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Data processing and process units

The Data Processing menu is where you query, transform and store your data beyond what a dashboard shows.

Page What it's for
Data Analysis Build a query, look at the results, refine. The place to explore.
Process Units Your saved, reusable processing steps
Process Unit Builder A visual builder for process units
Alert Builder Alerts on query results. See Alerts.
Datasets The tables your processing writes to
Macros and Snippets Reusable pieces of logic and code
External Data Access Query your data from your own Python environment

Process units

A process unit is a saved step that reads data, optionally transforms it, and writes the result somewhere. Once saved, it can run on a schedule as part of a pipeline, feed a dashboard, or be reused by other units.

%%{init: {"flowchart": {"curve": "linear"}}}%%
graph LR
    A["Query modules<br/>(one or more data sources)"] --> B["Post-processing<br/>function or algorithm"]
    B --> C["Destination<br/>storage, partitioning, retention"]

Process unit list

Build one

  1. Open Data Processing → Process Unit Builder and click Create new process unit.
  2. Give it a name and description. Anosys can draft the description for you.
  3. Configure Query Module: add at least one data source and the query to run. Add more modules to combine sources.
  4. Configure Post-Processing Stage (optional): run a function or a built-in algorithm on the results, with its parameters.
  5. Configure Data Destination: choose where results go and set storage, partitioning and retention.
  6. Save. The unit appears in Process Units, where you can run, edit or clone it.

Start in Data Analysis

Get the query right in Data Analysis first, where you can see results immediately. Then turn it into a process unit.

Macros and snippets

  • Snippets are small pieces of code you write once and reuse across process units.
  • Macros are reusable building blocks that templates and pipelines can deploy.

External Data Access

If you want to work with your data in your own notebooks or scripts, External Data Access generates a temporary access token and a ready-to-run Python example. The token expires automatically.