Apache Superset for Product Analytics: A D23.io Implementation Pattern

Table of Contents

1. Why Apache Superset for Product Analytics
2. The D23.io Implementation Pattern
3. Data Modelling for Product Insights
4. Dashboard Design and Architecture
5. Sharing, Permissions, and Governance
6. Production Deployment and Scaling
7. The Typical D23.io Engagement Scope
8. Real-World Outcomes and Metrics
9. Getting Started: Next Steps

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Why Apache Superset for Product Analytics

Product analytics sits at the intersection of engineering, product, and business intelligence. You need speed—dashboards live for weeks, not months. You need flexibility—metrics change as hypotheses evolve. You need cost control—per-seat BI licences kill budgets at scale. And you need ownership—your team should understand the data layer, not depend on a vendor’s black box.

Apache Superset is purpose-built for this. It’s an open-source, modern data exploration and visualisation platform that runs on your infrastructure, scales with your data, and gives your team full control over the analytics layer. Unlike traditional BI tools (Tableau, Looker, Power BI), Superset doesn’t lock you into per-user licensing or vendor-managed infrastructure. Unlike basic dashboarding (Grafana, Metabase), Superset is built for SQL-first, exploratory analytics at scale.

At PADISO, we’ve shipped product analytics stacks with Superset across seed-stage startups, Series-B scale-ups, and mid-market enterprises. The pattern is consistent: teams move from spreadsheets or ad-hoc SQL queries to a governed, self-service analytics layer in 4–8 weeks. Revenue teams see conversion funnels in real time. Product teams iterate on feature impact without waiting for BI. Operations teams cut reporting time by 60–80%.

But Superset alone isn’t enough. The implementation matters. Data modelling, dashboard architecture, sharing patterns, and production governance determine whether you ship a toy or a tool your team actually uses.

This guide covers the full pattern—how to design, build, and operate product analytics on Superset in production. We’ll walk through the technical decisions, the organisational patterns, and the typical scope of a PADISO D23.io engagement that gets you from idea to analytics-driven decision-making in weeks, not quarters.

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The D23.io Implementation Pattern

D23.io is PADISO’s product analytics and data platform framework. It’s built around a simple principle: analytics should ship as fast as product code. No six-month BI projects. No vendor lock-in. No data warehouse that costs $50k/month and sits unused.

The pattern has three layers:

Layer 1: Data Ingestion and Transformation

Your product generates events—page views, clicks, form submissions, API calls, transactions. These events need to flow into a queryable data store with minimal latency and maximum reliability.

D23.io uses a combination of tools depending on your scale:

• For early-stage (< 1M events/day): Direct database or event-streaming service (Segment, mParticle, or in-house event collection) into PostgreSQL or a lightweight data warehouse like DuckDB.
• For growth-stage (1M–100M events/day): Event streaming (Kafka, Kinesis) into a columnar data warehouse like ClickHouse or Snowflake, with transformation via dbt or Airflow.
• For scale-up (> 100M events/day): Multi-region event ingestion, partitioned warehouse architecture, and real-time aggregation layers (Materialize, RisingWave) to keep dashboard load times under 2 seconds.

The key decision: batch vs. real-time. Product analytics doesn’t always need sub-second updates. Most teams are happy with 5–15 minute latency. This simplifies architecture, cuts infrastructure cost by 40–60%, and lets you focus on data quality instead of operational complexity.

Layer 2: Semantic Layer and Metrics

Raw events aren’t analytics. You need a semantic layer—a set of agreed definitions for what “active user,” “conversion,” “churn,” and “revenue” actually mean.

This is where most teams fail. They build dashboards on raw events, each analyst writes their own SQL, and suddenly you have five different definitions of “monthly active users.” Revenue teams and product teams argue. Trust breaks down.

D23.io enforces a single source of truth via:

• dbt models for transformation and metric definitions.
• Superset datasets that wrap dbt output with business logic (date filters, currency conversions, cohort definitions).
• Superset native filters that apply consistently across all dashboards.

This takes 1–2 weeks to set up properly. It saves 10+ hours per week in future analytics work and eliminates metric disagreements.

Layer 3: Dashboards, Exploration, and Sharing

Superset is the presentation layer. It’s where product teams, revenue teams, and executives see the data.

D23.io uses a three-tier dashboard architecture:

• Tier 1: Executive dashboards (4–6 charts, 60-second load time, updated daily). KPIs, revenue, user growth, churn. Designed for board meetings and investor updates.
• Tier 2: Functional dashboards (15–30 charts, 5–10 minute refresh). Conversion funnels, feature adoption, cohort analysis. Designed for product, marketing, and operations teams.
• Tier 3: Exploration layer (ad-hoc SQL, saved queries, drill-downs). For analysts and engineers who need to dig deeper.

Each tier uses different Superset features, different data sources, and different refresh schedules. This keeps the system fast and usable at scale.

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Data Modelling for Product Insights

Data modelling is where the work happens. A good data model makes analytics trivial. A bad one makes every question take a week to answer.

Event Schema Design

Start with your event schema. Every event should capture:

• Timestamp (UTC, precise to millisecond or better).
• User ID (consistent across sessions and devices).
• Session ID (groups events into user interactions).
• Event type (page_view, click, form_submit, purchase, etc.).
• Event properties (what happened—button name, form field, product ID, amount, etc.).
• User properties (cohort, plan, region, signup date—denormalised for speed).
• Context (device, browser, OS, IP, referrer—useful for filtering and segmentation).

For product analytics specifically, avoid the trap of logging too much. Every extra property doubles your storage cost and makes queries slower. Log only what you’ll actually filter or segment on.

A well-designed event schema looks like this:

Events table:
- event_id (UUID)
- timestamp (TIMESTAMP)
- user_id (STRING)
- session_id (STRING)
- event_type (STRING)
- event_properties (JSON or STRUCT)
- user_properties (JSON or STRUCT)
- context (JSON or STRUCT)

If you’re using a tool like Segment or Amplitude, they’ll handle schema enforcement. If you’re building in-house, use Apache Superset’s data validation patterns or enforce schema via Avro/Protobuf at ingestion time.

Building the Semantic Layer with dbt

Once events are flowing, transform them into business metrics. This is where dbt shines.

A typical dbt project for product analytics has these layers:

Staging layer (stg_events): Raw events, cleaned and standardised. Fix timestamp bugs, parse JSON properties, add surrogate keys.

Intermediate layer (int_*): Event-level transformations. Funnel steps, session boundaries, user journey staging.

Marts layer (fct_* and dim_*): Business-ready tables. fct_conversions (one row per conversion event with user, product, and outcome). fct_feature_adoption (one row per user-feature pair with adoption date and intensity). dim_users (one row per user with cohort, plan, lifetime value).

For product analytics, focus on these marts:

• fct_events – All events, deduplicated, with user and session context. Typically 10–100M rows for a Series-B startup.
• fct_conversions – One row per conversion funnel step (signup → trial → paid). Includes source, cohort, time-to-conversion, and outcome.
• fct_feature_adoption – One row per user-feature pair. Adoption date, usage intensity, retention at 7d/30d/90d.
• fct_churn – One row per user with churn date, last active date, and churn reason (inferred from event patterns).
• dim_users – One row per user with denormalised properties (plan, region, signup cohort, LTV, segment).
• dim_dates – Standard date dimension for time-based joins.

Each mart is built incrementally. Start with fct_events and dim_users. Add conversion and adoption marts as product questions emerge. This takes 2–3 weeks to mature, but it’s worth the investment.

Superset Datasets and Business Logic

Once dbt marts are built, wrap them in Superset datasets. A Superset dataset is a SQL query (or table) plus metadata—column descriptions, aggregation rules, and filter definitions.

For each mart, create a Superset dataset with:

• Columns defined: Data type, description, aggregation method (sum, count, avg, distinct count).
• Date fields marked: Superset uses these for time-based filtering.
• Calculated columns added: If you need a derived metric (LTV = total_revenue / user_count), define it here, not in every dashboard.
• Row-level security (RLS) applied: If teams need different data (sales sees their region only), set RLS rules here.

Example: A conversions dataset wraps the fct_conversions mart and adds:

• Calculated column: days_to_conversion = conversion_date - signup_date.
• Calculated column: cohort_size = count of users in signup cohort.
• Filter: conversion_date >= {{ filter_date_start }} (for dashboard-level date filtering).

This takes 3–4 hours per dataset but saves 10+ hours per week in dashboard building.

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Dashboard Design and Architecture

Dashboards are where insights live or die. A well-designed dashboard answers a specific question in 60 seconds. A poorly designed one overwhelms the user with 40 charts and no clear story.

Executive Dashboard Pattern

Executive dashboards have one job: show me the health of the business in 60 seconds.

For a SaaS company, that’s typically:

• ARR and MRR (top-left, large number, trend line).
• Churn rate (monthly and annual, with benchmark comparison).
• CAC and LTV (with LTV:CAC ratio highlighted).
• New MRR (from new customers and expansion).
• Expansion revenue (upsells and cross-sells).
• Gross margin (revenue minus COGS).
• Payback period (months to recover CAC).

Each chart refreshes daily. Load time is under 3 seconds. No drill-downs, no ad-hoc filters—just the numbers that matter.

For a marketplace or consumer app:

• DAU and WAU (daily and weekly active users, with growth trend).
• Engagement rate (% of DAU who perform core action, e.g., post, message, transact).
• Retention curves (D1, D7, D30 retention by cohort).
• ARPU (average revenue per user, with trend).
• Conversion funnel (signup → activation → paid, with drop-off rates).

Design rule: One metric per chart. No multi-series line charts. No stacked bars. Make it obvious what’s up and what’s down.

Functional Dashboard Patterns

Functional dashboards are for teams that own a metric. Product teams own activation and retention. Revenue teams own conversion and expansion. Operations owns churn and payback.

A functional dashboard has 15–30 charts organised by question:

Activation dashboard (Product team):

• Signup volume (daily, with trend).
• Activation rate (% of signups completing onboarding, by day).
• Time-to-activation (median days from signup to first core action, by cohort).
• Feature adoption (% of activated users using each feature, by week).
• Onboarding funnel (signup → email verified → product opened → core action, with drop-off).
• Activation by source (organic, paid, referral—which converts best?).

Conversion dashboard (Revenue team):

• Trial starts (daily, by source).
• Trial-to-paid conversion (% of trial users converting to paid, by trial length).
• Days to conversion (median days from trial start to first paid transaction, by cohort).
• Price sensitivity (conversion rate by price point).
• Expansion revenue (% of paid users upgrading or expanding, by plan).

Retention dashboard (Product team):

• Retention curves (D1, D7, D30, D90, D365 retention, by cohort and plan).
• Churn rate (monthly, with trend and benchmark).
• Churn by reason (inferred from event patterns—low engagement, no logins, feature deprecation).
• Reactivation rate (% of churned users who return, by time since churn).
• Engagement distribution (% of users in engagement quintiles—heavy, active, moderate, light, dormant).

Each dashboard has a consistent layout:

1. Top row: KPI cards (single metric, large font, colour-coded trend).
2. Middle rows: Time-series charts (trend lines, week-over-week comparison).
3. Bottom rows: Breakdown charts (by cohort, source, plan, region—whatever segments matter).

Each chart has a title that answers a question (“Which cohort has the highest D7 retention?” not “Retention by Cohort”). Hover text shows exact numbers. Colours are consistent (green = good, red = bad, grey = neutral).

Exploration and Ad-Hoc Analysis

Not every question fits into a dashboard. Some teams need to explore—drill into a specific cohort, test a hypothesis, compare two time periods side-by-side.

Superset’s native query builder and SQL lab enable this. But ad-hoc queries are slow if your data warehouse isn’t optimised.

For production analytics, set up a dedicated exploration database:

• Dedicated compute: A separate Superset instance or warehouse cluster for ad-hoc queries, so they don’t slow down dashboard refreshes.
• Aggregated tables: Pre-aggregate common breakdowns (by day, by cohort, by plan) so exploratory queries run in seconds, not minutes.
• Query limits: Set max query time to 5 minutes. If a query takes longer, it needs optimisation or a pre-aggregated table.
• Caching: Cache query results for 15 minutes. Most exploratory queries are repeated within hours.

This keeps the system responsive even when analysts are running heavy queries.

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Sharing, Permissions, and Governance

Analytics is only useful if people can find and access it. Sharing and permissions determine adoption.

Role-Based Access Control (RBAC)

Superset has built-in RBAC. Define roles and assign permissions:

• Viewer: Can see dashboards and saved charts. No edit, no SQL.
• Analyst: Can see dashboards, edit charts, run SQL queries. No dataset creation, no user management.
• Admin: Full access. Can create datasets, manage users, configure Superset.

For most teams:

• Executives: Viewer (executive dashboard only).
• Product teams: Analyst (product and activation dashboards + exploration).
• Revenue teams: Analyst (conversion and expansion dashboards + exploration).
• Data team: Admin (owns datasets, data quality, new dashboards).

Use Superset’s row-level security (RLS) to limit data by region, plan, or team. A sales rep in APAC sees only APAC data. A customer success manager sees only their assigned accounts.

Dashboard Discoverability

If people can’t find a dashboard, they won’t use it. Superset’s default search is weak. Improve discoverability with:

• Naming convention: [Team] [Question]. Example: [Product] Activation Funnel, [Revenue] Expansion by Plan. This makes dashboards sortable and searchable.
• Dashboard collections: Group related dashboards. Product Analytics, Revenue Analytics, Operations Analytics. Users browse collections, not a flat list.
• Descriptions: Every dashboard has a 1-sentence description. What question does it answer? Who should use it?
• Links: Embed links in Superset dashboard descriptions to related dashboards. “See also: Retention Dashboard.”
• Slack integration: When a new dashboard launches, post it to Slack with a screenshot. Use Superset’s alert and reporting features to push key metrics to Slack daily.

Data Governance and Quality

Garbage in, garbage out. If your data is wrong, your dashboards are wrong.

Set up data governance:

• Data dictionary: Document every table, column, and metric. What does it mean? How is it calculated? Who owns it? Use dbt’s documentation feature to auto-generate this.
• Data quality checks: Automated tests for common issues. Are event timestamps monotonically increasing? Are user IDs never null? Are revenue amounts always positive? Use dbt’s tests or a dedicated data quality tool (Great Expectations, Soda).
• SLA for data freshness: “Dashboards refresh every 6 hours.” “Event data is available within 15 minutes.” Document this and stick to it.
• Incident response: When data quality issues arise, have a process. Alert the data team, pause dashboards if needed, investigate, fix, and communicate the outage.

At PADISO, we typically spend 1–2 weeks on data governance setup. It feels like overhead but prevents weeks of downstream confusion.

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Production Deployment and Scaling

Building Superset locally is easy. Running it in production at scale is different.

Infrastructure and Deployment

Superset is a Python web application. Deploy it like any other app:

• Containerise: Docker image with Superset, dependencies, and custom plugins.
• Orchestrate: Kubernetes (EKS, GKE, AKS) for multi-region, high-availability deployments. Or managed services (Preset, which is Superset-as-a-service).
• Database: PostgreSQL for Superset metadata (dashboards, users, permissions). Separate from your analytics database.
• Cache: Redis for query result caching and session management.
• Message queue: Celery + Redis or RabbitMQ for background jobs (dashboard refreshes, alerts).

A typical production stack looks like:

Superset (web) ← PostgreSQL (metadata)
     ↓
  Celery (background jobs)
     ↓
Analytics database (ClickHouse, Snowflake, Postgres)

For most teams, this runs on a single Kubernetes cluster with 2–4 nodes. Cost is $500–2000/month. Managed Superset (Preset) is $1000–5000/month depending on usage.

Query Performance and Caching

As dashboards scale, query performance becomes critical. A dashboard that takes 30 seconds to load won’t be used.

Optimise with:

• Query caching: Cache results for 5–15 minutes. Most dashboards don’t need real-time data.
• Aggregation tables: Pre-compute common breakdowns (daily by cohort, by plan, by region). Queries against aggregation tables run 10–100x faster.
• Database indexes: Index on timestamp, user_id, and event_type. Most queries filter on these.
• Partitioning: Partition large tables by date. Queries that filter on a date range scan only relevant partitions.
• Columnar storage: Use ClickHouse, Snowflake, or Redshift (columnar databases). Row-oriented databases (PostgreSQL) are 10x slower for analytics queries.

For a Series-B startup with 10M events/day:

• Event table: ClickHouse, partitioned by day, indexed on timestamp and user_id. Queries run in 1–5 seconds.
• Aggregation tables: Pre-computed daily summaries (events by user, by cohort, by plan). Queries run in 100–500ms.
• Dashboard caching: 15-minute cache. Most dashboards load in < 1 second.

Monitoring and Alerting

Production analytics systems need monitoring.

• Dashboard health: Track load times, query errors, and cache hit rates. Alert if a dashboard takes > 10 seconds to load.
• Data freshness: Confirm that data is arriving on schedule. Alert if ingestion is delayed by > 1 hour.
• Query performance: Log slow queries (> 5 seconds). Identify which dashboards are slow and optimise them.
• Uptime: Superset should be up 99.9%+. Use health checks and auto-restart on failure.

Use Superset’s native alerting (based on query results) and infrastructure monitoring (Datadog, New Relic, Prometheus) to catch issues before users notice.

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The Typical D23.io Engagement Scope

At PADISO, we run product analytics engagements across Australia and the US. The typical scope looks like this:

Phase 1: Discovery and Planning (Week 1)

Deliverables:

• Event schema audit (what data are you currently collecting? what gaps exist?).
• Analytics requirements document (what questions do executives, product, and revenue teams need answered?).
• Data architecture proposal (which data warehouse? which transformation tool? which BI layer?).
• Timeline and resource plan.

Effort: 40 hours.

Outcome: Shared understanding of the problem and the solution path.

Phase 2: Data Infrastructure Setup (Weeks 2–3)

Deliverables:

• Data warehouse provisioned (ClickHouse, Snowflake, or managed Postgres).
• Event ingestion pipeline set up (Segment, mParticle, or in-house Kafka → warehouse).
• dbt project initialised with staging and intermediate layers.
• Superset instance deployed (self-hosted or Preset).

Effort: 80 hours.

Outcome: Data is flowing. You can query raw events. Foundation is ready for analytics.

Phase 3: Semantic Layer Build (Weeks 4–5)

Deliverables:

• dbt marts built (fct_events, fct_conversions, fct_feature_adoption, dim_users).
• Data quality tests implemented.
• Superset datasets created and configured.
• RLS rules applied (if needed).

Effort: 100 hours.

Outcome: Business-ready tables. Analytics team can build dashboards without writing SQL.

Phase 4: Dashboard Build (Weeks 6–8)

Deliverables:

• Executive dashboard (KPIs, trends, benchmarks).
• 3–4 functional dashboards (product, revenue, operations).
• Exploration layer set up (saved queries, drill-downs).
• Sharing and permissions configured.
• Documentation and training.

Effort: 120 hours.

Outcome: Dashboards live. Teams are using analytics to make decisions.

Phase 5: Handoff and Optimisation (Weeks 9–10)

Deliverables:

• Performance optimisation (caching, aggregation tables, query tuning).
• Monitoring and alerting configured.
• On-call runbook for data issues.
• Training for analytics team and stakeholders.
• Handoff to in-house team.

Effort: 60 hours.

Outcome: System is production-ready. Your team owns it.

Total Engagement Scope

Duration: 10 weeks (2.5 months).

Effort: 400 hours (2 FTE for 10 weeks or 1 senior + 1 mid-level for 10 weeks).

Cost: $80k–150k depending on complexity and team seniority (at PADISO rates).

Team composition:

• 1 data architect (weeks 1–3, 8–10).
• 1 dbt/analytics engineer (weeks 3–7).
• 1 Superset/BI engineer (weeks 4–8).
• 1 DevOps/platform engineer (weeks 2–3, 9–10).

Many teams run this with a smaller team (1–2 people) over a longer timeline (4–6 months). PADISO’s model is to compress it with focused effort and handoff quickly.

What’s Included

• Architecture design and vendor selection.
• Infrastructure setup and deployment.
• dbt project and data modelling.
• Superset instance and dashboard build.
• Data quality and monitoring.
• Training and documentation.
• 4 weeks of post-launch support (bug fixes, optimisation, questions).

What’s Not Included

• Ongoing data engineering (new marts, new dashboards). This is your team’s job post-launch, or you hire a fractional data engineer.
• Advanced features (machine learning, predictive analytics, real-time streaming). We can design this, but it’s a separate engagement.
• Custom Superset plugins or integrations. We use vanilla Superset.

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Real-World Outcomes and Metrics

Here’s what teams achieve with a D23.io engagement:

Time to Insight

Before: Analysts write ad-hoc SQL queries. A simple question (“What’s our DAU trend?”) takes 2–4 hours.

After: Product teams pull up a dashboard. The same question is answered in 30 seconds. New questions (“What’s DAU by plan? By region? By cohort?”) are answered in 2–5 minutes via drill-downs and filters.

Impact: 10–20 hours/week saved across product, revenue, and operations teams.

Decision Quality

Before: Teams rely on gut feel or outdated reports. Revenue team doesn’t know their churn rate. Product team guesses at feature adoption.

After: Every decision is backed by data. Churn is tracked daily. Feature adoption is visible in real-time. Teams iterate faster because they see the impact of changes within days.

Impact: 30–40% faster iteration cycle. Product launches ship with clearer success metrics.

Cost Reduction

Before: Per-seat BI licence ($2k–5k/user/year). For a 50-person company, that’s $100k–250k/year. Plus a data warehouse that’s over-provisioned and under-used.

After: Open-source Superset ($500–2k/month for infrastructure) or managed Superset ($1k–5k/month). Data warehouse is right-sized for actual usage.

Impact: 60–80% reduction in BI and analytics infrastructure costs.

Hiring and Retention

Before: Analytics work is boring and repetitive. Writing the same report every week kills morale.

After: Analytics team owns the semantic layer and data quality. They build dashboards, not reports. Work is more interesting.

Impact: Easier to hire and retain analytics talent. Team moves from “report writers” to “data engineers.”

Compliance and Governance

Before: No data governance. Everyone has their own definitions of metrics. Audits are messy.

After: Single source of truth. All metrics are documented and versioned. Data quality is monitored. Audits are straightforward.

Impact: Easier to pass SOC 2 and ISO 27001 audits. Risk of data errors is lower.

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Getting Started: Next Steps

If you’re building product analytics, here’s how to start:

Step 1: Audit Your Current State

Answer these questions:

• What data are you currently collecting? (events, logs, transactions)
• Where does it live? (database, data warehouse, third-party tool)
• Who uses it? (product, revenue, operations, executives)
• What questions are you trying to answer? (activation, retention, revenue, churn)
• What’s broken about your current setup? (slow, expensive, inflexible, siloed)

This takes 2–4 hours and clarifies the scope.

Step 2: Define Your Analytics Strategy

Work with your leadership team to align on:

• North Star Metric: What one metric best reflects business health? (DAU, MRR, LTV, etc.)
• Key Metrics by Function: What does each team need to track? (product: activation, retention; revenue: conversion, expansion; operations: churn, payback)
• Cadence: How often do you need updated data? (real-time, hourly, daily, weekly)
• Budget: What can you spend on analytics infrastructure and headcount?

This takes 4–8 hours and guides your implementation.

Step 3: Choose Your Tech Stack

For most teams:

• Data warehouse: ClickHouse (cost-effective, fast) or Snowflake (fully managed, more expensive).
• Transformation: dbt (industry standard, open-source).
• BI layer: Apache Superset (open-source, flexible) or Preset (managed Superset).
• Orchestration: Airflow (if you have a data engineer) or managed tools like Fivetran/Stitch (if you don’t).

This takes 4–8 hours of research and evaluation.

Step 4: Build or Partner

You have three options:

1. Build in-house: Hire a data engineer and BI engineer. Takes 4–6 months. Requires ongoing investment.
2. Partner with a vendor: Use a managed analytics platform (Amplitude, Mixpanel, Heap). Fast, but expensive and inflexible.
3. Partner with a studio: Work with PADISO or a similar platform development partner. You get a production system in 10 weeks and transfer ownership to your team. This is the D23.io pattern.

For most growth-stage startups, option 3 makes sense. You get a production system fast, your team learns the stack, and you avoid hiring delays.

Step 5: Implement and Iterate

Once you’ve chosen your approach, execute:

• Weeks 1–3: Data infrastructure and ingestion.
• Weeks 4–7: Semantic layer and dashboards.
• Weeks 8–10: Optimisation and handoff.
• Ongoing: New dashboards, new metrics, performance tuning.

Analytics is never “done.” New questions emerge. Data patterns change. Your system needs to evolve with your business.

Why Partner with PADISO

If you’re a founder or operator in Sydney, Melbourne, or across Australia, PADISO can help. We specialise in platform development for startups and scale-ups. We’ve shipped product analytics stacks with Superset for 50+ companies. We understand Australian startups—the constraints, the growth patterns, the ambitions.

We also offer fractional CTO services if you need ongoing technical leadership. Our team can help with architecture, hiring, and vendor decisions.

For teams in the US, we have platform development hubs in San Francisco, Seattle, Austin, and Chicago. Same pattern, same outcomes.

If you need security compliance (SOC 2, ISO 27001) alongside your analytics build, we can integrate that into the engagement. Compliance and analytics often share infrastructure and governance, so it’s efficient to tackle both together.

The Bottom Line

Product analytics doesn’t have to be expensive, slow, or complicated. Apache Superset is a production-grade, open-source tool that powers analytics at scale. The D23.io pattern is a proven way to build it—data infrastructure → semantic layer → dashboards → handoff. In 10 weeks, your team has a system that answers questions in seconds and costs a fraction of traditional BI.

The hard part isn’t the technology. It’s the data modelling, the dashboard design, the governance, and the handoff. That’s where PADISO adds value. We’ve done this 50+ times. We know where teams stumble. We build systems that last.

If you’re ready to ship product analytics, let’s talk. Book a call with our team—we’ll audit your current state, propose a plan, and give you a timeline and cost estimate. No obligation. Just a conversation between operators.

Book a call with PADISO or explore our platform development services to get started.