I am new to PySpark. My current project requirement is to do ETL in Databricks. I have a CSV file which has almost 300 million rows, and this is only one such source. There will be 2 more data sources. Below will be my approach to solve it:
Step1 : Create Abstract class and method to read data from various sources
Step2: Read the data from step1 and create dictionaries for each source
Step3: Pass the dictionaries from step 2 into this step and do all the transformations needed
Step4: Load the data into parquet files and then into tables
My problem is in Step 3, where I'll be using the dictionary passed from step2. Will this be possible, because the Data volume is so huge and will be bad in performance.
Please let me know what approach should I follow as I am stuck in step3.
Thank you in advance.
Instead of creating dictionaries you can work directly with Spark DataFrames as they are distributed across the cluster, and operations on them are parallelized, which will significantly improve performance.
For example if you read a CSV file as follows:
df_csv = spark.read.csv("path/to/your/csv", header=True, inferSchema=True)
df_csv is a Spark DataFrame that is distributed across the cluster and can handle large datasets efficiently.
Define a Generalized ETL Method
This can perform transformations on Spark DataFrames, This method can take a DataFrame as input, apply necessary transformations, and return the transformed DataFrame.
Sample example :
def transform_data(df):
# Apply transformations like filtering, aggregating, joining, etc.
df_transformed = df.filter(df["column_name"] > 100) # Example transformation
df_transformed = df_transformed.withColumn("new_column", df_transformed["column_name"] * 10)
return df_transformed
This transformed data can then be loaded into parquet and then tables, if you have a date column you can use it for partitioning which will also help in performance for reading later.
df_transformed.write.partitionBy("date_column").parquet("path/to/output/directory", mode="overwrite")