In this PySpark Training course, there are no formal prerequisites.
This PySpark Training provided by The Knowledge Academy is ideal for anyone who wants to learn the use of PySpark to support the collaboration of Apache Spark and Python.
PySpark Training Course Overview
PySpark is an interface for Apache Spark in Python and a comprehensive language for conducting exploratory data analysis at scale, for creating machine learning pipelines and building ETLs for a data platform. PySpark supports various features of Spark like Spark SQL, DataFrame, Streaming, MLlib, and Spark Core. It comes with immense benefits to its users and organisations, including simple to write, the framework handles errors, various useful algorithms, etc. This PySpark Training is curated by industry experts to help individuals in mastering skills required by utilising PySpark features in their day-to-day tasks and get opportunities to work on lucrative job posts in multinational companies.
In this 1-day PySpark Training course, delegates will learn about using the Conda environment to export their third-party Python packages by leveraging Conda-pack. They will gain in-depth knowledge about using virtualenv to manage Python dependencies in their clusters by using venv-pack. Further, delegates will learn other crucial concepts, such as reading, writing, and transforming data, MLlib, using PyPI, Conda, PySpark native features, Virtualenv, and PEX, connecting to network servers, etc. Our expert and technically sound trainer, who has years of experience in teaching technical courses, will conduct this training.
This training course will cover various essential concepts, such as:
- Spark data frames
- Setting up a Spark virtual environment
- Building batch and streaming apps with Spark
- Exploring the GitHub world
- Learning from data using Spark
- Contextualising Spark MLlib in the app architecture
After attending this training course, delegates will be able to use conceptual frameworks for implementing the architecture of data-intensive applications in their organisations. They will also be able to harvest the data, ensuring its integrity and preparing for batch and streaming data processing by Spark.