Where to go next | GraphJSON Docs

Once you are comfortable with collect → visualize → export, choose the next step based on the problem you are solving.

Ask more complex questions#

Use the SQL notebook for joins, window functions, cohort analysis, deduplication, or calculations that do not map cleanly to visualizer controls.

Then work through:

Put analytics in your product#

Start with Embed graphs to understand static and dynamic embeds.

For internal reporting and read-only sharing, use Manage and share dashboards.

For user-specific views, follow Personalized dashboards. It explains how to keep the API key on your server while changing a filter for each authenticated user.

Before a customer-facing launch, use Production embedded analytics for tenant isolation, caching, states, accessibility, and testing.

If you have strict accessibility, localization, or design-system requirements, continue with Accessible and localized embedded analytics.

For build-time generation, use Static deployments.

For a fully native React surface, use the Data API frontend reference. For an aggregate page that anyone can view, use Public metrics and status dashboards.

Harden ingestion#

Review:

Operate analytics and customer workflows#

Use:

Structured application logs for queryable request and job outcomes
Background jobs and queue analytics for attempt, retry, latency, backlog, and workflow analysis
Security audit event analytics for trusted actor-action-resource investigations
SLOs, error budgets, and alerts for reliability analysis with explicit paging boundaries
Feature flags and rollout analytics for progressive-release exposure and guardrails
Email, push, and notification analytics for provider delivery, consent, and attributable outcomes
Customer health and account scoring for explainable account-level prioritization
Account analytics and customer 360 for a governed product, billing, CRM, support, and reliability view
Trial, onboarding, and customer-lifecycle analytics for mature activation, paid conversion, churn, and winback cohorts
Collaboration, invitations, and team adoption for multi-user value, roles, and seat utilization
Multi-product and cross-product analytics for portfolio activation, attach, migration, and expansion
Customer support account timelines for an authorized investigation surface
Support operations analytics for demand, backlog, service clocks, SLA, escalation, and CSAT
Scheduled reports and recurring delivery for secure external scheduling, rendering, and delivery
Product search and discovery analytics for result quality, reformulation, latency, and downstream outcomes
Survey, NPS, and feedback analytics for response coverage, score semantics, privacy, and follow-up
Abuse and risk-signal analytics for versioned rate-limit, challenge, and risk decisions
Incident lifecycle and post-incident analytics for detection, mitigation, customer impact, communication, recurrence, and follow-through
Pricing, packaging, and plan-change analytics for offer exposure, package choice, checkout, upgrades, downgrades, seats, and add-ons
Sales pipeline and Revenue Operations analytics for stage history, velocity, product qualification, pipeline snapshots, and closed outcomes
Content and documentation analytics for discovery, reading, search, feedback, and assisted outcomes
API product and developer-platform analytics for activation, endpoint adoption, reliability, versions, quota pressure, and customer value
AI-agent and tool-use analytics for runs, tools, approvals, evaluations, cost, and successful task completion
Feature lifecycle and deprecation analytics for discovery, repeat value, decline, migration, and safe removal
Workflow and state-machine analytics for transitions, work in progress, time in state, rework, and terminal outcomes
Permissions, roles, and entitlement analytics for grants, denials, utilization, stale access, and policy changes
Referral and product-led growth-loop analytics for sender participation, recipient value, rewards, retention, and abuse boundaries
Mobile product analytics for activation, permissions, app versions, reliability, offline delivery, and durable engagement
Analytics asset change management before changing a shared field, query, chart, or embed

Migrate or connect existing systems#

GraphJSON can receive data from Segment, Stripe, and Vercel. For another provider, build a narrow custom-source webhook adapter. To copy ongoing database changes, use change data capture, an outbox, or incremental polling. Use integrations when the source already emits the events you need; use the logging API when you need a product-specific event contract.

For Kafka, Amazon SQS, Kinesis, Google Pub/Sub, or RabbitMQ, run a message-broker consumer that checkpoints only after accepted GraphJSON delivery.

For selected service outcomes from an OpenTelemetry pipeline, use the OpenTelemetry ingestion and correlation pattern. For governed aggregates moving between GraphJSON and a warehouse or BI workflow, use Warehouse and BI handoff patterns. For low-volume provider workflows in Zapier, Make, n8n, or Pipedream, use the automation-platform adapter guide.

Use Operating managed integrations for monitoring, drift, replay, reconnection, and incident response.

To bring historical data with you, use the migration runbooks for Amplitude, Mixpanel, PostHog, and Google Analytics 4.

Additional source-specific runbooks cover Heap, Pendo, RudderStack, and Snowplow.

For CSV, NDJSON, PostgreSQL, warehouse exports, or object storage, use File, database, and object-storage migrations.

Start from a complete architecture#

Adapt one of the production references:

A production-readiness checklist#

Before treating a metric as a source of truth:

document who owns its event instrumentation
use stable event and field names
keep monetary values numeric and include a currency
attach durable user or account identifiers
choose an explicit retention policy
handle ingestion failures without breaking your application
reconcile a sample period against the source system
document the chart’s filters, time zone, and aggregation

The goal is not merely to make a chart. It is to make a number another person can understand, reproduce, and trust.

Review the product and documentation changelog for material documentation and developer-experience changes.