Databricks Community

# in python: explicitly define the schema, read in CSV data using the schema and a defined timestamp format.... <a href="http://thestoreguide.co.nz/auckland/orewa/mcdonalds-orewa-akl-0931/">McDonald’s in Orewa</a>

I agree about this being a usability bug. Documentation clearly states that if the optional flag "IF EXISTS" is provided that the statement will do nothing.https://docs.databricks.com/spark/latest/spark-sql/language-manual/drop-table.htmlDrop Table ...

Hi @matt@direction.consulting I just found the following doc https://docs.azuredatabricks.net/delta/isolation-level.html#set-the-isolation-level. In my case, I could fixed partitioning the table and I think is the only way for concurrent update in t...

I think you can get some pretty good insight into the optimizations on Databricks here:https://docs.databricks.com/delta/delta-on-databricks.html Specifically, check out the sections on caching, z-ordering, and join optimization. There's also a grea...

@andersource Looks like the bucket is in us-east-1 but you've configured your AmazonS3 Cloud platform with us-west-2. Can you try switching configuring the client to use us-east-1 ? I hope it will work for you. Thank you

exit(value: String): voidCalling dbutils.notebook.exit in a job causes the notebook to complete successfully. If you want to cause the job to fail, throw an exception.

the same problem here (similar code and the same behavior with Spark 2.4.0, running with spark submit on Win and on Lin) dataset.coalesce(1) .write() .option("charset", "UTF-8") .option("header", "true") .mode(SaveMod...

Hi @rishigc You can use something like below. SELECT explode(arrays_zip(split(Product, '+'), split(Price, '+') ) as product_and_price from df or df.withColumn("product_and_price", explode(arrays_zip(split(Product, '+'), split(Price, '+'))).select( ...

Hi Siddhu, You can use df.select(sum("col1"), sum("col2"), sum("col3")) where col1, col2, col3 are the column names for which you would like to find the sum please let us know if it answers your question Thanks

Hi @pascalvanbellen ,There is no concept of FK, PK, SK in Spark. But Databricks Delta automatically takes care of SCD type scenarios. https://docs.databricks.com/spark/latest/spark-sql/language-manual/merge-into.html#slowly-changing-data-scd-type-2 ...

You can use dropDuplicates https://spark.apache.org/docs/latest/api/python/pyspark.sql.html?highlight=distinct#pyspark.sql.DataFrame.dropDuplicates

Well, just a little bit research, and i found this post below: Hopefully this will help. " registerTempTable() registerTempTable() creates an in-memory table that is scoped to the cluster in which it was created. The data is stored using Hive's high...

What eventually worked for me was displayHTML(profile.to_html()) for the pandas_profiling and displayHTML(profile.html) for the spark_profiling.

The documents are out for databricks-connect: https://docs.azuredatabricks.net/user-guide/dev-tools/db-connect.html I've also written up about a few limitations I have found - some with workarounds: https://datathirst.net/blog/2019/3/7/databricks-co...