Data-Heavy B2B.
When real-time data, streaming pipelines, or complex data UIs are the core value of your product, not a bolt-on. We build for the case where slow data or wrong data ends the customer relationship.
P99 LATENCY
142ms
SLA
99.95%
02The investment
Data products live or die on trust.
Why we go deep
Data systems are accountability systems.
Data systems are accountability systems. Every event must be traceable to its source. Every transformation must be reproducible. Every dashboard must be defensible. We design pipelines as if they'll be audited because eventually, they always are. A single broken lineage trail, a stale dashboard, or an unexplained number on a customer screen will end a contract, so we build with lineage, retention, and observability as first-class concerns rather than afterthoughts.
Domain context
Streaming · lakehouse · catalog.
We track the practitioner literature on data contracts, schema evolution, and lineage. We choose stacks based on cost and reliability, not vendor hype. Our defaults: streaming plus batch with lineage and evals from day one. We follow the engineers writing the observability and lakehouse playbooks, not the analyst-relations decks.
03What Data-Heavy B2B teams hire us for
Three capabilities, lineage by default.
Capability 01
Streaming pipelines
Low-latency event pipelines with backpressure, exactly-once semantics, and replay. Built to survive Black Friday and the post-mortem.
Capability 02
Lineage & data catalog
Every column traceable to its source. Schema changes that don't silently break downstream consumers. Documentation engineering treats as first-class.
Capability 03
Cost-optimised warehousing
Right-sized storage, query optimisation, and aggressive caching. Your warehouse bill shouldn't scale linearly with your customer count.
142ms
p99 stream latency
End-to-end, source to dashboard. Measured continuously.
−34%
warehouse cost
Typical reduction in the first 90 days of an engagement.
99.95%
pipeline SLA
Backed by replay capability, not promises.
04How we approach a Data-Heavy B2B engagement
Map. Instrument. Optimise.
Week 0
Map the lineage
Where does each customer-visible number come from? What is the failure mode? We trace the graph before we touch a pipeline.
Week 1
Scope a measurable outcome
Fixed-price proposal tied to a latency, cost, or SLA target. No data-modernisation theatre without a number attached.
Week 2 onward
Instrument and optimise
Senior-led pod ships pipelines with lineage, observability, and cost dashboards from day one.
05Services we bring here
The full delivery stack.
Customers depend on your numbers — are your pipelines defensible?
Book a 20-minute Architecture Review. We'll look at your data lineage, latency posture, and cost profile, and give you a written diagnosis. No deck. Just a roadmap.
FAQFrequently asked
Data-Heavy B2B, most common questions.
- What counts as a "data-heavy B2B" product?
- Products where the data itself is the value proposition: analytics platforms, observability tools, BI on data warehouses, customer 360 dashboards, lineage tooling. The surface area is usually thin (a few high-density screens) and the engineering effort is in the pipeline, the query layer, and the explainability of the output.
- Can analysts verify the AI features Hotreloads ships against the underlying data?
- Yes. Every model output that surfaces a number must link back to the rows that produced it, with the SQL the model wrote visible to the user. If the user cannot click through to the underlying data, the feature does not ship. This is non-negotiable for data products.
- How does Hotreloads handle dashboard performance?
- Three patterns: materialised views on dbt for the hottest queries, query-result caching at the application layer for repeated reads, and progressive rendering for the dashboards that can tolerate showing partial data first. p95 dashboard latency under 800ms is the bar.
- Does Hotreloads support natural-language-to-SQL features safely?
- Only with an explainability layer that shows the SQL the model wrote, run against a read-only role with row-level security applied, and joined to a verifier that flags joins on unindexed columns. A natural-language-to-SQL feature that ships without these is a customer-trust incident waiting to happen.
- How long does a typical data-B2B engagement run from kickoff to first production pipeline?
- A greenfield streaming pipeline with lineage and observability reaches production in 4 to 6 weeks for a well-scoped integration. Week 0 is lineage mapping and contract definition. Week 1 is a fixed-price proposal tied to a concrete latency or cost target. Weeks 2 through 5 are build, instrumentation, and load testing. Week 6 is a monitored cutover with a replay fallback in place. Engagements that exceed this timeline are almost always waiting on upstream data-access provisioning, not build time.
- How does Hotreloads reason about per-customer warehouse costs?
- We model cost at the tenant level from day one, not as a total bill divided by seat count. Each customer's query volume, data volume, and retention window gets its own cost projection, tracked against actuals in a Metabase dashboard. The standard levers: partition pruning and clustering on BigQuery or Snowflake, dbt-materialised aggregates for the hottest query patterns, and a 90-day tiered retention policy that moves cold data to object storage. Our typical engagement produces a 30 to 40 percent reduction in warehouse spend within the first 90 days.
- Does Hotreloads handle the customer-facing data delivery layer (S3 drops, APIs, dashboards)?
- Yes. We build the full delivery stack: scheduled S3 exports with Parquet and Delta Lake formats, a REST or GraphQL read API fronted by a Redis cache for sub-50ms repeated reads, and high-density React dashboards using Recharts or Nivo. The delivery format is a contract decision made at design time, not bolted on at launch. Each format ships with a data contract (Protobuf or JSON Schema) and a changelog so downstream consumers are not broken by upstream changes.
- How does Hotreloads handle schema versioning for data-B2B contracts?
- Schema contracts live in a Git-versioned registry, one file per topic or table, with a compatibility mode set to BACKWARD by default. We use Confluent Schema Registry for Kafka-backed pipelines and dbt contracts plus dbt expectations for warehouse models. A breaking change (column drop, type narrowing) requires a new major version and a parallel-run period of at least 14 days before the old version is retired. Our CI pipeline runs schema-diff checks on every pull request and blocks merges that introduce undeclared breaking changes.