Participant Guide

Overview

The Databricks AI Social Impact (DAISI) Challenge invites student teams from Singapore’s Institutes of Higher Learning to build data-driven solutions for the country’s most pressing social challenges, using Databricks and open government datasets from data.gov.sg.

Registration and Round 1 closed on 6 October 2026. Ten shortlisted teams are in the 12–26 October mentored build sprint, followed by Demo Day on Tuesday, 27 October, where the top three teams are selected. Those three teams will be invited to showcase at the Databricks Singapore office launch on 4 November 2026.

Key dates

16 Sep

Launch at Data+AI World Tour Singapore, registration opened (completed)

24 Sep

Hands-on training: Get Started with Databricks: End to End, 7–10pm SGT (completed; not recorded — self-paced course available)

6 Oct

Round 1 submissions closed, 11:59 PM SGT (1-page concept note or 3-slide PDF)

7–9 Oct

Shortlisting (completed)

9 Oct

Top 10 teams announced (completed)

12 Oct

Virtual build kickoff, mentors assigned (completed)

12–26 Oct

Two-week mentored build sprint (current phase)

27 Oct

Demo Day: check-in from 3:30 PM SGT, programme 4:15–7:00 PM SGT, dinner 7:00–8:30 PM SGT; top 3 selected

30 Oct

Top 3 teams submit their presentation decks; instructions shared directly with those teams

4 Nov

Top 3 teams invited to showcase at the Databricks Singapore office launch

Submissions & preparation

Round 1 — idea submission (closed)

Closed Tuesday, 6 October 2026, 11:59 PM SGT. Round 1 was an idea submission through Devpost; no working build was needed. Teams chose one format: a one-page concept note or a three-slide PDF.

• Either a 1-page concept note or a 3-slide PDF covering: the problem statement chosen, the proposed solution, the datasets to be used, and the intended Databricks architecture.
• Team name and full member list: name, institution, course, year and email.

Demo Day preparation

No team is required to submit anything before Demo Day. Come ready to present and demonstrate your solution on 27 October. Preparation and backup materials are for your own use and are not submissions. See the Demo Day section for the format and practical tips.

Top three follow-up

Only the top three teams selected at Demo Day need to submit their presentation decks by 30 October 2026. Submission instructions will be shared directly with the top three teams.

Tips for a strong demo

• Scope aggressively — a polished end-to-end solution on one dataset beats a half-built solution across many.
• Tell a story — your dashboard should answer a clear question, not just display data.
• Show the pipeline — mentors want to see how data moves from source to insight.
• Handle messy data — real datasets have nulls, inconsistencies and edge cases.
• Make it actionable — who would use this, and what decision does it inform?

Demo readiness checklist

• The final deployment owner can sign in and open every required resource.
• The app, endpoint, SQL warehouse, pipeline and data are ready in the final workspace.
• The main user journey has been tested from a fresh browser session.
• No token, password, private key or personal data is visible.
• The team can explain the problem, user, architecture, Databricks contribution and presenter handoffs.
• Third-party datasets, libraries, models and templates are credited.
• A screenshot or short recording is ready for the most failure-prone live step.
• Any link you will show opens on the presenting laptop. Nothing needs to be submitted before Demo Day.

Judging criteria

Round 1 and Demo Day are scored against separate published criteria. Judges' decisions are final.

Round 1 — idea submission (completed)

Demo Day — final solution

Problem statements

Three tracks, nine statements. All datasets are published on data.gov.sg. Many can be downloaded or tested without an API key, but rate limits and endpoint requirements differ, so check the official API guidance.

Working with data.gov.sg

Most datasets can be downloaded as CSV or GeoJSON from the dataset page, queried through the datastore API, or read from real-time APIs for weather, air quality, flood alerts and bus arrivals. Some APIs work without a key for testing, but rate limits and endpoint requirements differ. Plan around the published limits rather than working around them.

https://data.gov.sg/api/action/datastore_search?resource_id=<dataset_id>&limit=100

Official data.gov.sg API guidance

• API overview
• API rate limits
• Real-time APIs
• Search and filter within a dataset

Recommended Databricks workflow

1. 1
   Data ingestion

   Ingest raw datasets from data.gov.sg APIs and CSVs into Delta Lake tables using Auto Loader or notebook-based ingestion with schema inference.

2. 2
   Data engineering

   Clean, join and transform raw data into analysis-ready tables. Apply data quality checks and document lineage.

3. 3
   Machine learning

   Build and train models on the prepared datasets. Use MLflow to track experiments, compare runs and register the best model.

4. 4
   Analytics & BI

   Build an interactive AI/BI dashboard that makes findings accessible to a non-technical audience and clearly actionable.

5. 5
   Governance (stretch)

   Catalogue datasets in Unity Catalog. Document sources, apply access controls appropriate to sensitivity, and show lineage from raw source to final model.

Databricks Free Edition

Free Edition is a no-cost, serverless workspace for learning and prototyping with data and AI. It includes usage quotas and is intended for non-commercial use.

1. 01
   Create your account

   Sign up for Databricks Free Edition with your preferred sign-in method. An existing Free Edition account works too.

2. 02
   Open your workspace

   Complete the welcome flow and confirm you can create a notebook, start the SQL warehouse and navigate the workspace.

3. 03
   Set up the team

   Let every teammate explore in their own Free Edition workspace, then choose one workspace as the final demo workspace.

4. 04
   Deploy early

   If you are building an app, get a small version running in the final workspace before adding features.

5. 05
   Prepare the demo state

   Test the exact path you will show, keep required data in the final workspace, and record a fallback for any live step.

What you can build

• An AI app or agent using Databricks Apps, Foundation Model APIs and model serving.
• An analytics experience using notebooks, SQL, dashboards and Genie spaces.
• A data pipeline that ingests, cleans and transforms data with Lakeflow.
• A database-backed app using Lakebase and Databricks Apps.
• An ML experiment tracked and evaluated with MLflow.

A practical build path

• Define the user and problem in one sentence: who has it, what they need, how you help.
• Prepare a small dataset you can load, query and reproduce before scaling up.
• Build one end-to-end slice — data, core logic and user-facing output — before extras.
• Add the Databricks capability that matters because it improves the solution, not to pad the feature count.
• Deploy and rehearse from the final workspace, within the 5-minute presentation slot.

Use AI and data responsibly

• Never upload confidential, customer or personal data you do not have permission to use, or any credential.
• Review AI-generated code, SQL, citations and outputs, and test the cases that matter to your demo.
• Credit datasets, libraries, models and templates in your submission.
• Be clear with judges when data is synthetic, a response is cached, or a feature is a prototype.

Limits & quotas to plan around

Free Edition is serverless-only and governed by fair-usage quotas. When a quota is exceeded, the affected compute can become unavailable for the rest of the day, and in extreme cases the rest of the month. Keep the project small and test the final flow before the demo. Official Free Edition limitations

• SQL warehouse: one serverless warehouse at 2X-Small size.
• Jobs: up to five concurrent job tasks per account.
• Lakeflow pipelines: one active pipeline per supported pipeline type.
• Model serving: limited active endpoints; no GPU serving and no provisioned throughput.
• AI Search: one endpoint and one search unit; Direct Vector Access is not supported.
• Databricks Apps: up to three apps per account; apps can auto-stop after 24 hours and be restarted.
• Lakebase: one project per account, with scale-to-zero behaviour.
• Workspace administration: one workspace and one metastore; no account APIs, SSO or SCIM.
• Languages and features: no R or Scala, no custom workspace storage, online tables or clean rooms.
• Service level: non-commercial use, with no support SLA or guaranteed reliability.
• Outbound internet: access is limited to trusted domains, so test external dependencies and API connectivity in advance.

How to conserve quota

• Work locally when practical, then deploy to Free Edition regularly.
• Deploy once early to confirm the real deployment path.
• Use a representative data sample that proves the idea without wasted compute.
• Stop what you are not using — idle notebooks, jobs, pipelines, apps and endpoints.

If something stops working

Compute is unavailable

Stop unused resources, reduce the workload, and check whether the daily quota needs time to reset.

A model is unavailable

Select another available pay-per-token model and re-test the response format.

A teammate cannot access the final state

Confirm they were invited to the selected demo workspace and that shared assets live there.

The live path still fails

Show the prepared fallback, explain what the audience is seeing, and continue with the product story.

Official resources