Author: user
MapReduce vs. Spark – A Comprehensive Guide with example
MapReduce and Spark are two widely-used big data processing frameworks. MapReduce was introduced by Google in 2004, while Spark was…
PySpark : Dropping duplicate rows in Pyspark – A Comprehensive Guide with example
PySpark provides several methods to remove duplicate rows from a dataframe. In this article, we will go over the steps…
PySpark : Replacing null column in a PySpark dataframe to 0 or any value you wish.
To replace null values in a PySpark DataFrame column that contain null with a numeric value (e.g., 0), you can…
AWS Lambda : Export all AWS Lambda function with code in a single go using AWS CLI [For backup]
To export all AWS Lambda functions in a single Go using AWS CLI, you can use the following steps: Install…
Snowflake : DESCRIBE SEARCH OPTIMIZATION – Analyze the query plan for a specific query and identify areas for optimization
In Snowflake, the DESCRIBE SEARCH OPTIMIZATION command is used to analyze the query plan for a specific query and identify…
Redshift : Learn how to link Amazon redshift to s3 bucket
Amazon Redshift is a fully-managed, petabyte-scale data warehouse service in the cloud. It allows you to run complex analytical queries…
PySpark : unix_timestamp function – A comprehensive guide
One of the key functionalities of PySpark is the ability to transform data into the desired format. In some cases,…
PySpark : Reading parquet file stored on Amazon S3 using PySpark
To read a Parquet file stored on Amazon S3 using PySpark, you can use the following code: from pyspark.sql import…
Redshift : Role of VACUUM and ANALYZE in Redshift
Amazon Redshift is a popular data warehousing solution that is widely used by businesses to manage and analyze large volumes…
Google Dataflow : Handling Late Data in Google Dataflow
Handling late-arriving data is a common challenge when working with streaming data processing systems like Google Dataflow. Late data refers…