The updated data exists in Parquet format. df = sqlContext.createDataFrame ( [ (10, 'ZZZ')], ["id", "name"]) This approach requires the input data to be Spark DataFrame. Create a DataFrame from the Parquet file using an Apache Spark API statement: Python. Solved: I am trying to update the value of a record using spark sql in spark shell I get executed the command - 136799. Table deletes, updates, and merges — Delta Lake Documentation updatesDf = spark.read.parquet ("/path/to/raw-file") How to UPDATE a table using pyspark via the Snowflake Spark connector. With the UI, you can only create global tables. When using the UPDATE statement, all of the rows in the table can be modified or just a subset may be updated using a condition. Upsert into a table using merge. field_name. Spark provides many Spark catalog API's. Here, I have covered updating a PySpark DataFrame Column values, update values based on condition, change the data type, and updates using SQL expression. 1) Global Managed Tables: A Spark SQL data and meta-data managed table that is available across all clusters. [WHERE clause] Parameters. Spark SQL Using Python - An Explorer of Things Spark Join Multiple DataFrames | Tables - Spark by {Examples} In the Maintenance database field, enter the name of the database you'd like to connect to. Table deletes, updates, and merges - Azure Databricks | Microsoft Docs Spark stores the details about database objects such as tables, functions, temp tables, views, etc in the Spark SQL Metadata Catalog. For the purpose of demonstration let's update AGE value to be 30 and CITY value to be PUNJAB where CITY value is 'NEW DELHI'. The alias must not include a column list. Two tables in our database. Set Column1 = Column2. Spark withColumn () function of the DataFrame is used to update the value of a column.