Building a data loader
A walk-through building Northgate Energy's broker settlements loader step by step, from extracting a sample to running it
A walk-through building Northgate Energy's broker settlements loader step by step, from extracting a sample to running it
Making event timeseries, event curves and event options once from a data loader's events, kept on the same master data record
An insight report of the Data Loaders extension that shows every run of the data loaders in a period, what they loaded, the rows they left out or could not load, and the runs that failed
Deploying a data loader as a workflow and a process, running it now or for an earlier date, its schedule, its runs and run reports, alerts, and changing or deleting it
Ask Fusion AI to propose a data loader's event type, mapping, conditions, event id and dataset id from the sample
What a data loader is made of, the workflow of standard ETL actions it runs, and what deploying it creates
How the Map step turns columns into event fields, with data types, date formats, decimal commas, empty values, value maps, conditions and previews
Where data loaders and everything they deploy are kept, the settings a process passes to its workflow, the run report fields, the extension's contents, and troubleshooting
The settings of each kind of data loader source (CSV, JSON, XML, Excel and custom ODSL), and dates in URLs
The standard private actions every data loader's workflow runs (etl_extract, etl_transform, etl_load and etl_report), their inputs, outputs and exits, and using them in your own workflows
An introduction to the Data Loaders extension, which builds data loaders step by step that turn files and API data into events and run them on a schedule