Naperville IL 311
Public data
Service requests from Naperville, Illinois's 311 system from 2023 onward. Ingest delivers this data to a data warehouse you own as one table, and updates it on the schedule you choose. There is no code to write and nothing to install.
- 50,377service requests as of September 2026
- About 490new service requests a week
- 1 tableone row per service request
What people use it for
- See current stateReview the complete current collection to spot active problem areas.
- Find where problems clusterMap requests by coordinates to identify neighborhoods needing attention.
What you get
| Table | Each row is |
|---|---|
Service requestsfeatureserver | One service request |
Good to know
Each run replaces the table with a fresh copy, so edits and removals at the source show up after the next run.
How it works
- Choose Naperville IL 311. The data is public, so there is nothing to sign in to.
- Pick where it goes. BigQuery, Snowflake, Databricks, Redshift and 10 more, in an account you own.
- Pick a schedule. Daily is the default, and weekly and monthly are there too.
- Ingest runs it from then on. If a run fails, your dashboard shows which table and why.
Questions
- What does the Naperville IL 311 data include?
- One table, service requests, with one row per service request.
- Do I need an account with Naperville IL 311?
- No. The data is public, so there is nothing to sign up for and nothing to paste in.
- Which warehouses can Naperville IL 311 data go to?
- Amazon S3, Google Cloud Storage, Azure Blob Storage, Apache Iceberg, Ingest Managed Lakehouse, MotherDuck, Postgres, Amazon Athena, Databricks, Redshift, BigQuery, ClickHouse, MySQL / generic SQL and Snowflake, in an account you own.
- How fresh is the data?
- As fresh as your schedule: daily by default, or weekly or monthly. Schedules as often as every 15 minutes are on the Enterprise plan. Each run replaces the table with a fresh copy, so edits and removals at the source show up after the next run.
- Will running it again create duplicate rows?
- No. Each run replaces the table, so it never holds two copies of the same row.
- Do I need to write code?
- No. Ingest builds, runs and maintains the connector. Someone with access to your warehouse connects it once, following a short guide.
For your data team: technical reference
Connector naperville_311, version 0.1.0, a REST source, status beta, authentication none.
Vendor documentation: https://services1.arcgis.com/rXJ6QApc2sOtl1Pd/arcgis/rest/services/Service_Request/FeatureServer
Configuration
No configuration required.
Resources
| Resource | Status | Plan | Write | Primary key | Overrides |
|---|---|---|---|---|---|
featureserverAll public 311 service requests exposed by layer Service_Requests (layer 0) of the City of Naperville's Service_Request ArcGIS Online feature service. Each run fetches the complete current layer contents (50,377 records, measured 2026-09-13) via the layer's native resultOffset/resultRecordCount pagination at its 2,000-record maximum, and REPLACES the destination table with that snapshot: records later removed from the public layer drop out, and every run reflects the current public state, including later closure dates and status changes on already-loaded requests (a full snapshot, so the creation-only increment gap does not apply). The layer is a rolling public view (isView) over the city's Service_Requests feature class, currently holding requests created 2023-01-01 through 2026-09-13 (measured min/max DATE_CREATED); it is not a permanent archive, so full history must not be assumed if the city retires older records from the view. DATE_CREATED and DATE_CLOSED are ArcGIS native Date fields that travel as UTC epoch MILLISECONDS and are loaded as bigint columns (unit: milliseconds since 1970-01-01); REQUESTID is the human-facing request number and is nullable in the schema, while OBJECTID is the layer-declared, system-maintained unique key and is the primary key. | beta | full | replace | OBJECTID | - |
Where this lands, and what else connects
Setting up the destination is its own short guide, one per warehouse or lake: Amazon S3, Google Cloud Storage, Azure Blob Storage, Apache Iceberg, Ingest Managed Lakehouse, MotherDuck, Postgres, Amazon Athena, Databricks, Redshift, BigQuery, ClickHouse, MySQL / generic SQL, Snowflake.
Other sources Ingest connects for government teams: Abilene TX 311, Albany NY 311, Ann Arbor MI 311, Anne Arundel County MD 311, Auburn WA 311, Aurora IL 311.
- How Ingest reads a REST source
- Ingest for government teams
- The tables Ingest creates
- Every source Ingest connects
Get Naperville IL 311 data into your warehouse
Last updated .