Pulse gives fintech and financial services teams a complete real-time data infrastructure stack, built to the same spec every time, running in production within days.

Managed as long as you need it.

Event Sources

Live streams in

Stripe transactions

Risk & fraud signals

Audit & compliance logs

Application events

Social media events

Pulse · Real-time data infrastructure

Event in. Insight out. In milliseconds.

Kafka Streaming

transactions

12.4k/s

risk-signals

4.8k/s

audit-logs

2.1k/s

Ingest at the speed of your business

ClickHouse Query

SELECT sum(amount)
FROM txn_stream
WHERE ts > now() - 1m

p95 latency 38 ms

Sub-50ms on billions of rows

dbt models Running

stg_transactions3.2s

fct_risk_signals1.8s

mart_live_positions0.9s

mart_compliance…

Modeled, tested, production-ready

Kubernetes34/36pods

Throughput184k/min

SLA100%

Live Dashboards

Decisions, instantly

Operational KPIs

Revenue & volume

Customer behavior

Refreshed in seconds

Risk & Alerts

Before it costs you

Fraud & anomaly signals

Compliance breach alerts

Threshold notifications

Audit-ready reports

~0week

From scoping call to production deployment

0ms

p95 query latency after ClickHouse migration

Clients who trust Datum Labs with their data infrastructure

Of clients continue on managed service after build

What is real-time data infrastructure?

Real-time data infrastructure is the layer that moves event data from your systems into fast, query-ready storage as it happens.It brings together event streaming, analytical storage, transformation models, orchestration, monitoring, and ongoing operations so teams can trust live data in production.
Pulse deploys this layer using Kafka, ClickHouse, dbt, Kubernetes, and monitoring, then stays on to manage it.

Book a demo now

[ THE PROBLEMS ]

Your infrastructure is not keeping up. Your business already knows it.

These are the problems that appear once event volume outgrows batch pipelines. Most teams reach out after living with at least one of these for months.

Postgres Is Carrying Work It Was Never Meant to Handle

Analytical queries take 30 to 60 seconds. You have added indexes. Performance does not improve. The database was never designed for this workload and the problem compounds as volume grows.

Kafka Is Running, but Nobody Truly Owns It

Kafka is running. Topics have accumulated. Consumer lag spikes unpredictably. When something breaks it bounces between teams with no resolution path and no SLA.

Operations Are Making Decisions on Yesterday’s Data

Compliance teams report on data hours old. Risk teams monitor signals from this morning. Decisions are made on a picture of the business that has already moved.

The Real-Time Stack Has Been Stuck in “Almost Ready”

A consulting firm ran discovery. A contractor built a proof of concept. Neither produced a production system. The business has been waiting over a year for infrastructure that works.

[ What we build ]

The full scope of what Pulse delivers

You can start with one. Most clients end up running the full stack.

Real-Time Event Pipeline Deployment

We deploy production Kafka pipelines for high-volume events, with topic design, consumer groups, schema registry, stream processing, Kubernetes deployment, monitoring, and delivery into ClickHouse before event delays become operational risk.

ClickHouse Analytics Infrastructure

We move analytical workloads into ClickHouse with optimized schemas, dbt models, materialized views, and query tuning, so slow queries stop holding back reporting, risk monitoring, and operational dashboards.

Managed Kafka and ClickHouse Operations

We manage the full real-time stack after deployment, including monitoring, alerting, schema changes, performance tuning, capacity planning, and incident response, so ownership does not keep bouncing between teams.

Postgres to ClickHouse Migration

We migrate high-volume analytical workloads from Postgres to ClickHouse, helping teams protect application performance while giving reporting and operations the speed they need.

Real-Time Risk and Compliance Infrastructure

We build real-time infrastructure for transaction streams, audit logs, risk signals, and compliance reporting, so financial and insurance teams are not making critical decisions on stale data.

Build Your Data Front Today

[ Who this is for ]

Built for teams that cannot wait on the real-time data layer

Pulse is for technical teams where event volume, query speed, and infrastructure ownership are already business critical.

CTOs and Heads of Engineering

You need Kafka, ClickHouse, dbt, and Kubernetes in production without pulling your core engineers away from the product roadmap. Pulse gives you a specialist team to deploy the real time stack, own the infrastructure, and keep delivery moving.

Heads of Data at Scale

Your pipelines are growing, dashboards are slowing down, and the application database is carrying workloads it should not handle. Pulse gives your team a dedicated real time data layer for ingestion, modeling, fast analytics, and operational reporting.

VPs of Engineering in Fintech, Insurance, and Financial Services

Your business runs on transaction streams, risk signals, audit logs, and compliance data that cannot be stale. Pulse helps you process, store, query, and monitor that data at speed without waiting through a six month internal build cycle.

If this sounds familiar, the real time data layer is no longer a future project. It needs an owner now.

Book a 30-minute scoping call

Book the Call Before the Next Incident

30 minutes. You talk about your stack, your volumes, and your deadline. We tell you whether Pulse fits and what it would look like.

Know Exactly What You Are Getting

A proposal lands with a fixed scope, fixed cost, and fixed timeline. No hourly billing. No scope creep. You sign when you are ready.

Live in Production in One Week

Kafka, ClickHouse, dbt, Kubernetes, and Grafana deployed and documented. A production system your team can build on immediately.

We Own It From Here

Full stack walkthrough, complete documentation, then we take over operations. Your team builds the product. We keep the infrastructure running.

[ HOW PULSE WORKS ]

From Scoping Call to Production Stack in Four Steps

No discovery sprints. No open-ended retainers. Pulse is built on 25,000 plus hours of real data infrastructure delivery. The playbook exists. We execute it.

[ WHY PULSE ]

Why engineering teams choose Pulse over building internally?

There are other ways to get Kafka and ClickHouse into production. Here is what they actually look like.

Category	Building Internally	Pulse by Datum Labs
Time to production	4 to 9 months depending on backlog and hiring timeline	Approximately one week for a full production-ready deployment
Engineering cost	$140,000 to $180,000 per year for a senior data infrastructure hire	Fixed-scope engagement that does not touch your product backlog
Stack ownership	Single hire covers one layer with no clear accountability across Kafka, ClickHouse, dbt, and ops	One team accountable for Kafka, ClickHouse, dbt, Kubernetes, and Grafana end to end
Time to hire	Three to six months to find the right candidate, if they are available at all	No hiring cycle. Pulse is available now and deploys in approximately one week
After go-live	Incidents bounce between teams with no clear SLA or resolution path	Managed operations with 24/7 monitoring, alerting, and a dedicated Slack channel
Infrastructure expertise	Application engineers inherit a stack they did not build and do not fully own	We understand the full stack before we deploy a single component

Plan Your First Data App

[ Why Data Front ]

The exact tools your engineering team already evaluates

Pulse deploys and manages a proven combination of open-source and production-grade tools. No proprietary lock-in. No unfamiliar technology. Just the right stack, deployed correctly and owned long-term.

Ingestion and Streaming

Apache Kafka

Schema Registry

Kafka Connect

Debezium CDC

Application layer

ClickHouse

dbt

Materialized Views

HTTP API

Auth and access

Kubernetes RBAC

TLS

IAM Integration

Deployment

Kubernetes

Helm

Terraform

CI/CD

Monitoring

Grafana

Prometheus

Alertmanager

PagerDuty

Infrastructure

AWS

GCP

Azure

Hetzner

[ Case Studies ]

This is what we have already shipped

Voice AI SaaS · Analytics Foundation

Voice AI Platform

Five systems producing five versions of the same metrics. Revenue, activation, and retention defined differently by every team. Unified into one BigQuery warehouse with a single dbt transformation layer.

From five conflicting definitions to one source of truth across product, growth, sales, finance, and support.

Read case study →

Lead Generation · ClickHouse Migration

Westwise

Five disconnected data sources with no shared analytics layer. Migrated from BigQuery to ClickHouse with dbt attribution models refreshing every 15 minutes. Cost per qualified lead went from manual spreadsheet reconciliation to a live governed metric.

From fragmented reporting to a unified ClickHouse analytics layer updating in near real time.

Read case study →

Ad Analytics · Kubernetes Migration

Amanahfy

Cohort analysis and retention queries hitting a ceiling. Replaced with a ClickHouse Cloud warehouse and incremental dbt models serving hundreds of millions of rows in under a second.

From engineering escalations to sub-second queries across every team.

Read case study →

Ad Analytics · Kubernetes Migration

Venon

Docker Compose with no redundancy, manual deployments, and pipeline failures found by users. Migrated to Kubernetes with Kafka, ClickHouse, and 25 real-time streams with zero data loss.

From no recovery path to full observability and version-controlled deployments.

Read case study →

[ Ready to Build? ]

Your event data is arriving. Your infrastructure needs to match it.

Launch the Kafka pipeline, ClickHouse analytics layer, or full managed stack your engineering team keeps deferring.

Scope it this week

Frequently asked questions

When should a company move analytics off Postgres?

When analytical queries are taking more than a few seconds, when adding indexes no longer improves performance, or when analytical workloads are visibly competing with application performance. At that point Postgres is no longer the right engine for the analytical layer. ClickHouse is designed specifically for the query patterns that break Postgres at scale.

How long does it take to deploy a production Kafka and ClickHouse stack?

A production-ready deployment takes approximately one week. That timeline is possible because Pulse is built on a proven deployment playbook developed across 25,000 plus hours of real data infrastructure delivery. We are not designing the architecture during your engagement. We are executing a process that has already been taken to production.

What is the difference between Pulse and a managed Kafka or ClickHouse cloud service?

Managed cloud services give you a hosted cluster. They do not give you the pipeline design, the dbt models, the Kubernetes deployment, or a team accountable for the full stack. Pulse delivers and manages the entire real-time data layer as one fixed-scope engagement.

Why not hire a senior data infrastructure engineer internally?

A senior data infrastructure engineer takes three to six months to find and costs $140,000 to $180,000 per year to retain. That hiring timeline is a business risk when the deadline is real. Pulse deploys in approximately one week and stays on as the managed team at a fraction of that cost, with a team that already knows the stack because they built it.

What happens after go-live?

Pulse owns the stack under a monthly managed service agreement scoped and budgeted upfront. Monitoring, alerting, schema migrations, performance tuning, capacity planning. Your team builds on top of the infrastructure. Pulse maintains what is underneath.