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Destination
Beta
Beta

Views, committed to Iceberg.

Write any view to an Apache Iceberg table in your own bucket. Each commit is an atomic snapshot that Spark, Trino and Frostbase can read.

Setup
5 min
Delivery
Exactly-once
Throughput
600 MB/s
Since
v2.1
Overview

How it works.

Basalt writes Parquet data files to your bucket and commits them through a REST, Glue or Nessie catalog. Each commit records the source offsets in snapshot properties, so a retried commit is detected and skipped.

Three Kafka topics stream into the Basalt ordered log, which feeds two materialized views served through the API.KAFKA TOPICSBASALTVIEWSSERVEpayments.v3orders.v1refunds.v2ORDERED LOGpayments_liverevenue_by_minAPI3.1 MS
Setup

Set it up in three steps.

01

Add the destination

Point Basalt at your catalog and give it write access to the warehouse bucket. Basalt creates the table with the view's schema and partition spec.

terminal
# commit the orders view to a Glue-catalog table
basalt sink add orders --iceberg glue://lake.orders --view orders_enriched
✓ table lake.orders created · format v2
✓ snapshot 1 committed · 128 files · 1.9 GB
02

Shape it with a view

Write plain SQL over the source. Basalt keeps the view current with every record and tracks lineage back to the topic, partition and offset.

views/revenue_by_min.sql
CREATE MATERIALIZED VIEW revenue_by_min AS
SELECT date_trunc('minute', ts) AS minute,
       currency,
       sum(amount) AS revenue,
       count(*)    AS payments
FROM payments
WHERE status = 'captured'
GROUP BY 1, 2;
03

Read it from anywhere

Every view is an endpoint the moment it exists. Call it over REST, connect with any Postgres client, or use the SDK from your app.

terminal
curl https://api.basalt.dev/v1/views/revenue_by_min?limit=5 \
  -H "Authorization: Bearer $BASALT_TOKEN"
✓ 200 OK · 5 rows · served from eu-north-1 in 3.1 ms
Included

What you get.

01

Atomic commits

Every batch is one snapshot. Readers never see a partial write.

02

Any catalog

Works with the REST catalog, AWS Glue, Nessie and Polaris.

03

File compaction

Small files are rewritten on a schedule so query engines scan fewer objects.

04

Time travel

Query any past snapshot by ID or timestamp from your engine of choice.

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