Logship / data analysis
Analyze the data you already have.
Logship is an analytical database for more than telemetry. Import an existing dataset or write an integration that sends records from another source, then query and visualize it alongside other data.
From a file to an analysis
Use the logsh CLI to upload CSV or TSV files, or send JSON through the ingestion API. Declare column types when the data needs them. Filter records, group values, and join a second table to add context.
Explore public records
A local example is Charlotte-area voter and property data. Address fields can connect datasets so their combined coverage can be explored geographically. Neighborhood-level counts and maps are a useful way to understand those relationships without publishing individual records.
That workflow starts with checking source permissions and data quality, normalizing address fields, joining the tables, and aggregating the result. It is an analysis you build with your own sources, rather than a bundled public dataset.
Put the results on a map
The Mecklenburg tutorial walks through the datasets, address matching, complete queries, current maps, and saving a dashboard. The same query-to-visualization workflow supports time charts, bar charts, tables, and other render types.
Bring your own integrations
The collection agent is optional for general data analysis. Write a small service or scheduled job to read an API, transform the records, and send JSON to Logship. Keep credentials and scheduling in your integration, and use an inflow token scoped to the destination account and schemas.