Category: spark

Spark User full article

PySpark @

Spark’s cluster connectivity issues – AppClient$ClientActor – SparkDeploySchedulerBackend – TaskSchedulerImpl

Apache Spark, a powerful tool for distributed computing, occasionally confronts users with connectivity and cluster health issues. Among them, the…

PySpark @

Navigating Hadoop’s Connection refused’ challenge: Causes and resolutions

Hadoop, a popular framework for distributed storage and processing, frequently confronts newcomers and sometimes even experienced users with errors that…

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Extracting minutes from timestamp in Google BigQuery and handling in PySpark

Often in data analytics, there’s a need to extract specific parts of a date or timestamp for more granular analysis….

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Advanced grouping and aggregation operations on DataFrames in PySpark

In this article, we will explore one of the lesser-known yet incredibly useful features of PySpark: grouping_id. We will cover…

PySpark @

Analyzing User rankings over time using PySpark’s RANK and LAG Functions

Understanding shifts in user rankings based on their transactional behavior provides valuable insights into user trends and preferences. Utilizing the…


Step-by-step guide on executing PySpark code from Snowflake Snowpark to read a DataFrame:

Here are the steps on how to execute PySpark code from Snowflake Snowpark to read a DataFrame: 1. Open Snowsight…

PySpark @

PySpark : When are new Stages created in the Spark DAG?

Apache Spark’s computational model is based on a Directed Acyclic Graph (DAG). When you perform operations on a DataFrame or…

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PySpark : Identifying Data Skewness and Partition Row Counts in PySpark

Data skewness is a common issue in large scale data processing. It happens when data is not evenly distributed across…

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PySpark : from_utc_timestamp Function: A Detailed Guide

The from_utc_timestamp function¬† in PySpark is a highly useful function that allows users to convert UTC time to a specified…