🚀 New MinIO adaptor released, plus a DHIS2 to MinIO data lake template

Adaptors & Templates
1

Dear Community,

We’re excited to announce two things at once: a new MinIO adaptor, and a ready-to-use template, DHIS2 to MinIO, that puts it to work landing DHIS2 analytics data straight into a data lake.

The OpenFn MinIO adaptor

The new @openfn/language-minio adaptor gives your workflows direct read/write access to a MinIO (or any S3-compatible) bucket. It exposes:

  • createBucket(bucketName, [region], [options]): create a new bucket, optionally with object locking enabled

  • putObject(bucketName, objectName, data, [options]): upload an object (JSON, NDJSON, CSV, or raw), with support for custom content types and metadata

  • getObject(bucketName, objectName, [options]): retrieve and optionally parse an object as JSON, NDJSON, or CSV

  • listObjects(bucketName, [options]): list objects in a bucket, with prefix filtering and recursive listing

  • getObjectTags(bucketName, objectName)/setObjectTags(bucketName, objectName, tags, [putOpts]): read and write object tags, useful for marking data as raw vs. processed, tracking source system, etc.

Together these cover the core pattern of a landing zone: write raw extracts in, tag them, list what’s there, and read them back out for downstream processing.

The template: DHIS2 to MinIO

To show the adaptor in action, we’ve published a template that pulls DHIS2 analytics data and writes it into a MinIO bucket as structured JSON snapshots — a real extract-and-load pipeline from a live source system into a data lake.

The example workflow (built against an EPI/immunization use case) does the following:

  1. Fetch EPI data elements from DHIS2 — starts with a clearly marked configuration block where you set your own data element group ID(s), org unit ID, and reporting period range (start/end), then looks up data element groups, collects all data element IDs, and chunks them into small batches so each analytics request stays within DHIS2’s limits.

  2. Pull analytics data from DHIS2 — sequentially works through each org unit level (facility, district, region, etc.) and each reporting period, pulling analytics values chunk by chunk. Processing levels one at a time (rather than firing them all at once) keeps requests manageable and avoids overwhelming the DHIS2 instance.

  3. Upload analytics snapshot to MinIO — writes one JSON object per org unit level and period to the bucket, under a path like raw/analytics/epi/level_<n>/<period>.json, each tagged with its source, extraction time, and the query that produced it. The result is a raw, well-organized landing zone in MinIO that a downstream ELT tool can pick up and load into a warehouse’s staging tables.

Key features

  • Chunks large data element lists automatically to stay within DHIS2 request limits

  • Processes org unit levels sequentially, so it scales to instances with a lot of analytics data without overwhelming the source system

  • Writes each period/level combination as its own JSON object, with source metadata baked in (source system, extraction timestamp, query parameters)

  • Runs on a schedule (cron trigger, disabled by default so you can configure it first)

Under the hood

  • @openfn/language-dhis2@8.0.13 — fetches data element metadata and analytics values from the DHIS2 API

  • @openfn/language-minio@1.1.2 — writes the resulting JSON snapshots to a MinIO bucket

Getting started

  1. Log in at app.openfn.org (or register for a free cloud project)
  2. Click Create Workflow in your project workspace
  3. Search templates for “DHIS2 to MinIO” (tags: dhis2, minio, datalake, analytics, warehouse)
  4. Add your DHIS2 credential to the two fetch jobs and your MinIO credential to the upload job
  5. Set your data element group ID(s), org unit, and period range in the “CONFIGURE ME” block, run manually to check the logs, then enable the trigger

One heads-up: the period generator assumes a 12-month calendar, so if your instance uses a different calendar (e.g. the Ethiopian 13-month calendar), adjust generatePeriods() in the first job.

For the full story, including where OpenFn fits in a data warehouse architecture and why we recommend pairing it with dedicated ELT tools like dbt, read the blog post: OpenFn: New MinIO Adaptor: Land DHIS2 Data in Your Data Lake | OpenFn

Questions about configuring it for your instance? Ask away below. :backhand_index_pointing_down:

Author: @AishaH

Happy automating!
The OpenFn Team