Pyspark Intermediate Assesment
This how to create an effective relational database design to use in your IT career or even a personal project is an all-encompassing course designed to impart a deep understanding of RDBMS, one of the most sought-after in the tech industry. This course is tailored for both beginners and existing programmers, focusing on core concepts. It uniquely combines theoretical knowledge with practical exercises, preparing students for advanced areas. By the end of the course, learners will have mastered RDBMS, equipped with the skills to develop robust applications and the confidence to tackle real-world challenges.
Skills for certificate:
Python
Apache Spark
Data Engineering
Azure DataBricks
Problem Solving
Critical Thinking
Pyspark Intermediate Assesment
5ff38238-ebcf-471d-85c0-bc0fbd00f756
Description
This how to create an effective relational database design to use in your IT career or even a personal project is an all-encompassing course designed to impart a deep understanding of RDBMS, one of the most sought-after in the tech industry. This course is tailored for both beginners and existing programmers, focusing on core concepts. It uniquely combines theoretical knowledge with practical exercises, preparing students for advanced areas. By the end of the course, learners will have mastered RDBMS, equipped with the skills to develop robust applications and the confidence to tackle real-world challenges.
Learning Objectives
- Introduction to PySpark and Big Data Processing: Overview of PySpark and its role in big data processing. Understanding the basics of Apache Spark and its ecosystem.Setting up the PySpark environment.
- Working with DataFrames and Spark SQL: Creating and manipulating DataFrames. Performing data transformations and actions.Using Spark SQL for querying data.
- Data Processing with RDDs (Resilient Distributed Datasets): Understanding the RDD abstraction. Creating RDDs and performing transformations and actions. Working with key-value pairs and aggregations.
- Machine Learning with PySpark MLlib: Overview of MLlib and its components.Building and evaluating machine learning models.Working with feature engineering and pipelines.
- Optimizing and Tuning Spark Applications: Understanding Spark job execution and the DAG (Directed Acyclic Graph). Tuning Spark configurations for performance optimization. Best practices for debugging and monitoring Spark applications.
Certificate Issuer
Cognizant