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Pyspark RDDs & Dataframes -Transformations, actions and execution operations- please explain and list them

PySpark offers two fundamental data structures for distributed data processing: Resilient Distributed Datasets (RDDs) and DataFrames. Both provide mechanisms for manipulating and analyzing data, but they differ in structure and operations. Here's a breakdown of...

Are Dataframes in PySpark Lazy evaluated?

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 that...

BDL Ecosystem-HDFS and Hive Tables

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...

Big Data and Big Data Lake – Explain in simple words

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...

PySpark Architecture (Driver- Executor) two ways to describe

PySpark, as part of the Apache Spark ecosystem, follows a master-slave architecture(Or Driver- Executro Architecture) and provides a structured approach to distributed data processing. Here's a breakdown of the PySpark architecture with diagrams to illustrate the key...

Difference between RDD and Dataframes in Pyspark

In Apache Spark, the core data structures for handling and processing data are RDDs (Resilient Distributed Datasets) and DataFrames/Datasets, which are part of the higher-level Spark SQL API. PySpark DataFrames are lazily evaluated. They are implemented on top...

Pyspark -Intro, Components, getting started

PySpark is a powerful Python API for Apache Spark, a distributed computing framework that enables large-scale data processing. It allows you to leverage Spark's capabilities for tasks like: Ingesting and processing massive datasets from various sources like CSV, JSON,...

Window functions in Oracle Pl/Sql and Hive explained and compared with examples

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...

Common Table Expressions in Oracle Pl/Sql and Hive explained and Compared

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...

String/Character Manipulation functions in Oracle PL/SQL, Apache Hive

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 LENGTH('Oracle');6LOWERConverts all characters in a...

Python input function in Detail- interesting usecases

The input() function in Python is primarily used to take input from the user through the command line. While its most common use is to receive text input, it can be used creatively for various purposes. Here are some interesting uses of the input() function along with...

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Python Strings Interview Questions

Python Programming Strings Interview Questions Write a Python program to remove a Specific character from string? Here's a Python program to remove a specific character from a string: def remove_char(text, char): """ Removes a specific character from a string. Args:...

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