Import Monday

Description

Imports the items of one or more monday.com boards into project tables over a monday.com connection. Use it to bring project tracking, intake queues, or operational task data into a workflow alongside financial data.

Unique Configuration Items

Connection Configuration

The monday.com connection to read through.

Target Table

One row per board: the board ID to read and the project table it is written to. A single step can import several boards at once. Each target must be a table, not a view.

Common Configuration Items

Data Mapper Configuration

Table Data Mapper

The Data Mapper is used to map columns from the source data to the target data table.

Inspection and Populating the Mapper

Using the Inspect Source menu button provides additional ways to map columns from source to target:

Populate Both Mapping Tables: Propagates all values from the source data table into the target data table. This is done by default.
Populate Source Mapping Table Only: Maps all values in the source data table only. This is helpful when modifying an existing workflow when source column structure has changed.
Populate Target Mapping Table Only: Propagates all values into the target data table only.

If the source and target column options aren’t enough, other columns can be added into the target data table in several different ways:

Propagate All will insert all source columns into the target data table, whether they already existed or not.
Propagate Selected will insert selected source column(s) only.
Right click on target side and select Insert Row to insert a row immediately above the currently selected row.
Right click on target side and select Append Row to insert a row at the bottom (far right) of the target data table.

Deleting Columns

To delete columns from the target data table, select the desired column(s), then right click and select Delete.

Column Data Type

Every target column carries a Type. A column propagated from the source keeps the source column’s type; a row you add by hand starts as Text, and you set Type to say what it should actually be — a column you intend to sum or sort needs its numeric or date type, because text sorts and sums as text.

A step saved with a column that has no type at all still runs. The type is worked out when the step starts, from the source column that column reads. Where there is nothing to read it from, it is treated as text: an Extract SQL step’s columns come from the query you wrote rather than from a mapped source, and where several sources carry the same column name under types that disagree, text is used rather than guessing one of them — a wrong guess produces a conversion the warehouse can reject, and anything converts to text.

Setting Type yourself is still how you say what a column should be. Leaving it unset means accepting whatever the source column happens to be.

Changing Column Order

To rearrange columns in the target data table, select the desired column(s). You can use either:

Bulk Move Arrows: Select the desired move option from the arrows in the upper right
Context Menu: Right clikc and select Move to Top, Move Up, Move Down, or Move to Bottom.

Reduce Result to Distinct Records Only

To return only distinct options, select the Distinct menu option. This will toggle a set of checkboxes for each column in the source. Simply check any box next to the corresponding column to return only distinct results.

Depending on the situation, you may want to consider use of Summarization instead.

The distinct process retains the first unique record found and discards the rest. You may want to apply a sort on the data if it is important for consistency between runs.

Aggregation and Grouping

To aggregate results, select the Summarize menu option. This will toggle a set of select boxes for each column in the target data table. Choose an appropriate summarization method for each column.

Group By
Sum
Min
Max
First
Last
Count
Count (including nulls)
Mean
Standard Deviation
Sample Standard Deviation
Population Standard Deviation
Variance
Sample Variance
Population Variance
Advanced Non-Group_By

For advanced data mapper usage such as expressions, cleaning, and constants, please see the Advanced Data Mapper Usage