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You can bring fitness history from separate services into one repeatable set of analytical tables without starting with a hosted warehouse. Export your data, let DuckDB read or store it locally, and use dbt to define transformations you can rerun and maintain. Here, “serverless” means you can use an embedded database without managing a database server; it does not mean every DuckDB setup runs in the cloud.
How the pieces fit together
Think of the workflow as three layers: fitness exports are the inputs, DuckDB is the analytical engine, and dbt is the transformation layer. DuckDB can query files directly or work with data in a database. dbt models organize transformations as repeatable SQL workflows.
The result is a small, portable warehouse: raw exports remain available as inputs, while modeled tables give you consistent structures for questions such as activity volume by week or distance by activity type. Begin with one export and a local database; add other sources only when the first model is trustworthy.
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Choose a local setup before considering hosted options
DuckDB is embedded rather than a database server you must run. Depending on the setup, it can operate in memory or use a local database file. The dbt DuckDB setup documentation also describes connecting to MotherDuck, a hosted option. The hosted route is optional; it is not a prerequisite for a local warehouse. See dbt’s DuckDB setup documentation.
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For a personal project, a local file is a practical starting point: it persists between sessions and keeps the workflow on your machine. In-memory operation can suit an experiment, but the database disappears when the process ends unless you save outputs elsewhere. A hosted connection changes where the database runs and introduces a service boundary; choose it for a concrete collaboration or access need, not because “serverless” implies that cloud hosting is required.
Pick a supported dbt route and check its limits
dbt’s product interface matters. The current supported-platform documentation lists DuckDB as generally available for dbt v2 through the CLI only, and notes lifecycle differences between local development and the dbt platform. Do not assume the same adapter behavior or feature set in every dbt interface. Check the current supported data platforms page and the setup guide before choosing versions or adapting commands.
The dbt v2 DuckDB setup page also documents limitations relevant to this workflow: its bundled driver does not support loading DuckDB extensions, and static analysis can have issues when models reference local CSV, Parquet, or JSON files through DuckDB file-reading functions. If a model depends on a community extension or direct local-file reads, verify that the selected driver and dbt workflow support that path. The setup guide describes using a system-installed driver for extensions.
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Start with an export you already have
Fitness exports are a sensible first input because they are portable files rather than a dependency on ongoing vendor connectivity. The documented examples cover two different paths:
- Apple Health: the DuckDB community extension
healthkit_exportreads an Apple Healthexport.ziporexport.xmland exposes typed tables for records, workouts, activity rings, workout routes, and GPX points. Its extension listing provides installation and load commands: healthkit_export listing. It is community-maintained, so check compatibility with your DuckDB version and review the extension’s status before relying on it. - Garmin activity files: the community
fitextension reads.fitfiles and exposes records, activities, sessions, laps, and device information. See the fit extension listing. This is file ingestion, not a live connection to a Garmin service.
These examples do not establish that DuckDB syncs automatically with fitness platforms. Treat the export as a snapshot: when you obtain a newer export, decide how to load it and whether to replace, append, or deduplicate records. Confirm the source’s field definitions and units before combining datasets.
Build a small model before adding more data
Keep the first version deliberately narrow. A useful design separates source-shaped staging models from a normalized activity table and from summaries. The names below are suggested design choices, not required schemas; match them to the actual columns in your export.
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Stage each source
Create a staging model for each imported table or file. Preserve source fields and make only clear, reversible adjustments, such as renaming columns or parsing a date. Keeping source-specific staging models makes it easier to trace a questionable result back to its origin.
Normalize activities
Build a canonical activity model with fields such as activity date, duration, distance, activity type, and source. Define units explicitly—for example, seconds for duration and meters for distance—and convert only after confirming the input unit. A field named “distance” is not enough evidence that two platforms report the same unit or meaning.
Aggregate only after validating the base model
Once activity rows are credible, create summaries such as weekly volume or totals by activity type. Keep these as derived models so changing a source mapping does not require hand-editing a spreadsheet of results. dbt’s role is to make transformations repeatable and reviewable; its author Petrica Leuca describes dbt as an open-source transformation framework that supports practices such as Git workflows and unit testing in “Fully Local Data Transformation with dbt and DuckDB”.
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Expand beyond local exports when there is a reason
DuckDB documents access to multiple kinds of sources, including CSV, JSON, Parquet, S3-compatible storage, Azure Blob Storage, Cloudflare R2, MySQL, PostgreSQL, and SQLite. The breadth makes it possible to combine more than one local export or reach other storage and database systems, but it does not make cloud storage necessary for an individual workflow. See DuckDB’s data sources overview.
The dbt-duckdb documentation also describes external storage paths through fsspec-backed integrations and connection strings for MotherDuck. External files can avoid loading every input into a local database first, but they add configuration and may change access or credential requirements. Keep inputs local until the convenience of an external source outweighs that complexity. For adapter details, consult the dbt-duckdb README.
Validate the data before trusting the summaries
Fitness records from different sources are not automatically comparable. Before treating a total as meaningful, check the source data and the transformations that produced it.
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- Compare row counts in each staging model with the number of records you expect from the export.
- Inspect minimum and maximum dates to spot missing periods, unexpected time zones, or parsing errors.
- Check for duplicate activities, especially if a newer export overlaps an earlier one.
- Verify duration, distance, and other measurement units source by source before aggregation.
- Review nulls and unusual values in the normalized activity model rather than silently converting them to zero.
These checks are especially important when imports are refreshed. A repeatable transformation can reliably repeat a mistaken assumption, so validate the model’s definitions as well as its row totals.
Keep the result portable
You can retain the DuckDB database file as the working warehouse, or export derived data for other tools. DuckDB’s EXPORT DATABASE documentation describes CSV and Parquet exports and reloading them with IMPORT DATABASE. Import into a fresh database: the exported schema uses plain CREATE statements, which can conflict with existing objects. Check the syntax for your installed DuckDB version in the EXPORT and IMPORT DATABASE documentation.
dbt-duckdb also documents external materialization for file outputs, which can suit models you want written as files rather than kept only as database tables. Use CSV for broad interchange or Parquet for a columnar data file, and retain the database and model definitions if you want to reproduce the outputs later.
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