Python is a versatile and beginner-friendly programming language that has gained immense popularity for its simplicity, readability, and wide range of applications. Whether you’re new to programming or looking to expand your skills, learning Python is an excellent choice. In this comprehensive guide, i’ll provide you with a curated list of resources and tutorials from my website to help you master Python programming from scratch.
PySpark Tutorial | Learn PySpark
PySpark is the Python API for Apache Spark, a powerful open-source framework designed for distributed computing and processing large datasets. By combining the scalability and performance of Spark with Python’s simplicity, PySpark has become an essential tool for data engineers and data scientists working with big data.
Big Data Engineer Interview Questions
Preparing for an interview in the Big Data field can be challenging, given the diverse range of technologies and methodologies involved. To help you excel in your career, I’ve compiled an extensive collection of Big Data interview questions asked by different companies in the industry
Python Free Learning Resources: Your Gateway to Mastering Python Programming
Python, with its simplicity and versatility, has become one of the most popular programming languages today. Whether you’re a beginner or an experienced developer, the abundance of free learning resources available online can help you master Python and unlock its full potential. In this blog post, we present a carefully curated selection of what we believe to be the best free resources available online. From YouTube channels to websites and offline tools, these resources are handpicked to empower you with the knowledge and skills needed to excel in Python programming. Let’s dive in!
End to End Data Engineering Roadmap
End to End Data Engineering Roadmap:
Prerequisites:
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1. Basic Linux commands.
2. Programming fundamentals.
3. SQL is very important.
Discover My Topmate Services
In this article, I will be listing down and explain all my services available on topmate platform but before this let’s talk about topmate first. topmate.io is a platform that enables you to connect with your audience through 1:1 session – to share your knowledge and monetise your time better.
PySpark | How to Perform Data Type Casting on Columns in a DataFrame?
When working with data in PySpark, ensuring the correct data type for each column is essential for accurate analysis and processing. Sometimes, the data types of columns may not match your requirements. For example, a column containing numeric data might be stored as a string (string), or dates may be stored in an incorrect format.
PySpark | How to Remove Non-ASCII Characters from a DataFrame?
When working with text data in Spark, you might come across special characters that don’t belong to the standard English alphabet. These characters are called non-ASCII characters. For example, accented letters like é in “José” or symbols like emojis 😊. Sometimes, you may need to clean your data by removing these characters. This article will show you how to identify and remove non-ASCII characters from a Spark DataFrame.
PySpark | How to Handle Nulls in DataFrame?
Handling NULL (or None) values is a crucial task in data processing, as missing data can skew analysis, produce errors in data transformations, and degrade the performance of machine learning models. In PySpark, dealing with NULL values is a common operation when working with distributed datasets. PySpark provides several methods and techniques to detect, manage, and clean up missing or NULL values in a DataFrame.
In this blog post, we’ll explore how to handle NULL values in PySpark DataFrames, covering essential methods like filtering, filling, dropping, and replacing NULL values.
PySpark | How to remove duplicates from Dataframe?
When working with large datasets in PySpark, it’s common to encounter duplicate records that can skew your analysis or cause issues in downstream processing. Fortunately, PySpark provides some methods to identify and remove duplicate rows from a DataFrame, ensuring that the data is clean and ready for analysis. In this article, we’ll explore two methods to remove duplicates from a PySpark DataFrame: dropDuplicates() and distinct().
PySpark | How to Sort a Dataframe?
Sorting data is a fundamental task in data processing, whether for analysis, reporting, or data transformation. In PySpark, sorting a DataFrame is a common operation that allows you to organize your data based on one or more columns. PySpark provides multiple ways to sort data efficiently, even when dealing with large datasets distributed across clusters.
In this blog post, we’ll explore various methods to sort a DataFrame in PySpark, covering both ascending and descending orders, sorting by multiple columns, and handling null values during sorting.
PySpark | How to Filter Data in DataFrame?
Filtering data is one of the most common operations you’ll perform when working with PySpark DataFrames. Whether you’re analyzing large datasets, preparing data for machine learning models, or performing transformations, you often need to isolate specific subsets of data based on certain conditions. PySpark provides several methods for filtering DataFrames, and this article will explore the most widely used approaches.
Tech Mahindra | Data Engineer Interview Questions – Set 1
In this post, we will see the list of questions asked in Tech Mahindra Company Interview for Data Engineering profile.
PySpark | How to Rename Column in a Dataframe?
Renaming columns in a PySpark DataFrame is a common task when you’re cleaning, transforming, or organizing data. Whether you’re working with external datasets or need to make your DataFrame more readable, PySpark offers multiple ways to rename columns. In this article, we’ll cover three popular methods to rename columns in PySpark:
1) withColumnRenamed()
2) selectExpr()
3) select() with col()