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Event-driven Microservices with Data Lakehouse

How to Architect a SaaS Analytics Dashboard

This blueprint outlines a robust architecture for a SaaS analytics dashboard, emphasizing an event-driven approach to handle diverse, high-volume data streams. It leverages specialized databases for OLAP queries and scalable microservices to provide real-time, interactive insights across multiple tenants. The design prioritizes data isolation, performance, and cost-efficiency for analytical workloads.

Recommended architecture pattern

Recommended tech stack

Frontend

React with Next.js: Provides server-side rendering (SSR) for initial load performance and a rich, interactive user experience essential for complex dashboards.

Backend

Python (FastAPI) & Go: FastAPI for data ingestion and API gateways due to its async capabilities, Go for high-performance data processing and aggregation services.

Database

ClickHouse (OLAP) & PostgreSQL (OLTP) & AWS S3 (Data Lake): ClickHouse for lightning-fast analytical queries on massive datasets, PostgreSQL for user/tenant metadata, and S3 as the data lake for raw and processed data storage.

Real-time / Messaging

Apache Kafka: Acts as a high-throughput, fault-tolerant message broker for ingesting raw event data from various sources and distributing it to processing services.

Infrastructure

Kubernetes (AWS EKS): Provides container orchestration for microservices, enabling auto-scaling, high availability, and efficient resource utilization across environments.

Authentication

Auth0 / AWS Cognito: Offers robust, multi-tenant authentication and authorization, supporting SSO, social logins, and fine-grained access control for enterprise clients.

Key third-party services

Stripe (Payments), Grafana (Embedded Visualizations), SendGrid (Notifications): Stripe for subscription management and billing, Grafana for advanced embedded charting, SendGrid for critical alerts and system notifications.

Core components

Key data model

Entity	Key fields	Notes
Tenant	id, name, subscription_plan_id, created_at	Primary entity for multi-tenancy, linked to all other tenant-specific data.
User	id, tenant_id, email, role, last_login	Foreign key to Tenant, indexed on tenant_id and email.
DataSourceConfig	id, tenant_id, type, connection_params_encrypted, status	Stores encrypted credentials and configuration for external data sources per tenant.
RawEvent	event_id, tenant_id, timestamp, event_type, payload_json	High-volume immutable events, typically stored in S3/ClickHouse, partitioned by tenant_id and date.
AggregatedMetric	tenant_id, metric_name, time_bucket, dimensions_json, value, count	Pre-calculated metrics for faster dashboard loading, stored in ClickHouse, indexed by tenant_id, metric_name, time_bucket.
Dashboard	id, tenant_id, name, layout_json, is_public	Stores metadata and layout of user-created dashboards.
Widget	id, dashboard_id, type, query_config_json, position_json	Components within a dashboard, with specific query parameters.

Core API endpoints

Method	Endpoint	Purpose
POST	/api/v1/events/ingest	Ingest raw event data from client applications or external sources.
GET	/api/v1/dashboards/{dashboardId}	Retrieve a specific dashboard's configuration and data for rendering.
POST	/api/v1/dashboards	Create a new dashboard for the authenticated tenant.
GET	/api/v1/metrics/query	Execute an analytical query to fetch aggregated metric data based on specified filters.
POST	/api/v1/data-sources	Configure a new external data source connection for a tenant.
GET	/api/v1/tenants/me/subscription	Retrieve the current tenant's subscription status and plan details.
PUT	/api/v1/users/{userId}/roles	Update a user's role within a tenant for access control.
GET	/api/v1/alerts	Fetch active alerts and notifications for the current tenant.

Scaling considerations

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Security & compliance

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Estimated monthly cost

Want a tailored build estimate? Try the free software cost estimator or the tech stack finder.

Suggested build plan

Phase	Timeframe	Deliverables
Phase 1: Core Data Ingestion & Storage	Weeks 1-6	Kafka setup, basic data ingestion API, S3 data lake, initial ClickHouse schema, raw event storage, basic ETL pipeline.
Phase 2: User Management & Basic Dashboarding	Weeks 7-12	Auth0 integration, user/tenant management APIs, frontend user login/signup, dashboard creation/editing, simple chart widgets, data source connectors.
Phase 3: Advanced Analytics & Multi-tenancy Features	Weeks 13-20	Complex aggregation pipelines, real-time data processing, advanced filtering/segmentation, multi-tenant data isolation, subscription management (Stripe), alerting system.
Phase 4: Performance, Scaling & Enterprise Readiness	Weeks 21-28	Performance tuning (queries, ingestion), auto-scaling implementation, comprehensive monitoring/logging, security audits, SSO integration, API documentation.

Frequently asked questions

How do we ensure data freshness for real-time dashboards?

We'll use Kafka for immediate event ingestion and stream processing frameworks (e.g., Flink) to process and aggregate data with low latency, pushing updates to dashboards via WebSockets or frequent polling against ClickHouse views.

What's the strategy for handling diverse data sources from different tenants?

A flexible Data Connector Service will abstract different external data source APIs. Raw data is normalized into a common schema before being ingested into Kafka, allowing for consistent processing regardless of origin.

How can we manage the cost of storing petabytes of analytical data?

By leveraging AWS S3 as a cost-effective data lake for raw data, implementing data lifecycle policies to move older data to colder storage tiers (e.g., Glacier), and carefully designing ClickHouse schemas for efficient storage and query performance, including data archiving strategies.

What if a single tenant generates an extremely high volume of data or queries?

Our multi-tenant architecture includes mechanisms for resource isolation. For 'whale' tenants, we can provision dedicated ClickHouse clusters or increase their resource limits within the shared infrastructure, ensuring their activity doesn't impact other tenants.

How do we handle custom reporting requirements that aren't covered by standard dashboards?

The Analytics Query Service will expose a flexible query API, allowing for custom report generation. Additionally, we can integrate with embedded BI tools like Grafana, enabling tenants to build highly customized visualizations and reports using their data.

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