![]() On the contrary, if your purpose is to commit the updates in the source DB, then click on the right arrow ( Synchronize data dictionary with model) and you will see, once again, a Compare Models window being displayed. This great feature (aka Compare/Merge Utility) can involve not only a live database but also other models. To double-check we can retry to synchronize our model and see if it does not pick up further differences. ![]() Keep in mind that you should check only the boxes of those elements you really want to be affected.Ĭhanges seem to have been correctly applied. Select the existing connection to the Snowflake, press Ok and (if required) enter your Snowflake account password.Īfter that, you will see the SDDM immediately starting comparing the current model with the remote DB and, once finished, it will show a report signalling all the differing objects with a warning:įor example, above you may see our ‘new_attribute’ marked as missing in the target table.Īfter ticking the To Drop boxes and eventually clicking Merge, SDDM will automatically integrate the current model with the target one undoing our last changes. When clicking on the left one, a window like this will pop up … So, proceed as follows: in the toolbar you may find two blue arrows ( ← →) Obviously the list of possible actions is not limited to this but there is a myriad of other possible operations you can try.Īnyway, if at any point you are not satisfied with your changes, you might want to revert the model to its original state in this case, all you need to do is re-synchronize the model against your remote Snowflake DB. To generate the logical model click on > in the toolbar in order to extend the updates to the relational model. Ok, now we can get back to our TPCH_1000 relational model. So pay attention when defining new attributes! Note that there may be some issues also on the other side: in fact, as you know, Snowflake does not currently support BLOB, CLOB and user-defined data types. To check if all that was not a useless effort, let’s try to import again our sample table. Just remember to navigate back to the third tab in this same panel and click on the Logical type in order to select your new custom type from the dropdown menu. Luckily, in Snowflake official blog you may find a useful workaround ( ): in these circumstances, in the Tools → Types Administration → User defined native types tab we need to add three different entries (one for each data type) intitially set to the default unknown.įor each one, in the Logical types to native types tab we will choose a valid, meaningful, name and we’ll pick our newly added type from the dropdown menu alongside the database we are working with. This because Oracle is not capable of automatically detecting Snowflake VARIANT, ARRAY or OBJECT data types without some extra work prior to the import. When working with such tables all these columns are actually generically imported as VARCHAR, which is definitely not what we wanted. Unfortunately, it’s not always a piece of cake and this operation can frequently bring some inconsistencies. Then select the needed tables in the target schema of your “SNOWFLAKE_SAMPLE_DATA” DB just ticking the corresponding boxes. To do that, select File → Import → Data Dictionary from the menu and choose the jdbc connection to Snowflake (to learn how to connect see ). How to use SSDM working with Snowflakeīefore starting, we will import the TPCH_SF1000 schema from the Snowflake samples and we will try to reverse engineer it. In this quick example we will go through it particularly focusing on Snowflake. Since its release, Oracle Sql Developer Data Modeler has become extremely popular and has been worldwide adopted: in fact, compared to other famous tools it is completely graphical and, above all, can easily connect to many different DBs. ![]() ![]() Indeed, SSDM not only can help you to report, define and efficiently structure your DB metadata, but it also lets you create an ER diagram from an existing database ( reverse engineer), or generate a DDL from a given ER diagram ( forward engineer). If not, just know that it may incredibly simplify your life. However, if you are a data engineer, then you might have already used it at least once. Oracle Sql Developer Data Modeler (SDDM) is a free-to-use data modeling tool born to support developers while designing their database, or architecture, at a logical, relational or physical level, even storing and versioning changes in an easy and comprehensible way. ![]()
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