Amazon Kinesis + Bruin

SourceDestination

Ingest data from Amazon Kinesis or push enriched data back — with quality checks, lineage, and scheduling. Defined in YAML, version-controlled in Git.

For business teams

What you get

Files and events in your warehouse
Amazon Kinesis data lands in your warehouse with automatic schema detection. No manual parsing, no format guessing.
Schema drift protection
Quality checks catch unexpected format changes, null values, and schema drift from Amazon Kinesis before it breaks models.
Data lake orchestration
Use Amazon Kinesis as a staging layer. Bruin handles landing, transforming, and materializing — all in one pipeline.
Multi-cloud flexibility
Move data between Amazon Kinesis and other storage or warehouses. Bruin manages scheduling, retries, and lineage.

For data & engineering teams

How it works

Automatic schema detection
Bruin detects Amazon Kinesis data schemas automatically. No manual configuration when formats change.
YAML-defined, Git-versioned
Your Amazon Kinesis pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
Format validation
Quality checks catch schema drift, unexpected nulls, and format changes from Amazon Kinesis at the ingestion layer.
Land, transform, materialize
Use Amazon Kinesis as staging. Bruin handles the full flow: land raw data, transform, and materialize into your warehouse.

Before you start

AWS credentials

Kinesis stream access permissions

Step 1

Add your Amazon Kinesis connection

Connect using AWS credentials and Kinesis stream configuration. Add this to your Bruin environment file — credentials are stored securely and referenced by name in your pipeline YAML.

Parameters

access_key_idAWS access key ID
secret_access_keyAWS secret access key
region_nameAWS region for the Kinesis stream
stream_nameKinesis data stream name

connections:
  kinesis:
    type: kinesis
    uri: "kinesis://?access_key_id=<access_key_id>&secret_access_key=<secret_access_key>&region_name=<region_name>&stream_name=<stream_name>"

Step 2

Create your pipeline

Define a YAML asset that tells Bruin what to pull from Amazon Kinesis and where to land it. This file lives in your Git repo — reviewable, version-controlled, and deployable with CI/CD.

name: raw.kinesis_data
type: ingestr

parameters:
  source_connection: kinesis
  source_table: 'data'
  destination: bigquery

Step 3

Add quality checks

Add column-level and custom SQL checks to your Amazon Kinesis data. If a check fails, the pipeline stops — bad data never reaches downstream models or dashboards.

Catch events with future timestamps

Validate file paths and timestamps are present

Flag schema drift at the ingestion layer

columns:
  - name: file_path
    checks:
      - name: not_null
  - name: event_timestamp
    checks:
      - name: not_null

custom_checks:
  - name: no events from the future
    query: |
      SELECT COUNT(*) = 0
      FROM raw.kinesis_data
      WHERE event_timestamp > CURRENT_TIMESTAMP()

Step 4

Run it

One command. Bruin connects to Amazon Kinesis, pulls data incrementally, runs your quality checks, and lands clean data in your warehouse. If a check fails, the pipeline stops — bad data never reaches downstream.

Backfill historical data with --start-date

Schedule with cron or trigger from CI/CD

Full lineage from Amazon Kinesis to your dashboards

$ bruin run .

Running pipeline...

  kinesis_data
    ✓ Fetched 2,847 new records
    ✓ Quality: campaign_id not_null     PASSED
    ✓ Quality: spend not_null           PASSED
    ✓ Quality: no negative ad spend     PASSED
    ✓ Loaded into bigquery

  Completed in 12s

Other Storage & Streaming integrations

Amazon S3

Apache Kafka

CSV Files

Dropbox

Google Cloud Storage

Google Drive

Ready to connect Amazon Kinesis?

Start for free, or book a demo to see how Bruin handles ingestion, quality, lineage, and scheduling for your entire data stack.

Book a demo Read the docs