DATA PLATFORMS FOR DIGITAL PRODUCTS
Fast, Secure Analytics for Products That Never Stop Growing.
Deliver embedded analytics, activity insights, experimentation metrics and low-latency application features with an architecture designed for tenant isolation, predictable economics and continuous delivery. Bassirah supports high-growth teams from architecture selection through production performance engineering.
Embedded analytics · Multi-tenancy · Cost control · Operational data integration
THE CHALLENGE
Growth Exposes Every Shortcut in Your Data Path.
What worked for the first hundred customers rarely holds at the next thousand. Customer-facing analytics turn data design into product quality, security and gross margin.
The transactional database becomes the reporting engine
Dashboards and exports running against production PostgreSQL or MongoDB compete with the application itself, and every new chart adds load.
Tenant boundaries under pressure
Shared analytical stores must never return data outside the tenant’s context, and isolation needs to be tested, not assumed.
Costs that grow faster than revenue
Unbounded retention, high-cardinality data and unshaped queries push infrastructure cost per customer in the wrong direction.
Noisy neighbors and unpredictable latency
A few heavy tenants can degrade dashboards for everyone without quotas, resource isolation and per-tenant observability.
OUTCOMES
What We Help Product Teams Deliver.
Embedded Product Analytics
Offer customer-facing dashboards and operational insights with a dedicated analytical serving design. ClickHouse and Redis can play complementary roles while authentication, tenant boundaries and the API layer remain explicit.
Build for Multi-Tenant Scale
Define tenant-aware data models, query quotas, resource isolation, audit logs and workload testing before promoting customer-facing analytics into production.
Control Cost as Data Grows
Use workload characterization, partitioning and retention strategies, query shaping, caching and observability to identify and quantify viable optimizations.
Integrated Operational Data
Connect PostgreSQL and MongoDB Community workloads to suitable analytical or federated access patterns without treating the production transactional database as an unlimited reporting engine.
Activity insights and experimentation metrics follow the same serving pattern, with freshness targets agreed per use case.
Running analytics on your production database today?
Let’s design the path off it.
REFERENCE APPROACH
A Dedicated Analytical Serving Path for Every Tenant.
ClickHouse tenant-aware analytics, Redis serving, and PostgreSQL or MongoDB Community as operational stores. Authentication, authorization and tenant isolation live in an explicit API layer, never implicitly in the database.
Logical data path
- Ingest.Application events and operational data are ingested through governed pipelines from PostgreSQL, MongoDB Community or event producers.
- Analytical serving.ClickHouse serves product usage, billing analytics, campaign or customer-insight queries. Tenant keys and query security are mandatory.
- Targeted caching.Redis supports bounded, invalidatable result caching and low-latency operational state where measurements justify it.
- API / BFF layer.Handles authentication, authorization, rate limits, tenant isolation and secure parameterized query access.
- Observability.Captures p95/p99 tenant query latency, resource fairness, cardinality growth and unit cost. Automated tests detect cross-tenant access.
Component responsibilities
| Component | Role | Qualification |
|---|---|---|
| ClickHouse | Tenant-aware analytical serving | Partner-led or ClickHouse OSS route chosen per support needs |
| Redis | Bounded result cache and fast operational state | Used where measured; invalidation and freshness rules defined |
| PostgreSQL | Transactional application data | Protected from unbounded analytical load |
| MongoDB Community Server | Document-oriented operational data | Source-available (SSPL); independent Bassirah engineering |
| API / BFF layer | Authentication, authorization, quotas, tenant isolation | Owned by your product team; Bassirah designs and tests the pattern |
PROOF PACKAGE
Acceptance Tests Before Customers See a Single Chart.
We agree acceptance tests with your team and run them on representative tenants and query shapes. These are the metrics we validate, not guaranteed outcomes.
Acceptance categories
| Acceptance category | Sample test |
|---|---|
| Tenant isolation | A cross-tenant access attempt never returns data outside the tenant context |
| Concurrency | Test growing numbers of concurrent dashboards and known query shapes |
| Freshness | Measure ingest-to-display lag; distinguish live from cached data |
| Operational cost | Per-tenant storage and compute cost at representative retention |
| Resilience | Recover after an ingest or serving component failure without silent data loss |
What we measure
- Concurrent tenants
- p95 chart latency
- Unit infrastructure cost
- Isolation test results
What you receive
- Architecture decision record for the serving path
- Tenant isolation test suite and results
- Concurrency and latency test report
- Unit-cost model per tenant at target retention
ARCHITECTURE BLUEPRINT
Embedded Analytics for SaaS
Embedded analytics built for tenants, scale and speed. Deliver secure dashboards and contextual insights inside your product with tenant-aware ingestion, analytical serving and targeted caching, without making primary transactional databases carry unlimited multi-user analytical workloads.
Stack: ClickHouse · Redis · PostgreSQL · MongoDB Community
Delivery caveat.
Bassirah validates sector-specific residency, privacy, retention, security and regulatory requirements during discovery. Product selection alone does not confer compliance. Tenant isolation, latency and cost targets are validated through agreed tests on your workload.
ENGAGEMENT OFFERS
Start Where Your Growth Hurts Most.
Embedded Analytics Workshop
Map your product analytics use cases, tenant model and API layer to a target serving architecture.
Performance & Cost Review
Characterize workloads, query shapes and retention, then quantify viable optimizations with measured evidence.
Tenant Isolation Test Plan
Design and run automated cross-tenant access, concurrency and resilience tests before launch.
Digital Natives & SaaS FAQs
Why not run customer dashboards directly on PostgreSQL or MongoDB?
It can work early on, but analytical queries compete with the application as tenants and data grow. A dedicated analytical serving layer protects transactional performance and makes latency more predictable.
How do you ensure one tenant never sees another tenant's data?
Tenant keys and query security are mandatory in the serving layer, and the API layer enforces authentication, authorization and parameterized queries. We add automated cross-tenant access tests to the acceptance criteria.
When is Redis worth adding?
When measurements show repeated queries or operational lookups benefit from it. Caches are bounded and invalidatable, and the interface clearly distinguishes cached from live data.
Can you reduce our analytics infrastructure cost?
We characterize workloads and retention, then identify and quantify viable optimizations such as partitioning, query shaping and caching. Savings are stated only after measurement on your workload.
Partner-led ClickHouse or ClickHouse OSS?
Both are available. The partner route brings vendor-backed commercial capabilities; the OSS route is independent Bassirah engineering. We help you choose based on support needs, operations and cost. Compare ClickHouse support routes.
Is MongoDB Community open source?
MongoDB Community Server is source-available under the SSPL, which is not an OSI-approved license. We support it through independent engineering.
Related Pages
Build Analytics Your Customers Can Rely On.
Tell us about your product, tenants and growth plans. We will propose a serving architecture and the tests that prove it.