Can I get some advice on how we can improve our integration between data ingestion and ML using delta lake? Right now our data ingestion is done in databricks notebooks and is saved as hive tables. Pain points with this setup is lack of good version control for the ingestion code and how it integrates with downstream ML. We end up running into errors in feature generation because some schema updates have been made but not communicated well from data team to ML. Ideally we would like to have FG run with every schema update and reject the change or log the failure to be able to catch those issues earlier. I am new to delta lake so if there are any resources I need to check out please point me to them.