Apache Spark Python - Spark Metastore - Saving as Partitioned Table

Data Engineering Spark
1

In this article, we will learn how to create partitioned tables while using the saveAsTable function to write data from a Dataframe into a metastore table. The video provided in the link will complement the text by visually demonstrating the concepts discussed.

Creating Partitioned Tables

Partitioned tables allow you to organize data in a structured manner based on specific criteria. In this case, we will create a partitioned table for orders by order_month to store data efficiently.

orders.write.saveAsTable(
    'orders_part',
    mode='overwrite',
    partitionBy='order_month'
)

Working with Partitioned Data

Once the partitioned table is created, we can access and manipulate the data based on the defined partitions. This enables faster query execution and efficient data retrieval.

spark.read.table('orders_part'). \
    groupBy('order_month'). \
    count(). \
    show()

Note: Visit the provided link to watch the video tutorial for a better understanding of the concepts discussed.

Watch the video tutorial here

Hands-On Tasks

  1. Create a partitioned table for orders by order_month.
  2. Read data from a file into a Dataframe.
  3. Add an additional column for partitioning.
  4. Write the Dataframe into the partitioned table using saveAsTable function.

Conclusion

In conclusion, understanding and working with partitioned tables is essential for efficient data management and query performance. Practice these tasks and engage with the community for further learning.