TABLEAU PREP SQL UPDATE
TABLEAU PREP SQL CODE

In fact, as one might expect, this is a predicament that applies to any database. This complication is not specific to Tableau Prep. Instead of trying to come up with a creative solution by manipulating the Timestamp column, letвЂ™s take a step back.

In some cases, we may not actually be in control of how the data is input and whether the Timestamp value is updated.

Depending on the case, it is probably no longer possible to uniquely identify rows which are actual and which are out-of-date.

If you do modify the timestamp, it will be inserted as a new row, causing duplication.

If you donвЂ™t modify the timestamp, the row will not be detected by PrepвЂ™s incremental functionality.

TABLEAU PREP SQL UPDATE

Perhaps the first thing you would think is: вЂњWhy do we not simply update the Timestamp of rows that have been modified?вЂќ That is definitely an idea, but there are limitations to fiddling with the Timestamp:

If the Prep data update takes place before the last edit of a message, we won't be able to save the latest version of the message to the database. In our case, the fact is that messages can be modified after they were initially posted. That in itself is a longer story, but letвЂ™s first understand why this can pose a problem. We here at Biztory have just stumbled upon this when working on a Prep Flow to export all of our Slack messages into a database for further analysis. In other words, what happens when a row weвЂ™d already added before, needs to be updated? In the example below, the value of the existing row has changed. ItвЂ™s a case that applies in many different settings and businesses, which is when existing data might be modified. There is however, a scenario where the above might not suffice. It does so based on the fact that we indicated that the Timestamp column is the key it should use to identify new data.ДЄs illustrated above, existing rows are thus left alone (in gray) and weвЂ™re looking at a much more efficient way of inserting new data as opposed to simply clearing the table and reinserting all rows. With each subsequent run, Prep is able to detect which rows are new, and thus to only append (i.e. All existing data is copied and put in place. With the initial transfer, data gets transferred from the source to the target table, with write-back. The following scenario is thus effectively covered: The combination of incremental refresh with the ability to write back to a database is effectively a very powerful combo, that enables a versatile approach allowing you to pick the most optimal approach for identifying new data and inserting that into your destination table.

TABLEAU PREP SQL CODE

If you think about it, that last example can be built without a single line of code and with just two Prep steps! Incremental Updates: A Decent StartД®ven before that, Tableau Prep did already support incremental refreshes of its FlowsвЂ™ data. Aside from that, it can also be used for simpler, no-code data transfers between e.g. machine learning or other data science applications. That approach covers specific use cases such as cleaning data with Prep, then writing it back to the database so it can be picked up by a specialized process for e.g. Write-back was a heavily anticipated functionality that enables analysts who use Prep to pick up their data, perform PrepвЂ™s trademark user-friendly transformations and enrichment and finally write it back to a database table for the next step in the pipeline. For over a year now, weвЂ™ve been blessed with one of Tableau PrepвЂ™s most powerful new features: the ability to write back to databases.

Tableau prep sql