# read file(s) in spark data frame sdf = spark.read.format('parquet').option(recursivefilelookup, true).load(source_path) # create new delta table with new data sdf.write.format('delta').save(delta_table_path) Web option 1 : Web i am trying to load data from delta into a pyspark dataframe. Is there a way to optimize the read as dataframe, given: Web is used a little py spark code to create a delta table in a synapse notebook. Web read a table. 2 timestampasof will work as a parameter in sparkr::read.df. Web seems the better way to read partitioned delta tables is to apply a filter on the partitions: Parameters pathstring path to the delta lake table. It provides code snippets that show how to read from and write to delta tables from interactive, batch, and streaming queries.
Df = spark.read.format (delta).option ('basepath','/mnt/raw/mytable/')\.load ('/mnt/raw/mytable/ingestdate=20210703') (is the basepath option needed here ?) option 2 : Web 2 answers sorted by: Python people_df = spark.read.table(table_name) display(people_df) ## or people_df = spark.read.load(table_path) display(people_df) r people_df = tabletodf(table_name) display(people_df) scala If the delta lake table is already stored in the catalog (aka the metastore), use ‘read_table’. Web read a table. Web instead of load function, you need to use table function: It provides code snippets that show how to read from and write to delta tables from interactive, batch, and streaming queries. Table (default.people10m) // query table in the metastore spark. 2 timestampasof will work as a parameter in sparkr::read.df. Web read a delta lake table on some file system and return a dataframe. Is there a way to optimize the read as dataframe, given:
