Tag: PySpark

First, we’ll quickly go over the fundamental ideas behind Apache Spark and Databricks, their relationships with one another, and how to utilize them to model and analyze big data. Why should we use Databricks ? Big data and machine learning-related tasks are primarily carri...

Spark is being integrated with the cloud data platform in the modern data world. Manipulating data with Spark became curial to any data persona like data engineers, data scientists, and data analysts. Last time, we covered a trivial exercise in big data on reading and writing static dat...

1. Introduction to PySpark PySpark is the Python API for Apache Spark, a distributed data processing framework that is designed for speed and ease of use. It allows you to work with large datasets in parallel and perform data processing tasks efficiently. PySpark provides a high-level API for dis...

This article explains two ways one can write a PySpark DataFrame with timestamp column for a given range of time. A) Plain way Here are the steps to create a PySpark DataFrame with a timestamp column using the range of dates: Import libraries: from pyspark.sql import SparkSession ...

How to copy a delta table with dynamically casting all the columns to the data type of the target delta table columns in azure databricks using pyspark In this blog post, I will show you how to copy a delta table with dynamically casting all the columns to the data type of the target delta table ...

What is Spark? — A quick overview Apache Spark is an open-source big data processing framework designed to process and analyze large datasets in a distributed and efficient manner. It provides a faster and more flexible alternative to MapReduce, which was primarily used for batch processing...