I am trying to ingest 2 csv files into a single spark dataframe. However, the schema of these 2 datasets is very different, and when I perform the below operation, I get back only the schema of the second csv,…
I am trying to install sparkmaic in jupyter notebook through pip but getting below error: Command: pip install sparkmagic error: Collecting gssapi>=1.6.0 Using cached gssapi-1.8.1.tar.gz (94 kB) Installing build dependencies ... done Getting requirements to build wheel ... error error:…
Description Within PySpark, even though a DataFrame can be saved to S3/S3A (not AWS, but a S3-compliant storage), its data are saved unencrypted despite that setting fs.s3a.encryption.algorithm (SSE-C) and fs.s3a.encryption.key are used. Reproducibility Generate the key as followed: encKey=$(openssl rand…
I'm struggling with the export of a pyspark.pandas.Dataframe to an Excel file. I'm working on an Azure Databricks Notebook with Pyspark. My goal is to read a csv file from Azure Data Lake Storage container and store it as a…
I have a pyspark job that is distributed in multiple code files in this structure: flexible_clendar - Cache - redis_main.py - Helpers - helpers.py - Spark - spark_main.py - main.py In the 'main.py' I'm using the functions from 'helpers.py', 'redis_main.py',…
I am facing issue when i try to write file in S3 as CSV. I am basically trying to overwrite existing single csv file in an S3 folder. Below is the peice of code in I'm running. I am getting…
When I am trying to create a sparksession I get this error: spark = SparkSession.builder.appName("Practice").getOrCreate() py4j.protocol.Py4JError: org.apache.spark.api.python.PythonUtils.getPythonAuthSocketTimeout does not exist in the JVM This is my code: import pyspark from pyspark.sql import SparkSession spark = SparkSession.builder.appName("Practice").getOrCreate() What am I doing…
Description : I have a list of column names which I need. I want to check if all these columns names are present in dataframe.if some columns are present then use those columns and make a generic code like Df1=df.select(df[column1],df(column2])…
Could someone please help me understand why we need to use PySpark or SprakSQL etc if the source and target of my data is the same DB? For example, lets say I need to load data to table X in…
I was trying to read from a table in snowflake and manipulate data and trying to write back ! I was able to connect to snow flake , read data as dataframe but cannot write back to the table code…