The Benefits Of Studying PySpark

PySpark Study

PySpark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming clusters with implicit data parallelism and fault tolerance.

Apache Spark requires a cluster manager and a distributed storage system. For cluster management, Spark supports standalone (native Spark clusters, where you can launch a cluster either manually or use the launch scripts provided by the install package.

Design and develop ETL integration patterns using Python on Spark. Develop a framework for converting existing PowerCenter mappings to PySpark (Python and Spark) Jobs.

Roles and Responsibilities

• Hands-on experience writing complex SQL queries, exporting, and importing large amounts of data using utilities.
• Analyze complex, high-volume, high-dimensionality data from the implementation platform and create data tools for analytics.
• Assemble large, complex data sets that meet functional / non-functional business requirements.
• Oversee the design of the data and technical architecture that ensures integrity and accuracy of data. 
• Design, build and maintain all business reporting and dashboard.
• Presenting analytics to management using visualization technologies and data storytelling.
• Perform workflow analysis and documentation to capture and communicate key functionality in preparation for future changes.

The Benefits of Studying PySpark

• In built-memory processing, it helps you increase the speed of processing and completion.  PySpark is an easy-to-learn language. You can learn and implement it easily if you know Python and Apache Spark.
• PySpark is simple to use. It provides parallelized codes that are simple to write.
• Error handling is simple in the PySpark framework. You can easily handle errors and manage synchronization points
• PySpark is a Python API for Apache Spark. It provides great library support. Python has a huge library collection for working in data science and data visualization compared to other languages.
• Many important algorithms are already written and implemented in Spark. It provides many algorithms in Machine Learning or Graphs.
• Technical Skills Acquisition
• Professional Certification
• Jobs Opportunities
• Career Advancement
• Increased Earning Potential

Things You Will Learn

Some of the skills you will acquire in the course of study include:

• PySpark - Introduction
• PySpark - Environment Setup
• PySpark - SparkContext
• PySpark - RDD
• PySpark - Broadcast & Accumulator
• PySpark - SparkConf
• PySpark – SparkFiles
• And lots more.