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Price: ₹
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20,000.00 Duration: 20 Hours
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About your PySpark Training

Skills Covered

Big data processing and analysis
Data transformation and aggregation
Real-time data stream processing
Machine learning model development

Tools Covered

Apache Spark
PySpark
Databricks
Apache Kafka (for streaming data)
Apache Flume (integration with streaming data sources)

PySpark Training Syllabus

Curriculum Designed by Experts

Introduction to Apache Spark and PySpark
Topics
  • Overview of big data and distributed computing
  • Spark's architecture and its components
Topics
  • Creating and transforming RDDs
  • Actions and lazy evaluation
Topics
  • Understanding DataFrames in Spark
  • Manipulating data using DataFrame operations
  • Using Spark SQL for structured data processing
Topics
  • Processing real-time data streams
  • Integrating with Kafka, Flume
Topics
  • Overview of MLlib
  • Building and evaluating machine learning models
  • Recommendation systems and classification algorithms
Topics
  • Graph processing with GraphFrames
  • Performance tuning and optimization
  • Deploying PySpark applications

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Why PySpark Training From Xpertised Learning

Live Interactive Classes

  • Top-Tier Trainers
  • Real Time Guidance by Industrial Experts
  • In Class Doubt Clearance

Resource Access

  • Life Time Access to Resource Library

Support

  • One-to-One Lab and Training Assistance
  • Help Desk Support
  • Doubt Clearance in Real-time

Project Based Learning

  • Labs and Learning Based from Industrial Experts
  • Learning Based on Project Scenarios

Certification

  • Xpertised Learning Training Certificate
  • Certificate of Completion

Labs

  • Cloud based Labs *Labs based on course requirement only

PySpark Training Details

This course offers comprehensive training on using PySpark to perform data analysis, build machine learning models, and handle large datasets. It explores the integration of Python and Spark and how to use them together to perform complex data analysis tasks.

  • Gain proficiency in PySpark, enhancing your ability to handle big data processing and analytics using Apache Spark with Python.
  • Boost your career opportunities by mastering PySpark, making you a valuable asset for roles in data engineering and analytics.
  • Stay competitive in the tech industry by acquiring advanced PySpark skills, ensuring your ability to tackle complex big data challenges.
  • Mastery of data processing, analysis, and machine learning in a distributed environment.
  • Improved career prospects in fields such as data science, big data analysis, and machine learning.
  • Practical knowledge applicable to real-world data processing challenges.
  • Big data processing and analysis
  • Data transformation and aggregation
  • Real-time data stream processing
  • Machine learning model development
  • Apache Spark
  • PySpark
  • Databricks
  • Apache Kafka (for streaming data)
  • Apache Flume (integration with streaming data sources)
  • To enable participants to use PySpark for data processing and analysis tasks efficiently.
  • To teach how to apply machine learning algorithms using PySpark.
  • To provide hands-on experience with real-world data sets and scenarios.
  • Data Scientists and Data Analysts looking to scale their data processing capabilities.
  • Software Engineers and Developers who want to transition into big data roles.
  • Students and researchers in fields that involve large scale data analysis.
  • Basic knowledge of Python programming.
  • Understanding of basic data processing concepts.
  • Familiarity with SQL and database concepts is helpful but not mandatory.
  • Comprehensive skills in handling big data using PySpark.
  • Techniques for data cleaning, transformation, and aggregation.
  • Building and deploying machine learning models with large datasets.
  • A computer with adequate RAM (at least 8GB recommended) and processing power to handle data-intensive operations.
  • Internet connection for accessing cloud-based Spark environments like Databricks or accessing datasets.
  • Python installed, with support for libraries used in data analysis and machine learning.
  • Careers in data science, big data analysis, and machine learning engineering.
  • Roles in industries that rely on large scale data analytics like finance, e-commerce, and telecommunications.

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