by Team AHT | Jul 21, 2024 | Python
I believe you read our Post https://www.hintstoday.com/i-did-python-coding-or-i-wrote-a-python-script-and-got-it-exected-so-what-it-means/. Before starting here kindly go through the Link. How the Python interpreter reads and processes a Python script The Python... by Team AHT | Jul 16, 2024 | Pyspark
Adaptive Query Execution (AQE) in Apache Spark 3.0 is a powerful feature that brings more intelligent and dynamic optimizations to Spark SQL on runtime statistics. By adapting the execution plan at runtime based on actual data statistics, AQE can provide significant... by Team AHT | Jul 15, 2024 | AI & ML
Training for Generative AI is an exciting journey that combines knowledge in programming, machine learning, and deep learning. Since you have a basic understanding of Python, you are already on the right track. Here’s a suggested learning path to help you progress: 1.... by Team AHT | Jul 12, 2024 | Python
Linked lists are a fundamental linear data structure where elements (nodes) are not stored contiguously in memory. Each node contains data and a reference (pointer) to the next node in the list, forming a chain-like structure. This dynamic allocation offers advantages... by Team AHT | Jul 10, 2024 | Python
In Python, classes and objects are the fundamental building blocks of object-oriented programming (OOP). A class defines a blueprint for objects, and objects are instances of a class. Here’s a detailed explanation along with examples to illustrate the concepts...
by Team AHT | Jul 9, 2024 | Python
It is a case sensitive, non-mutable sequence of characters marked under quotation. It can contain alphabets, digits, white spaces and special characters. In Python, a string is a sequence of characters enclosed within either single quotes (‘ ‘), double... by Team AHT | Jul 9, 2024 | Tutorials
Regular expressions (regex) are a powerful tool for matching patterns in text. Python’s re module provides functions and tools for working with regular expressions. Here’s a complete tutorial on using regex in Python. 1. Importing the re Module To use... by Team AHT | Jul 7, 2024 | Pyspark
Let us create One or Multiple dynamic lists of variables and save it in dictionary or Array or other datastructure for further repeating use in Pyspark projects specially for ETL jobs. Variable names are in form of dynamic names for example Month_202401 to... by Team AHT | Jul 7, 2024 | Pyspark
Error handling, debugging, and generating custom log tables and status tables are crucial aspects of developing robust PySpark applications. Here’s how you can implement these features in PySpark: 1. Error Handling in PySpark PySpark provides mechanisms to handle... by Team AHT | Jul 7, 2024 | Pyspark
Here is a detailed approach for dividing a monthly PySpark script into multiple code steps. Each step will be saved in the code column of a control DataFrame and executed sequentially. The script will include error handling and pre-checks to ensure source tables are... by Team AHT | Jul 7, 2024 | Pyspark
String manipulation is a common task in data processing. PySpark provides a variety of built-in functions for manipulating string columns in DataFrames. Below, we explore some of the most useful string manipulation functions and demonstrate how to use them with... by Team AHT | Jul 6, 2024 | Pyspark
Here’s a comprehensive list of some common PySpark date functions along with detailed explanations and examples on Dataframes(We will again discuss thess basis Pyspark sql Queries): 1. current_date() Returns the current date. from pyspark.sql.functions import... by Team AHT | Jul 3, 2024 | Pyspark
Window functions in PySpark allow you to perform operations on a subset of your data using a “window” that defines a range of rows. These functions are similar to SQL window functions and are useful for tasks like ranking, cumulative sums, and moving... by Team AHT | Jul 2, 2024 | Pyspark
PySpark provides a powerful API for data manipulation, similar to pandas, but optimized for big data processing. Below is a comprehensive overview of DataFrame operations, functions, and syntax in PySpark with examples. Creating DataFrames Creating DataFrames from... by Team AHT | Jul 1, 2024 | Pyspark
In PySpark, you can perform operations on DataFrames using two main APIs: the DataFrame API and the Spark SQL API. Both are powerful and can be used interchangeably to some extent. Here’s a breakdown of key concepts and functionalities: 1. Creating DataFrames:... by Team AHT | Jun 30, 2024 | Pyspark, Python
While searching for A free Pandas Project on Google Found this link -Exploratory Data Analysis (EDA) with Pandas in Banking . I have tried to convert this Pyscript in Pyspark one. First, let’s handle the initial steps of downloading and extracting the data: #... by Team AHT | Jun 30, 2024 | Pyspark
Project Alert:- Building a ETL Data pipeline in Pyspark and using Pandas and Matplotlib for Further Processing. For Deployment we will consider using Bitbucket and Genkins. We will build a Data pipeline from BDL Reading Hive Tables in Pyspark and executing Pyspark... by Team AHT | Jun 29, 2024 | Python
Let us go through the Project requirement:- 1.Let us create One or Multiple dynamic lists of variables and save it in dictionary or Array or other datastructre for further repeating use in python. Variable names are in form of dynamic names for example Month_202401 to... by Team AHT | Jun 29, 2024 | Python
I wrote a Python code or I created a Python script, and it executed successfully So what does it Mean? This will be the most basic question a Early Python Learner can ask ! So Consider this scenario- where i executed a script in python which saves a many csv in Local... by Team AHT | Jun 26, 2024 | SQL
Spark SQL supports several types of joins, each suited to different use cases. Below is a detailed explanation of each join type, including syntax examples and comparisons. Types of Joins in Spark SQL Inner Join Left (Outer) Join Right (Outer) Join Full (Outer) Join... by Team AHT | Jun 26, 2024 | SQL
Temporary functions allow users to define functions that are session-specific and used to encapsulate reusable logic within a database session. While both PL/SQL and Spark SQL support the concept of user-defined functions, their implementation and usage differ... by Team AHT | Jun 26, 2024 | SQL
For Better understanding on Spark SQL windows Function and Best Usecases do refer our post Window functions in Oracle Pl/Sql and Hive explained and compared with examples. Window functions in Spark SQL are powerful tools that allow you to perform calculations across a... by Team AHT | Jun 23, 2024 | Pyspark
explain a typical Pyspark execution Logs A typical PySpark execution log provides detailed information about the various stages and tasks of a Spark job. These logs are essential for debugging and optimizing Spark applications. Here’s a step-by-step explanation of... by Team AHT | Jun 16, 2024 | Pyspark
RDD (Resilient Distributed Dataset) is the fundamental data structure in Apache Spark. It is an immutable, distributed collection of objects that can be processed in parallel across a cluster of machines. Purpose of RDD Distributed Data Handling: RDDs are designed to... by Team AHT | Jun 16, 2024 | Pyspark
Yes, DataFrames in PySpark are lazily evaluated, similar to RDDs. Lazy evaluation is a key feature of Spark’s processing model, which helps optimize the execution of transformations and actions on large datasets. What is Lazy Evaluation? Lazy evaluation means...
by Team AHT | Jun 15, 2024 | Pyspark
Big Data Lake: Data Storage HDFS is a scalable storage solution designed to handle massive datasets across clusters of machines. Hive tables provide a structured approach for querying and analyzing data stored in HDFS. Understanding how these components work together... by Team AHT | Jun 15, 2024 | Pyspark
Big data and big data lakes are complementary concepts. Big data refers to the characteristics of the data itself, while a big data lake provides a storage solution for that data. Organizations often leverage big data lakes to store and manage their big data, enabling... by Team AHT | Jun 6, 2024 | SQL
Window functions, also known as analytic functions, perform calculations across a set of table rows that are somehow related to the current row. This is different from regular aggregate functions, which aggregate results for the entire set of rows. Both Oracle PL/SQL... by Team AHT | Jun 6, 2024 | SQL
Common Table Expressions (CTEs) are a useful feature in SQL for simplifying complex queries and improving readability. Both Oracle PL/SQL and Apache Hive support CTEs, although there may be slight differences in their syntax and usage. Common Table Expressions in... by Team AHT | Jun 5, 2024 | SQL
Function NameDescriptionExample UsageResultCONCATConcatenates two strings.SELECT CONCAT(‘Oracle’, ‘PL/SQL’) FROM dual;OraclePL/SQL“ (Concatenation)Concatenates two strings.LENGTHReturns the length of a string.SELECT...
