How to truncate a lakehouse table.

I just create a copy of the dataflow and set the method to "replace" instead of append. Run that first, and then have the other "Append" dataflows after that (using pipelines can really help keep this straight)

Hope this helps!

Scott

p.s. what I completely DON'T know how to do is delete "some" of the data out. For example "I accidentally loaded Jan 2023 twice, I need to delete just that month and then reload it once". No clue on that - maybe partitioning and deleting the partition files, but that doesn't help if the tables aren't partitioned by the slice you want to delete

Let me become precise. In one dataflowgen2 I have multiple tables. When dataflowgen2 refresh triggers all the tables within this flow will start inserting data into same lakehouse table parallely. I took this decision because my source table has 40 million rows. I divided this huge table into chunks. I am thinking that we would need to spinup spark cluster and run the pyspark script to wipe out all the rows in the lakehouse table and insert data. I am not sure about the script to be written to notebook. I don't want log files also to trace back. Data will be always available in source system.

That's exactly what I'm doing - running multiple jobs in parallel to populate a large table (in my case just over 1 billion rows). The first Gen2 dataflow does a "replace" which truncates the data, and then loads it's slice. After it's done, I launch the other Gen2 dataflows, all of which append in parallel.

Hope this helps,

Scott

what are the repercussions if the destination table didn't exist downstream? Eg I'm thinking that dropping the table beforehand and then recreating via the data flow may have less overhead