Help Centre

The Data section, or Data Repo, is the storage area for clean datasets and reference data.

Datasets with a single row of headers can be uploaded directly to the Data Repo to create a reference dataset for use in pipelines – for example, a target header schema to map to. The upload process will ignore any blank rows above or below the data, or blank columns either side of the data. The first line of data will be interpreted as column headers.

Read more about uploading data to the data repo.

Cleansed outputs from pipelines can also be exported to the Data Repo for onward sharing via API, sharing within Quantemplate, or reporting on in Analyse.

Datasets are organised in your Data repo. To access your datasets, click the folder button next to the dataset name: the repo view opens.

Datasets are sorted alphabetically by default by default.

The repo view shows:

• Dataset name
• A lock icon if the dataset is Read Only
• Row count
• Last updated date
• Last updated by
• Owner
• Number of users with access. Click here to see details of users and manage access
  
  
• Items shared with the whole organisation are indicated by the ‘office building’ icon
• Items shared with the API user are indicated by the ‘API connection’ icon
• The Dataset information popup for information on how a dataset has been created used and shared externally.
  
  

Sort, search and filter datasets

Datasets can sorted by dataset name, updated date and user last updated by. Click the arrow button to reverse the sort direction.

Click in the search field to search datasets by name.

Click the filter icon or hit ‘F’ on the the keyboard to filter datasets by date created, date updated, dataset name, owner, updated by user.

Duplicating datasets

Click the three dots at the end of the row and select ‘Duplicate’ from the popup menu. The dataset is instantly duplicated and the document opened. The suffix ‘– copy’ is added to the duplicate dataset’s name.

Renaming datasets

Click the three dots at the end of the row and select ‘Rename’ from the popup menu. Alternatively, edit the dataset name by opening the document and clicking on the name. Name changes propagate throughout the system – any reports or pipelines using that dataset will now display the updated name.

Archiving datasets

Click the three dots at the end of the row and select ‘Archive’ from the popup menu. Archived documents are not deleted, but hidden from the repo and the pipeline input selector

Archived datasets are ignored by the pipeline and will prevent a pipeline from running if used in a Join stage.

Archived datasets display a warning in the top right of the dataset view.

Note that datasets in Quantemplate cannot be deleted permanently by users. Contact us if you have a specific deletion requirement.

View and restore archived datasets

1. To show archived documents, click the ‘Show/hide archived’ button in the top right of the repo, or hit ‘A’ on the keyboard.
2. Click a dataset to open it.
3. To restore a dataset to the repo, click the three buttons at the end of the row and select restore.

Create a new dataset from within the data view:

1. Click the + button next to the dataset name. This creates and opens a new empty dataset.
2. Name your dataset by clicking on the dataset name.
3. Upload your data as XLS, XLSX, CSV or GZipped CSV files. Drag and drop straight in, or choose a file. Depending on the size of your dataset and the speed of your internet connection, this could take a few minutes.

Create a new dataset from within the repo view

1. Open the repo and click the green ‘New’ button at top right.
2. Follow steps 2 and 3 above.

The full change history of a dataset is logged in the Dataset History popup, on the top right.

The dataset history shows a dataset's revision number, date/time of revision, last editor, and the name of the dataset at the time of revision.

Dataset owners and editors can restore to a previous version of a dataset by clicking the green restore button to the right of the dataset name in the popup. The restored version will become the latest revision of the dataset.