Pyspark Training

Apart from our Flagship course Advanced diploma in Bigdata and workshops we also have course in Java, python, SQL, PySpark, postgreSQL etc, all are instructor led online courses.

Get Trained by Exprienced consultants with multiple decades of experience, Our rates staring as low as 10 USD /hour.
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skype: constantinestanley
whatsapp: +91 9895942514

Basic Course

Course Overview:

This introductory course provides a solid foundation in PySpark, a Python library for working with Apache Spark. Students will learn the fundamental concepts of distributed computing, data processing, and how to use PySpark to perform common data analysis tasks.

Key Topics:

Introduction to PySpark: Apache Spark overview, PySpark installation, and setup
Resilient Distributed Datasets (RDDs): Creating, transforming, and persisting RDDs
Spark SQL: Using SQL queries with PySpark
DataFrames and DataSets: Working with structured data
Spark MLlib: Machine learning algorithms in PySpark
Spark Streaming: Processing real-time data streams

Intermediate Course

Course Overview:

Building upon the foundation of the basic course, this course delves deeper into PySpark concepts and introduces advanced topics such as performance optimization, distributed caching, and integration with other tools.

Key Topics:

Performance optimization: Understanding performance bottlenecks and optimization techniques
Distributed caching: Using Spark's caching mechanisms for efficient data access
Integration with other tools: Connecting PySpark with Hadoop, Hive, and other big data technologies
Advanced RDD operations: Key-value pairs, aggregations, and joins
Spark SQL advanced features: Window functions, subqueries, and custom UDFs
Spark MLlib advanced algorithms: Classification, regression, clustering, and recommendation

Advanced Course

Course Overview:

This advanced course targets experienced PySpark developers who want to explore cutting-edge topics and best practices. It covers topics like big data pipelines, machine learning pipelines, and cloud-based deployments.

Key Topics:

Big data pipelines: Building and managing end-to-end data pipelines with PySpark
Machine learning pipelines: Creating and deploying machine learning models using PySpark
Cloud-based deployments: Running PySpark applications on cloud platforms like AWS, Azure, and GCP
Advanced Spark features: GraphX, Spark Streaming advanced topics
Case studies: Real-world examples of PySpark applications in various domains