Product
For Platform / Infra
Ingest without another stateful system to babysit
EdgeMQ gives your organization a dedicated HTTP → S3 ingest layer—per-tenant isolation, durable WAL, and explicit backpressure—instead of bespoke collectors or yet another Kafka/Kinesis cluster to run.
Data and ML teams experience EdgeMQ as their lakehouse ingest layer; you experience it as a standardized, safe ingest primitive you can roll out across the platform.
Standardize how data enters S3.
Keep control of security, reliability, and cost.
"Just one more ingest service"
As a platform / infra / SRE team, you've seen this movie:
Every new product or data initiative needs to "just ingest some events":
- A new webhook endpoint here.
- A tiny HTTP service that uploads to S3 there.
- A sidecar that streams logs to a bucket "temporarily" (forever).
Suddenly, you're responsible for:
- Multiple stateful services written by teams that don't specialize in infra.
- Debugging partial S3 uploads, retries, and timeouts during incidents.
- Keeping Kafka/Kinesis clusters alive for workloads that only ever get written into S3 anyway.
Security reviews turn into:
- "Where are these access keys coming from?"
- "Why does this service have full s3:* on the bucket?"
You want a paved path for ingest that's safe to bless as the way to get data into S3—without deploying and operating a whole new distributed system.
EdgeMQ: a platform-grade ingest primitive
EdgeMQ is a managed ingest plane that your org can standardize on:
HTTPS / ingest endpoint, reachable globally.
Per-tenant write-ahead log (WAL) on NVMe at the edge.
Bounded queues with honest backpressure (503 + Retry-After).
Compressed segments shipped into your S3 bucket and prefix.
Commit markers that mean "this segment is safely stored and ready to consume".
You don't have to:
- ▸Run Kafka/Kinesis just for ingest.
- ▸Maintain custom collectors for each team.
- ▸Embed long-lived S3 keys in random services.
EdgeMQ handles the ingest complexity; you keep control over the environment, IAM, and S3.
Safe to bless as a platform primitive
Per-tenant isolation by design
EdgeMQ is not a giant multi-tenant blob where everyone shares the same queues and disks.
- ▸Each account has its own microVM per region.
- ▸That microVM has: its own NVMe-backed WAL volume, its own network stack and public IPs, no shared WAL or queues between tenants.
For you, that means:
- ▸Clear blast-radius boundaries—noisy teams can't stomp on others at disk or queue level.
- ▸Easier reasoning about capacity and risk per environment or per product line.
Predictable overload behavior
One of the hardest parts of ingest is not "happy path" throughput—it's failure and overload. EdgeMQ is explicit about what happens when things get hot:
- ▸Bounded queues, not infinite buffers that slowly kill the box.
- ▸When queues fill: Ingest returns HTTP 503 responses with a Retry-After header.
- ▸This is intentional backpressure: it protects the WAL disk from overload, keeps latency from exploding for all tenants, and forces producers to slow down or temporarily shed load.
As a platform team, that gives you:
- ▸A well-defined failure mode under pressure.
- ▸Easier incident response: you know what clients will see and how they should behave.
- ▸No surprise data loss because some "helper script" wrote half a file to S3 and called it a day.
IAM and S3 that fit your security model
EdgeMQ writes to your S3 bucket, under your rules:
- ▸Uses an IAM Role + STS AssumeRole: no long-lived access keys to rotate, no secrets stuffed into app configs.
- ▸Least privilege by default: the role's permissions scoped to a specific bucket and prefix, plus a small validation path, nothing else.
- ▸Per-environment separation: use different roles/prefixes for dev, stage, prod, or by business unit.
Benefits for security and compliance:
- ▸Clear, auditable trust policies and permission boundaries.
- ▸Easy answer to "who can write to this bucket?" and "from where?"
- ▸A single, standardized pattern for "this is how we ingest data into S3."
Operated and observable like real infra
Platform teams need more than a black box. EdgeMQ is designed to expose the signals you care about:
Ingest metrics
- ▸Requests per second
- ▸Success/4xx/5xx rates
- ▸Payload volumes and burst behavior
Latency and queueing
- ▸p95/p99 accept latencies
- ▸Queue depth / saturation signals
S3 shipment status
- ▸Segment shipment times
- ▸Failed uploads and retries
- ▸Commit marker progression
With the right plan, you also get: always-on capacity for production (no scale-to-zero wakeups), reserved throughput for critical workloads, and SLAs that match your reliability expectations.
You can treat EdgeMQ as a first-class piece of platform infra, not a toy service.
Operational patterns EdgeMQ enables
Replace bespoke ingest services with one paved road
Before:
- ▸Team A runs a Node/Go "ingest service" they barely maintain.
- ▸Team B has a Lambda chain uploading JSON to S3.
- ▸Team C wants to spin up a small Kafka cluster "just for this project."
After:
- ▸Platform team provides: a standard EdgeMQ endpoint per region, a standard S3 prefix + IAM role pattern per environment.
- ▸Product / data / ML teams: send NDJSON to /ingest, see their data show up in S3 under the agreed prefix.
- ▸Platform team: monitors one ingest system instead of N bespoke ones, can enforce org-wide patterns for naming, partitions, retention, encryption.
You get a paved road for ingest instead of a sprawl of one-off solutions. That paved road is the same lakehouse ingest layer described on the Data Engineers and ML Teams pages—it's one piece of infrastructure, shared by everyone.
Decouple producers from warehouses and databases
A lot of systems currently write straight into warehouses or DBs:
- ▸Apps/cron jobs push directly into Snowflake, Postgres, ClickHouse, etc.
- ▸Under load or during incidents, they overload these downstream systems.
- ▸Tight coupling makes migrations painful.
With EdgeMQ as the ingest layer:
- ▸Producers write into EdgeMQ → S3.
- ▸Downstream consumers (Snowflake, Databricks, ClickHouse, Postgres, DuckDB) pull from S3 at their own pace.
- ▸You can: add new consumers without touching producers, migrate warehouses without redoing ingest, apply consistent governance and retention policies in S3.
It's classic decoupling by storage, but implemented as a managed, observable service.
Control cost and capacity centrally
Ingest bursts and traffic patterns tend to be unpredictable if every team rolls their own system. With EdgeMQ:
- ▸You can see aggregate ingest volumes and rates.
- ▸You can assign: environments to specific regions or instances, reserved capacity to critical paths.
- ▸You have a clear place to implement: rate limits (organizational or per-tenant), alerting when traffic deviates from normal, budget monitoring for uncompressed ingress volume.
Instead of chasing down dozens of pipelines, you tune one ingest layer.
Integrating EdgeMQ into your platform
As an internal "ingest product"
Treat EdgeMQ like an internal service you offer to application and data teams:
Platform team:
- ▸Sets up: S3 buckets & prefixes, IAM roles and trust policies, EdgeMQ endpoints and regions.
- ▸Documents: how to get a token, how to structure events, how to find the data in S3.
Teams across the org:
- ▸Request an "ingest space" (prefix + token).
- ▸Send events to /ingest using a simple client library.
You turn chaotic ingest demands into a clean self-service product.
Infrastructure-as-code friendly
Even if you don't yet have an official Terraform provider, you can still:
- ▸Encode: S3 bucket and prefix layouts, IAM roles and policies for EdgeMQ.
- ▸Store: EdgeMQ endpoint and region configs, per-environment tokens/secrets in your secret manager.
Then, you can provide higher-level modules:
module "edge_ingest_analytics_prod" module "edge_ingest_iot_prod"
Each module exposes: the /ingest URL, the S3 prefix where data will land, the expected schema / contract.
This fits cleanly into how you already manage platform resources.
Collaboration with data & ML teams
EdgeMQ sits at the intersection of Platform, Data, and ML:
Platform / infra:
- ▸Own security, IAM, S3, regions, and capacity.
- ▸Provide standardized ingest as a platform capability.
Data engineers:
- ▸Treat EdgeMQ prefixes as the Bronze layer.
- ▸Use Snowflake, Databricks, ClickHouse, DuckDB, Postgres to build models and tables on top.
ML teams:
- ▸Assume continuous data into S3 for training, evaluation, and features.
- ▸Don't need to build or operate ingest systems.
Everyone shares the same ingest contract and infrastructure.
Governance, compliance, and auditability
Because EdgeMQ writes into your S3 and uses your IAM roles, it fits neatly into existing governance:
Encryption and retention
Inherit your default bucket encryption (KMS) and lifecycle rules.
Access control
Use the same policies and lakehouse table-permissions you already manage.
Audit trails
CloudTrail/IAM logs for role assumptions. S3 access logs for who read/wrote what prefix.
Instead of fighting a hodgepodge of ad-hoc pipelines, governance can focus on one ingest path into S3 and well-defined consumers on top.
Make ingest a paved road, not a ticket queue
Right now, your team probably gets tickets that all sound roughly like:
- ▸"We just need a small service to accept events and put them in S3."
- ▸"Can we have somewhere to send partner webhooks for this new integration?"
- ▸"We have a new ML project; can you hook up this feed into Snowflake?"
EdgeMQ lets you answer those tickets with:
"Use the standard ingest endpoint. Here's your S3 prefix, token, and schema contract."
No new stateful services. No new clusters. One ingest layer.
Related pages
- For Data Engineers — how EdgeMQ powers their S3 Bronze layer and pipelines.
- For ML Teams — how they use the same ingest layer as an AI data hose into S3.
Ready to standardize ingest on your platform?
EdgeMQ gives platform and infra teams a way to: stop running one-off collectors and ingest services, provide a secure, reliable, and observable ingest plane, empower data and ML teams to move quickly on top of a solid foundation.
Make 'getting data into S3' the easy part of every project.