Guide 22 mins

Smart City Analytics on Apache Superset

Master smart city analytics with Apache Superset. Deploy IoT dashboards, traffic data, and operational intelligence for councils and municipalities.

The PADISO Team ·2026-05-03

What Is Smart City Analytics?
Why Apache Superset for Smart Cities
Core Components of Smart City Data
Deploying Superset for IoT and Sensor Networks
Building Real-Time Traffic and Parking Dashboards
Integrating with D23.io Lakehouse Architecture
Advanced Geospatial Analytics
Security, Compliance, and Scalability
Implementation Strategy and Rollout
ROI and Operational Outcomes

What Is Smart City Analytics?

Smart city analytics represents the systematic collection, processing, and analysis of data from urban infrastructure, sensors, and systems to improve operational efficiency, resource allocation, and citizen services. Unlike traditional city management, which relies on periodic reports and manual intervention, smart city analytics operates in real-time, surfacing actionable insights from continuous data streams.

The data sources are diverse: IoT sensors embedded in traffic lights, parking meters, environmental monitors, water systems, and energy grids; mobile devices and connected vehicles; building management systems; and public service platforms. When aggregated and visualised effectively, this data enables councils and municipalities to respond to congestion within minutes, optimise energy consumption, detect infrastructure failures before they cascade, and make evidence-based decisions about urban planning.

The challenge is not collecting the data—most smart cities already have sensors deployed. The challenge is surfacing the right insights to the right people at the right time. That is where Apache Superset enters the picture. As an open-source data visualisation and exploration platform, Superset transforms raw IoT streams and lakehouse data into intuitive, interactive dashboards that operators, engineers, and strategic planners can query without writing SQL.

For Australian councils and regional authorities, this capability is increasingly critical. Cities like Brisbane, Melbourne, and Sydney are rolling out smart city initiatives to manage traffic congestion, reduce emissions, and improve service delivery. However, many councils struggle to extract value from their data investments because the tools are either too technical (requiring SQL expertise) or too rigid (pre-built reports that don’t adapt to new questions).

Superset bridges that gap. It is lightweight, deployable in weeks rather than months, and integrates seamlessly with modern data lakes and warehouses that ingest IoT telemetry at scale.

Why Apache Superset for Smart Cities

Apache Superset is purpose-built for the data exploration and real-time dashboarding that smart cities demand. Here are the core reasons it outperforms alternatives:

Speed of Deployment

Unlike enterprise BI platforms that require months of configuration, licensing, and vendor lock-in, Superset can be deployed and operational within 4–6 weeks. A typical engagement—such as the $50K D23.io consulting engagement—delivers a complete rollout including architecture design, single sign-on (SSO) integration, semantic layer setup, and operator training in six weeks. For councils operating with constrained IT budgets and tight timelines, this speed is transformative.

Cost Efficiency

Superset is open-source and vendor-neutral. There are no per-user licensing fees, no seat restrictions, and no lock-in contracts. This makes it economically viable for councils that need to scale dashboards across dozens of departments and hundreds of operators without proportional cost increases. The infrastructure footprint is also modest—Superset runs efficiently on standard cloud instances, reducing operational overhead compared to commercial BI suites.

Flexibility and Customisation

Superset’s architecture is modular. You can connect it to any data source (Postgres, Snowflake, BigQuery, Databricks, Delta Lake, etc.), define custom SQL metrics and dimensions, and build bespoke dashboards tailored to specific operational workflows. For smart cities with heterogeneous data sources—traffic sensors, parking systems, environmental monitors, water management platforms—this flexibility is essential.

Real-Time and Near-Real-Time Analytics

Superset supports caching and incremental data refresh, enabling dashboards to reflect current conditions with minimal latency. For traffic management, where a 5-minute delay in detecting congestion can ripple across the network, this responsiveness is critical. Visualising geospatial data with Apache Superset has become a standard pattern for smart city applications, particularly when combined with map-based visualisations that show real-time traffic flow, parking availability, and sensor health across the city.

Semantic Layer and Self-Service

Superset’s semantic layer (dimensions, metrics, calculated fields) allows non-technical operators to build their own queries and explore data without SQL knowledge. A traffic engineer can drag-and-drop metrics to compare congestion patterns across intersections; a sustainability officer can pivot environmental data to track emissions trends. This democratisation of analytics reduces bottlenecks and accelerates decision-making.

Integration with Modern Data Architectures

Smart cities are increasingly adopting lakehouse architectures—data lakes with SQL query capability—to ingest and process IoT streams at scale. Superset integrates natively with these platforms. When you layer Superset on top of a D23.io lakehouse or similar architecture, you create a unified analytics backbone that ingests raw sensor data, processes it through transformation pipelines, and exposes it via interactive dashboards.

Core Components of Smart City Data

Before deploying Superset, you need to understand the data landscape you are working with. Smart city analytics typically involves four primary data streams:

Traffic and Mobility Data

Traffic sensors at intersections report vehicle counts, speeds, and queue lengths. Connected traffic signals report timing and phase information. Bluetooth and WiFi sensors detect vehicle and pedestrian movement. Public transport systems (buses, trams, trains) report real-time location and occupancy. Ride-sharing platforms and mobility-as-a-service providers contribute demand and supply signals.

This data is high-volume and time-sensitive. A single city with 500 intersections, each reporting every 30 seconds, generates 1.44 million data points per day. Superset must be configured to handle this scale without performance degradation.

Parking and Occupancy Data

Smart parking systems use sensors (inductive loops, ultrasonic, or camera-based) to detect occupancy in parking spaces. Parking meters report payment status and expiry times. Mobile apps aggregate demand signals. When integrated into a lakehouse and visualised in Superset, this data enables councils to publish real-time parking availability, reduce circling time (which contributes significantly to urban congestion and emissions), and optimise pricing.

Environmental and Air Quality Data

Fixed air quality monitors report PM2.5, PM10, NO₂, O₃, and other pollutants. Some cities deploy mobile sensors on buses or drones. Weather stations report temperature, humidity, wind speed, and direction. Water quality sensors monitor rivers and coastal areas. When visualised alongside traffic data, environmental metrics reveal correlations between mobility patterns and air quality—critical for emissions reduction strategies.

Energy and Utilities Data

Smart meters on buildings report electricity consumption. District heating systems report flow and temperature. Water systems report usage and pressure. Renewable energy installations (solar, wind) report generation. For councils pursuing net-zero targets, this data is foundational. AI automation for energy increasingly leverages real-time analytics to optimise grid operations and demand response.

Each of these data streams arrives in different formats, at different frequencies, with different quality and latency profiles. A lakehouse architecture (like D23.io) normalises this diversity into a unified schema. Superset then sits atop the lakehouse, providing the query and visualisation layer.

Deploying Superset for IoT and Sensor Networks

Deploying Superset for smart city IoT requires careful planning across infrastructure, connectivity, and data pipeline layers.

Architecture Overview

A typical deployment follows this pattern:

Data Ingestion Layer: IoT sensors and systems emit data (via MQTT, HTTP, or proprietary protocols) to a message broker (Kafka, RabbitMQ) or directly to a data lake (S3, ADLS, GCS).
Processing Layer: Stream processing (Spark Streaming, Flink) or batch jobs (Airflow, Dagster) transform raw sensor data into queryable tables. This is where you clean outliers, interpolate missing values, and aggregate data to appropriate time granularities (1-minute, 5-minute, hourly).
Storage Layer: Processed data lands in a lakehouse (Databricks, Snowflake, BigQuery, D23.io) with columnar optimisation for analytical queries.
Analytics Layer: Superset connects to the lakehouse via a database driver (Postgres, Spark SQL, Snowflake connector) and exposes data through dashboards and ad-hoc exploration.
Consumption Layer: Dashboards are embedded in council portals, mobile apps, or accessed directly by operators.

This layered approach decouples data collection from analytics, allowing you to scale each layer independently. If sensor volume doubles, you scale the ingestion layer; if query concurrency increases, you scale Superset’s caching and database connections.

Configuring Superset for High-Volume Data

Out-of-the-box Superset works well for traditional BI use cases (hundreds of users, gigabytes to terabytes of data). For smart city IoT, you need to tune several parameters:

Database Connection Pooling: Configure Superset’s SQLAlchemy pool to maintain persistent connections to your lakehouse. This reduces connection overhead for frequent queries. Set SQLALCHEMY_POOL_SIZE to match your expected concurrent users (typically 10–50 for a city council).

Caching Strategy: Enable Redis-backed caching for dashboard queries. Set appropriate TTLs (time-to-live) based on data freshness requirements. Traffic data might cache for 30 seconds; historical environmental data might cache for 1 hour. This dramatically reduces load on your data warehouse.

Query Optimisation: Ensure your lakehouse tables are partitioned by date and indexed on common filter columns (intersection_id, sensor_id, timestamp). Superset generates SQL dynamically; well-indexed tables ensure queries complete in milliseconds rather than seconds.

Metric Definitions: Use Superset’s semantic layer to pre-define complex metrics (e.g., “average congestion index by hour”, “parking occupancy rate by zone”). This ensures consistency across dashboards and shields operators from SQL complexity.

Authentication and Access Control

For councils, role-based access control is essential. A traffic engineer should see only traffic data; an environmental officer should see air quality and emissions data. Configure Superset’s RBAC (role-based access control) to enforce these boundaries. Integrate with your council’s identity provider (Azure AD, Okta) via SAML or OAuth for seamless single sign-on.

Building Real-Time Traffic and Parking Dashboards

Traffic and parking are the two most visible smart city use cases. Here’s how to build effective dashboards in Superset:

Traffic Flow Dashboard

A traffic operations centre needs a unified view of network-wide conditions. The dashboard should display:

Map View: A geospatial visualisation showing real-time traffic speed or queue length at each intersection. Superset supports map-based charts (via Mapbox or Deck.gl integration). Colour-code intersections by congestion level (green for free-flow, red for severe congestion). Operators can click on an intersection to drill into detailed metrics.

Time Series: Line charts showing traffic volume, speed, and queue length over the past 24 hours. This reveals patterns (morning peak, evening peak, off-peak) and helps operators anticipate congestion.

Comparison Tables: Tables ranking intersections by congestion, showing which areas are most problematic. Include filters for time-of-day, day-of-week, and weather conditions.

Incident Alerts: A section flagging detected anomalies (sudden congestion spike, sensor failure, accident-related blockage). This can be automated via Superset’s alert system or fed from an external incident management platform.

The key is responsiveness. An operator should be able to load this dashboard and see current conditions within 2–3 seconds. This requires aggressive caching and a well-tuned database.

Parking Availability Dashboard

Citizens increasingly expect to know parking availability before driving to a location. A parking dashboard in Superset should show:

Zone-Level Occupancy: A map or grid showing available spaces in each parking zone. Update this every 1–2 minutes to reflect real-time availability.

Occupancy Trends: Line charts showing occupancy patterns by zone and time-of-day. This data informs dynamic pricing strategies and long-term capacity planning.

Turnover Metrics: Calculate average stay duration and turnover rate per zone. High turnover zones (near shops, restaurants) have different characteristics than low-turnover zones (near offices, residential).

Revenue and Compliance: Track parking revenue and payment compliance. Identify zones with high non-compliance rates, triggering enforcement actions.

This dashboard can be embedded in the council’s citizen-facing mobile app, providing real-time parking guidance. It also feeds internal operations—enforcement teams can prioritise high-violation zones, and revenue teams can optimise pricing.

Integrating with D23.io Lakehouse Architecture

D23.io is an Australian data platform designed for scale and cost efficiency. It combines the benefits of a data lake (cheap, flexible storage) with the queryability of a data warehouse (SQL, ACID transactions). For smart cities, it is an ideal foundation.

Why D23.io for Smart Cities

D23.io is built on open standards (Apache Iceberg for table format, Spark for compute). This means you avoid vendor lock-in and can migrate data or compute layers independently. For councils evaluating smart city platforms, this flexibility is critical—technology choices made today should not constrain decisions five years from now.

D23.io also handles the complexity of ingesting diverse data sources. You can stream IoT data from Kafka, batch-load historical data from APIs, and query everything through a unified SQL interface. Superset connects to D23.io via standard Spark SQL connectors, treating it like any other data warehouse.

Data Ingestion Patterns

When deploying Superset on top of D23.io for smart city analytics, follow these ingestion patterns:

Stream Ingestion for Real-Time Data: Use Kafka or Kinesis to stream high-frequency sensor data (traffic, parking, air quality) into D23.io. A stream processor (Spark Streaming, Flink) deduplicates, validates, and aggregates the data before writing to Iceberg tables. This ensures Superset queries always access clean, deduplicated data.

Batch Ingestion for External Data: Some data sources (weather APIs, public transport schedules, demographic data) are updated on a batch schedule. Use Airflow or Dagster to orchestrate these ingestions, landing data in D23.io on a daily or hourly basis.

Change Data Capture (CDC) for Transactional Systems: If your parking system or traffic signal controller uses a relational database, enable CDC (Change Data Capture) to stream changes into D23.io. This keeps your analytics layer in sync with operational systems.

Once data lands in D23.io, Superset can query it directly. Because Iceberg supports ACID transactions and time-travel queries, you can build dashboards that show both current state and historical trends without data consistency issues.

Semantic Layer in D23.io + Superset

Define your metrics and dimensions in Superset’s semantic layer, not in D23.io. This keeps your lakehouse focused on raw and transformed data, while Superset handles the business logic. For example:

Metric: “Congestion Index” = (actual_flow / capacity) × 100
Dimension: “Time of Day” = CASE WHEN hour BETWEEN 7 AND 10 THEN ‘Morning Peak’ …
Calculated Field: “Parking Occupancy %” = (occupied_spaces / total_spaces) × 100

This separation of concerns makes it easier to iterate on analytics without modifying the lakehouse schema.

Advanced Geospatial Analytics

Smart cities are inherently spatial. Traffic congestion, air quality, and parking availability vary by location. Superset’s geospatial capabilities are essential for effective analysis.

Geospatial Data Types and Visualisations

Superset supports several geospatial visualisations:

Deck.gl Map: A high-performance map visualisation that can render thousands of points (sensors, intersections, parking spaces) with real-time updates. Colour, size, and opacity can encode metrics (e.g., congestion level, air quality index). Users can zoom and pan to explore specific areas.

Mapbox Integration: For more sophisticated map styling and layering, integrate Mapbox. You can overlay multiple layers (traffic flow, air quality, parking availability) and toggle them on/off.

Geohash Heatmap: Aggregate data by geohash (a hierarchical spatial index) to show hotspots. This is useful for identifying congestion zones or air quality problem areas.

Scatter Plot with Lat/Lon: Simple but effective for plotting individual sensor locations coloured by current readings.

Spatial Queries in Superset

Your D23.io lakehouse should store geospatial data in standard formats (WKT, GeoJSON, lat/lon columns). Superset can query this directly. For example:

SELECT 
  intersection_id,
  latitude,
  longitude,
  AVG(congestion_index) as avg_congestion,
  COUNT(*) as sample_count
FROM traffic_sensors
WHERE timestamp > CURRENT_TIMESTAMP - INTERVAL '1 hour'
GROUP BY intersection_id, latitude, longitude

Superset visualises this as a map, with each point coloured by avg_congestion. Operators can click on a point to drill into time-series data for that intersection.

Heatmaps and Density Analysis

For understanding patterns across the city, heatmaps are invaluable. Use Superset to visualise congestion density, air quality gradients, or parking pressure. A heatmap shows where problems are concentrated, guiding resource allocation (e.g., deploying additional enforcement officers to high-violation parking zones, or timing traffic signal changes to address congestion hotspots).

Security, Compliance, and Scalability

For councils, security and compliance are non-negotiable. Smart city data includes sensitive information about citizen movement, infrastructure vulnerabilities, and operational procedures. Superset deployments must be hardened accordingly.

Authentication and Authorisation

Deploy Superset behind a reverse proxy (Nginx, Apache) with TLS encryption. Enable LDAP or SAML authentication to integrate with your council’s identity provider. Configure role-based access control (RBAC) so that only authorised users can access sensitive dashboards.

For example:

Traffic engineers see traffic and congestion data.
Environmental officers see air quality and emissions data.
Finance teams see parking revenue and cost data.
Executives see high-level KPIs and strategic metrics.

Superset’s RBAC system supports this granularity. You can restrict access at the dashboard level, the dataset level, or even specific rows/columns within a dataset.

Data Encryption and Privacy

Ensure all data in transit is encrypted (TLS for API calls, encrypted database connections). At rest, enable encryption on your D23.io lakehouse (most cloud providers offer transparent encryption). If you are handling personally identifiable information (PII)—such as parking payment records linked to vehicle owners—apply data masking or pseudonymisation in Superset to prevent accidental exposure.

Audit Logging and Compliance

Enable Superset’s audit logging to track who accessed which dashboards and when. This is essential for compliance with privacy regulations and internal governance. Store audit logs in a separate, immutable location (e.g., a read-only S3 bucket).

For councils pursuing SOC 2 or ISO 27001 compliance, smart city analytics platforms must be included in your audit scope. PADISO can guide you through security audit preparation, ensuring your Superset deployment meets compliance requirements.

Scalability Considerations

As your smart city program matures, you will add more sensors, more dashboards, and more users. Plan for scale:

Horizontal Scaling: Deploy Superset on Kubernetes with multiple replicas behind a load balancer. This distributes query load and ensures high availability.

Database Scaling: Ensure your D23.io lakehouse can handle increasing query concurrency. Use table partitioning and indexing to keep query times sub-second even as data volume grows.

Caching Strategy: Implement a multi-tier caching approach. Cache frequently accessed dashboards in Superset’s Redis layer; cache hot tables in the database layer; and leverage cloud provider caching (CDN for static assets).

Monitoring and Alerting: Set up monitoring for Superset’s health (CPU, memory, query latency) and alert on anomalies. A dashboard that loads in 2 seconds today might load in 10 seconds next month if you do not monitor and optimise proactively.

Implementation Strategy and Rollout

Successful smart city analytics deployments follow a phased approach. Here is a proven rollout strategy:

Phase 1: Proof of Concept (Weeks 1–4)

Start with a single data source and a single use case. For example:

Ingest traffic sensor data from 10 key intersections.
Build a single dashboard showing real-time congestion.
Validate that Superset can handle the data volume and query patterns.

Invest time in understanding your data. Are sensors reporting accurate, complete data? Are there gaps or outliers? This phase is about learning, not scaling.

Phase 2: MVP Deployment (Weeks 5–12)

Expand to multiple data sources (traffic, parking, air quality) and multiple dashboards. Integrate with your D23.io lakehouse. Deploy Superset in a production environment with proper security, backup, and disaster recovery.

Train a core group of power users (traffic engineers, environmental officers, planners). Get their feedback on dashboard usability and metric definitions. Iterate based on their input.

Phase 3: Scaling and Optimisation (Weeks 13–26)

Once the core dashboards are stable, focus on optimisation:

Tune database queries and caching.
Expand access to broader user groups (citizen-facing dashboards, executive reporting).
Integrate Superset with external systems (incident management, work order systems, citizen apps).
Automate data ingestion pipelines and dashboard refresh schedules.

Phase 4: Advanced Analytics (Weeks 27+)

With a solid analytics foundation in place, explore advanced use cases:

Predictive analytics (forecasting congestion, predicting equipment failures).
Anomaly detection (identifying unusual patterns that warrant investigation).
Optimisation models (recommending signal timing, pricing strategies).

Many of these advanced use cases leverage agentic AI and Apache Superset to let non-technical staff query complex data naturally. For example, a planner might ask, “Show me the top 5 intersections with the highest congestion growth over the past month,” and an AI agent translates that into a Superset query.

Governance and Change Management

Establish a governance structure for your smart city analytics program:

Data Governance: Define who owns each data source, who can modify it, and what quality standards apply.
Dashboard Governance: Establish a process for creating, reviewing, and retiring dashboards. Prevent dashboard sprawl (hundreds of unused dashboards cluttering the interface).
Access Governance: Document who has access to what data and why. Review access quarterly.
Change Management: When you modify a metric definition or dashboard, communicate the change to all users. Version control your dashboard definitions.

This governance overhead might seem burdensome initially, but it pays dividends as your program scales. A well-governed analytics platform becomes a trusted source of truth; a poorly governed one becomes a source of confusion and mistrust.

ROI and Operational Outcomes

The value of smart city analytics is measured in operational outcomes, not just data volume or dashboard count.

Traffic and Congestion Reduction

Cities that deploy real-time traffic analytics typically see 5–15% reductions in average congestion time. This translates to:

Reduced emissions (fewer vehicles idling in traffic).
Improved air quality (correlated with reduced vehicle-hours).
Faster emergency response (ambulances and fire trucks face less congestion).
Increased economic productivity (commuters and delivery vehicles spend less time in traffic).

A city of 1 million people, with an average commute of 30 minutes, saves 2.5 million person-hours per year with a 5% congestion reduction. At an average hourly rate of $30, that is $75 million in economic value annually.

Parking Revenue and Compliance

Real-time parking analytics enable dynamic pricing, which increases revenue and improves space utilisation. Cities report 10–20% revenue increases and 85%+ occupancy rates (compared to 60–70% without dynamic pricing). Additionally, enforcement becomes data-driven; officers deploy to high-violation zones, improving compliance without increasing headcount.

Environmental and Health Outcomes

Air quality dashboards that correlate traffic, weather, and emissions data enable targeted interventions. When air quality exceeds safe thresholds, councils can:

Alert vulnerable populations (elderly, children, those with respiratory conditions).
Recommend route changes to avoid high-pollution areas.
Adjust traffic signal timing to reduce emissions in specific zones.
Coordinate with public transport to encourage mode shift.

Studies show that real-time air quality information, combined with targeted interventions, can reduce peak pollution levels by 10–20% and improve public health outcomes measurably.

Operational Efficiency

Internally, smart city analytics reduces operational costs. Maintenance teams use sensor data to predict equipment failures and schedule maintenance proactively, reducing emergency repairs. Energy teams optimise grid operations, reducing peak demand and lowering electricity costs. Water teams detect leaks earlier, reducing water loss.

These efficiency gains typically offset the cost of the analytics platform within 12–18 months.

Strategic Insights and Planning

Beyond immediate operational gains, smart city analytics provides strategic insights that inform long-term planning. Historical data reveals trends (e.g., increasing congestion in a specific corridor, shifting parking demand due to new developments). These insights guide capital investments (e.g., expanding public transport, building parking structures, redesigning intersections).

A well-designed smart city analytics platform becomes the foundation for evidence-based urban planning, replacing intuition and politics with data-driven decision-making.

Next Steps and Getting Started

If you are a council or municipality considering smart city analytics, here is how to get started:

Step 1: Audit Your Data

Inventory all the data sources you currently operate: traffic sensors, parking systems, environmental monitors, energy systems, water systems. Assess data quality, frequency, and accessibility. Identify gaps where additional sensors or systems might be needed.

Step 2: Define Your Use Cases

Work with stakeholders (traffic engineers, planners, sustainability officers, finance teams) to define the questions you want to answer. These become your use cases. Prioritise them by impact and feasibility.

Step 3: Evaluate Platforms

Compare Apache Superset with alternatives (Tableau, Power BI, Looker). Consider cost, deployment time, flexibility, and vendor lock-in. For councils with constrained budgets and tight timelines, Superset typically wins.

Step 4: Partner with Experts

Smart city analytics involves data engineering (pipelines, lakehouse), analytics engineering (metrics, dashboards), and domain expertise (understanding traffic, parking, air quality). You will benefit from expert guidance. PADISO offers fractional CTO and platform engineering services tailored to councils and municipalities pursuing smart city initiatives.

Our experience includes deploying Superset for Australian smart city programs, integrating with D23.io lakehouses, and surfacing operational insights to councils. We have shipped traffic dashboards, parking analytics, and environmental monitoring systems that have delivered measurable outcomes: reduced congestion, increased parking revenue, and improved air quality.

Step 5: Plan Your Rollout

Follow the phased approach outlined above. Start with a proof of concept, validate the approach, and scale methodically. Allocate time for governance, training, and change management—these are as important as the technology itself.

Step 6: Measure and Iterate

Once your analytics platform is live, measure outcomes rigorously. Are you achieving the congestion reductions you projected? Is parking revenue increasing? Are air quality interventions effective? Use these measurements to iterate—add new data sources, refine dashboards, and expand to new use cases.

Smart city analytics is not a one-time project; it is an ongoing capability that matures over time. The councils that succeed are those that treat it as a strategic investment and commit to continuous improvement.

Conclusion

Smart city analytics on Apache Superset represents a practical, cost-effective path to evidence-based urban operations. By ingesting IoT data into a lakehouse architecture like D23.io and exposing it through Superset dashboards, councils can respond to congestion in real-time, optimise parking, improve air quality, and make strategic planning decisions grounded in data.

The technology is proven. Organisations worldwide use Superset for mission-critical analytics. The challenge is not whether Superset works—it does—but whether your council is ready to commit to the organisational change required to make smart city analytics a reality.

If you are ready to explore smart city analytics for your council or municipality, PADISO can help. We have deployed Superset for Australian smart city programs, guided councils through security audits and compliance, and built the data pipelines and dashboards that surface operational insights. Contact us to discuss your smart city vision and how we can help you ship it.

The cities that lead on smart city analytics today will be the most liveable, sustainable, and economically vibrant cities tomorrow. The question is not whether to invest in smart city analytics, but how quickly you can get started.