HTTP → S3 ingest

The easiest way to land data in S3

POST JSON to an HTTP endpoint. Get Parquet and compressed segments in your S3 bucket. No Kafka, no Kinesis, no glue code.

10 GiB/month free on Starter. No credit card required.

your app
POST NDJSON
EdgeMQ
your S3 bucket
segments/seg-000042.wal.zst
parquet/dt=2025-01-20/
commits/seg-000042.json

Commit markers tell your jobs what's safe to read.

≤ 10 ms
p95 ingest latency
request → durable WAL
< 1 min
median S3 commit
WAL → S3 artifact
99.95%
availability
per region

No infrastructure

HTTP in, S3 out. No brokers, no clusters, no client libraries to install.

Zero data loss

Every record WAL-committed before acknowledgment. Automatic crash recovery.

Query-ready output

Schema-aware Parquet with type casting, partitioning, and filtering. No ETL pipeline.

Data quality built in

Dead letter queues, error rate metrics, and schema preview out of the box.

Your storage, your control

Data lands in your S3 bucket, your region, with your encryption keys.

Simple integration

One HTTP call, three output formats

Send NDJSON to your regional endpoint. EdgeMQ handles WAL, compression, and S3 uploads.

curl -X POST "https://lhr.edge.mq/ingest" \
     -H "Authorization: Bearer $TOKEN" \
     -H "Content-Type: application/x-ndjson" \
     --data-binary @events.ndjson
Segments

Compressed WAL segments (.wal.zst) plus commit markers. Ideal for archival and raw replay.

Parquet (raw)

Date-partitioned Parquet with full payload preserved. Query with any engine.

Parquet (views)

Schema-aware typed columns from view definitions. Ready for warehouses and feature stores.

Query withSnowflakeDatabricksClickHouseDuckDBPostgres

Built for teams that live on S3

From ML pipelines to lakehouse ingest

Simple pricing

Starter is free up to 10 GiB/month. Then $0.10/GiB. Pro starts at $99/month with schema-aware views.

View pricing →

Get data into S3 in under 10 minutes

Create an endpoint, connect your S3 bucket, and start POSTing. Your first Parquet files will land before you finish reading the docs.