a reference dashboard set from schema design to production typically takes 4–8 weeks, depending on data readiness and organisational complexity.

Week 1–2: Data Discovery and Schema Design

Activities:

• Interview operations, maintenance, safety, and finance teams to understand current reporting needs.
• Document existing data sources (historian, CMMS, ERP, safety system).
• Map source systems to proposed fact and dimension tables.
• Create a logical data model (ERD) and review with stakeholders.

Deliverables:

• Data lineage diagram.
• Dimensional model (star schema) with table definitions.
• Data dictionary (column names, types, units, business definitions).

Week 3–4: Data Pipeline and ETL

Activities:

• Build ETL pipelines to extract data from source systems and load into staging tables.
• Implement data quality checks (null counts, range validation, referential integrity).
• Create fact and dimension tables in your data warehouse or analytical database (PostgreSQL, ClickHouse, Snowflake).
• Validate row counts and sample data with business stakeholders.

Deliverables:

• ETL code (dbt, Airflow, or equivalent).
• Data warehouse schema (CREATE TABLE statements).
• Initial data load (historical data for 1–2 years, if available).
• Data quality report.

For mining operations in Australia, consider deploying your data warehouse on Platform Development in Australia infrastructure to ensure compliance with local data residency requirements and integration with OT/IT systems. If you’re based in Perth, Platform Development in Perth specialists can guide historian and SCADA pipeline design.

Week 5–6: Superset Configuration and Dashboard Build

Activities:

• Deploy Superset (Docker, Kubernetes, or cloud-hosted).
• Connect Superset to your data warehouse.
• Create calculated columns and virtual metrics (CPOT, MTBF, LTIFR, etc.).
• Build the five reference dashboards (Production, Equipment Health, Safety, Water, Financial).
• Configure filters, drill-through, and cross-filtering.
• Set up role-based access control (RBAC) so only authorised users see sensitive data (e.g., safety incidents).

Deliverables:

• Superset instance (production-ready).
• Five reference dashboards.
• RBAC configuration.
• Dashboard documentation (what each chart shows, how to interpret it, who to contact for questions).

Week 7–8: Testing, Training, and Go-Live

Activities:

• UAT (user acceptance testing) with operations, maintenance, safety, and finance teams.
• Identify and fix bugs (incorrect calculations, missing data, slow queries).
• Conduct training sessions for each user group.
• Document runbooks for common tasks (adding a new site, changing a KPI threshold, exporting a report).
• Monitor performance and user adoption in the first week of production.

Deliverables:

• UAT sign-off from stakeholders.
• Training recordings and user guides.
• Runbooks and troubleshooting guides.
• Performance baseline (dashboard load times, query latency).

Post-Go-Live: Continuous Improvement

Ongoing activities:

• Monitor dashboard usage and user feedback.
• Refresh aggregate tables and materialized views on a schedule (e.g., every 30 minutes).
• Add new dashboards or metrics based on user requests (e.g., contractor productivity, ore grade tracking).
• Archive old data and partition tables as they grow.
• Review and update RBAC quarterly as teams change.

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Security, Compliance, and Operational Readiness

Mining operations are highly regulated. Your Superset deployment must meet security, compliance, and reliability standards.

Authentication and Authorisation

Superset supports multiple authentication methods:

1. LDAP/Active Directory. Integrate with your corporate directory so users log in with their existing credentials.
2. OAuth 2.0 / SAML 2.0. For cloud-based identity providers (Azure AD, Okta, Google Workspace).
3. Database authentication. Superset’s built-in user management (suitable for smaller teams).

For security, use LDAP or SAML. This ensures:

• Users who leave the organisation are automatically deprovisioned.
• Password policies (complexity, expiry) are enforced centrally.
• Multi-factor authentication (MFA) is available.

Configure role-based access control (RBAC) to restrict dashboard and dataset access:

Role: Operations Manager
- Can view: Production, Equipment Health, Water dashboards
- Can export: Yes
- Can edit dashboards: No

Role: Safety Officer
- Can view: Safety dashboard (all sites)
- Can export: Yes
- Can edit dashboards: No

Role: Finance Analyst
- Can view: Financial dashboard
- Can export: Yes
- Can edit dashboards: No

Role: BI Administrator
- Can view: All dashboards
- Can export: Yes
- Can edit dashboards: Yes
- Can manage users and roles: Yes

Data Encryption

1. In transit. Use TLS 1.2+ for all connections between Superset, the data warehouse, and user browsers. Enable HTTPS on the Superset instance.
2. At rest. If your data warehouse is cloud-hosted (Snowflake, AWS RDS), enable encryption at rest. If on-premises, use encrypted storage or filesystem-level encryption.
3. Database credentials. Store data warehouse connection strings (username, password) in Superset’s encrypted secret store, not in plaintext configuration files.

Audit Logging

Enable Superset’s audit logging to track:

• Who accessed which dashboard and when.
• Which queries were executed.
• Who modified dashboards or created new ones.

Superset logs these events to a database table. Export logs regularly (daily or weekly) to a centralised logging system (ELK, Splunk, CloudWatch) for long-term retention and compliance.

Data Residency and Sovereignty

For Australian mining operations, ensure:

• Your data warehouse is hosted in Australia (AWS Sydney, Azure Australia, or on-premises).
• Data does not cross borders without explicit approval.
• Backup and disaster recovery (DR) are within Australia.

If you’re in Perth and working with OT/IT data (SCADA, historian), Fractional CTO & CTO Advisory in Perth can help design an architecture that meets industrial data residency and security requirements.

Compliance Frameworks

Mining operations often need to demonstrate compliance with:

1. ISO 27001 (Information Security Management). Superset is a data asset that must be protected. Ensure your deployment includes access controls, encryption, audit logging, and incident response procedures.
2. SOC 2 Type II (Security, Availability, Processing Integrity, Confidentiality). If you’re using a cloud-hosted Superset or data warehouse, verify the provider’s SOC 2 certification.
3. NIST Cybersecurity Framework. Align Superset deployment with NIST’s Secure Software Development Framework (SSDF) by using version control, code review, and automated testing for dashboard changes.
4. Industry-specific standards (e.g., ICMM, EITI for transparency). Ensure your dashboards support reporting to external bodies.

For comprehensive security audit readiness, consider engaging a platform engineering partner. Case Studies from mining and energy companies show how modern data platforms can achieve ISO 27001 compliance without sacrificing agility.

Disaster Recovery and High Availability

1. Backup strategy. Back up Superset’s metadata database (PostgreSQL) daily. Back up your data warehouse according to its RPO/RTO (recovery point objective / recovery time objective).
2. Failover. If your primary Superset instance fails, have a standby instance ready to assume the load. Use a load balancer to route traffic.
3. Monitoring. Set up alerts for:
   • Superset service down.
   • Data warehouse connection failures.
   • Query latency exceeding thresholds (e.g., >5 seconds).
   • Disk space on the Superset server below 20%.

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Real-World Mining Operations Scenarios

Here are three scenarios showing how the reference dashboard set solves common operational challenges:

Scenario 1: Equipment Failure Investigation

Situation: Production dropped 15% on Tuesday. Operations manager needs to know why.

Dashboard flow:

1. Open the Production and Throughput dashboard.
2. Notice the production bar for Tuesday is red (below forecast).
3. Click on the bar to drill into the Equipment Utilisation heatmap.
4. See that Excavator 3 shows 60% downtime on Tuesday (red cell).
5. Click on the cell to drill into the Equipment Health dashboard filtered to Excavator 3.
6. See a maintenance record: “Hydraulic leak, 8 hours unplanned downtime, replaced seal pack, cost AUD $12K.”
7. Cross-check the Maintenance Backlog to see if this was a known issue or surprise failure.

Outcome: Identified root cause in <5 minutes. Finance can forecast impact on weekly costs. Maintenance can plan preventive actions on similar equipment.

Scenario 2: Safety Trend Investigation

Situation: LTIFR (lost-time injury frequency rate) is trending up. Safety manager needs to understand why and where.

Dashboard flow:

1. Open the Safety and Compliance dashboard.
2. See LTIFR line chart trending up from 2.1 to 3.5 over the last 6 months.
3. Click on the Incident heatmap (rows = location, columns = incident type).
4. Notice that Site B (underground operation) has a cluster of “slip/trip/fall” incidents in the last 2 months.
5. Click on Site B to drill into a detailed incident list, showing dates, times, and remediation status.
6. Cross-check the Hazard Observation trend to see if Site B had a spike in hazard observations (leading indicator) before the incidents.
7. Identify that Site B’s ground support was upgraded in Month 1, but wet season (Month 2–3) caused ground instability.

Outcome: Root cause identified (environmental + infrastructure change). Recommend additional ground support and hazard briefings for Site B. Trend is now actionable.

Scenario 3: Cost Optimisation

Situation: Cost per tonne of ore (CPOT) is 8% above budget. CFO needs to know which cost drivers are moving.

Dashboard flow:

1. Open the Financial and Cost dashboard.
2. See CPOT line chart trending up from AUD $65/t to AUD $70/t.
3. Click on the Cost breakdown pie chart to see that energy costs have increased from 22% to 28% of total cost.
4. Drill into the Energy cost vs. consumption dual-axis chart.
5. See that energy consumption is stable, but energy prices ($/kWh) have increased by 12% due to grid tariff changes.
6. Cross-check the Equipment Utilisation heatmap to see if equipment is running less efficiently (higher power draw per tonne).
7. Identify that Excavator 1 and Crusher 2 are consuming 15% more power than historical average, likely due to wear.

Outcome: Energy cost increase is external (tariff), not operational. But equipment degradation is a concern. Recommend accelerated maintenance on Excavator 1 and Crusher 2 to restore efficiency.

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Next Steps: From Reference Set to Production

The reference dashboard set is a starting point. Your organisation will need customisations and extensions:

Phase 1: Deploy the Reference Set (Weeks 1–8)

Follow the implementation timeline above. Get the five core dashboards into production and stable.

Phase 2: Extend with Custom Dashboards (Weeks 9–16)

Based on user feedback, add dashboards for:

• Contractor performance (if you use contractors for mining or processing).
• Ore grade tracking (assay results by pit, stockpile, and product).
• Fleet telematics (truck utilisation, fuel consumption, haul times).
• Regulatory reporting (EITI, ICMM, state-mandated reports).

For complex custom dashboards (especially those requiring real-time data or edge analytics), engage a platform engineering partner. Platform Development in Brisbane and Platform Development in Adelaide specialise in logistics, telematics, and sovereign data platforms for mining and resources.

Phase 3: Embed Analytics in Operational Systems (Weeks 17+)

Once Superset is stable, embed dashboards in:

• Shift handover applications (so shift supervisors see key metrics at shift start).
• Mobile apps for site managers (Superset’s REST API supports mobile embedding).
• ERP systems (integrate Superset charts into your SAP or Oracle portal).

For embedded analytics and custom applications, Platform Development in Houston and Platform Development in Calgary have deep experience with operational data platforms for energy and resources.

Phase 4: Operationalise and Scale (Ongoing)

• Automate data quality. Use dbt tests to validate data freshness, completeness, and accuracy.
• Optimise performance. Monitor slow queries and create aggregate tables or indexes.
• Expand to new sites. As you acquire or develop new mining sites, add them to the dimensional model without re-architecting.
• Integrate with AI/ML. Use Superset dashboards to visualise predictions (e.g., equipment failure probability, ore grade forecasts) from machine learning models.

If you’re planning AI-driven operational optimisation (predictive maintenance, ore grade prediction, cost forecasting), Fractional CTO & CTO Advisory in Houston and Fractional CTO & CTO Advisory in Chicago can advise on integrating Superset with agentic AI and ML pipelines.

Getting Help

Superset is open-source and actively maintained by the Apache Software Foundation. The community is large and helpful:

• Official Superset documentation covers installation, configuration, and dashboard development.
• GitHub repository hosts code, issues, and release notes.
• Apache License governs use and distribution (permissive open-source license).

If you need hands-on support deploying Superset in a mining context, PADISO offers:

• Platform engineering (Platform Development in Australia) to design and build your data warehouse and Superset infrastructure.
• Fractional CTO leadership (Fractional CTO & CTO Advisory in Perth) to guide architecture, technology selection, and team building.
• Security and compliance support to ensure your Superset deployment meets ISO 27001, SOC 2, and industry-specific requirements.

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Summary

Apache Superset is a powerful, cost-effective alternative to traditional BI platforms for mining operations. A well-designed reference dashboard set—grounded in a dimensional data model, consistent metrics, and proven drilldown patterns—can be deployed in 4–8 weeks and immediately improve operational visibility and decision-making.

The five core dashboards (Production, Equipment Health, Safety, Water, Financial) cover the most critical operational domains. The schema patterns (time-series partitioning, slowly changing dimensions, conformed dimensions, aggregate tables) ensure the system scales as your operation grows from single-site to multi-site, and from daily to real-time monitoring.

Security, compliance, and disaster recovery are not afterthoughts—they’re built in from day one, with RBAC, encryption, audit logging, and alignment with ISO 27001 and SOC 2 frameworks.

The next step is to assess your current data infrastructure, define your dimensional model, and pilot the reference dashboard set on a single site or operational domain. Once proven, scale to your full operation.

Ready to get started? Engage with PADISO’s platform engineering team to design your Superset deployment and data warehouse architecture, tailored to your mining operation’s scale, compliance requirements, and growth plans.