August 2024

  • Pyspark -Introduction, Components, Compared With Hadoop, PySpark Architecture- (Driver- Executor)

    PySpark is a powerful Python API for Apache Spark, a distributed computing framework that enables large-scale data processing. Spark History Spark was initially started by Matei Zaharia at UC Berkeley’s AMPLab in 2009, and open sourced in 2010 under a BSD license. In 2013, the project was donated to the Apache Software Foundation and switched…

  • Deploying a PySpark job- Explain Various Methods and Processes Involved

    Deploying a PySpark job can be done in various ways depending on your infrastructure, use case, and scheduling needs. Below are the different deployment methods available, including details on how to use them: 1. Running PySpark Jobs via PySpark Shell How it Works: Steps to Deploy: Use Cases: 2. Submitting Jobs via spark-submit How it…

  • What is Hive?

    Hive a Data warehouse infra Hive is an open-source data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. It allows users to query and manage large datasets residing in distributed storage using a SQL-like language called HiveQL. Here’s an overview of Hive: Features of Hive: Components of Hive: Use…

  • Pyspark- DAG Schedular, Jobs , Stages and Tasks explained

    In PySpark, jobs, stages, and tasks are fundamental concepts that define how Spark executes distributed data processing tasks across a cluster. Understanding these concepts will help you optimize your Spark jobs and debug issues more effectively. At First Let us go through DAG Scheduler in Spark, we might be repetiting things but it is very…

  • Apache Spark- Partitioning and Shuffling, Parallelism Level, How to optimize these

    Apache Spark is a powerful distributed computing system that handles large-scale data processing through a framework based on Resilient Distributed Datasets (RDDs). Understanding how Spark partitions data and distributes it via shuffling or other operations is crucial for optimizing performance. Here’s a detailed explanation: Partitions in Spark Partitioning is the process of dividing data into…

  • Discuss Spark Data Types, Spark Schemas- How Sparks infers Schema?

    In Apache Spark, data types are essential for defining the schema of your data and ensuring that data operations are performed correctly. Spark has its own set of data types that you use to specify the structure of DataFrames and RDDs. Understanding and using Spark’s data types effectively ensures that your data processing tasks are…

  • Sorting Algorithms implemented in Python- Merge Sort, Bubble Sort, Quick Sort

    Merge sort is a classic divide-and-conquer algorithm that efficiently sorts a list or array by dividing it into smaller sublists, sorting those sublists, and then merging them back together. Here’s a step-by-step explanation of how merge sort works, along with an example: How Merge Sort Works Detailed Steps Example Let’s sort the list [38, 27,…

  • Mysql or Pyspark SQL query- The placement of subqueries

    Let’s list all possible places where subqueries in MySQL or Hive QL or Pyspark SQL Query can be used: 1. In the SELECT Clause Subqueries can compute a value for each row. 2. In the FROM Clause Subqueries can be used as derived tables. 3. In the WHERE Clause Subqueries can filter rows based on…