Free Spark Certification Course – Learn Spark with Real-time Projects

Free Spark Certification Course – Learn Spark with Real-time Projects

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Free Apache Spark and Scala Course offers a perfect blend of in-depth theoretical knowledge and strong practical skills via implementation of real-life Spark projects to give you a headstart and enable you to bag top Big Data Spark jobs in the industry.
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What will you take home from this Free Apache Spark Course?

  • 10+ hrs of self-paced course
  • 170+ hrs of study material, practicals, quizzes
  • Acquire practical knowledge which industry needs
  • Practical course with real-time case-studies
  • Lifetime access with industry renowned certification
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Why should you enroll in this Free Apache Spark Course?

  • Learn how Spark solves these Big Data challenges
  • Grasp concepts of Scala and implement them
  • Become adept in Apache Spark and Spark installation
  • Understand the Apache Spark architecture
  • Play with Spark RDDs – Transformation, Action, Load
  • Learn to handle in-memory data efficiently
  • Develop complex real-time Apache Spark applications
  • Master the concepts of Spark stream analytics and learn streaming APIs
  • Learn MLlib APIs in Spark for machine learning algorithms
  • Learn Spark GraphX APIs to implement graph algorithms
  • Work on live Spark project to get hands-on experience

Spark Course Objectives

Participants are first introduced to the fundamental ideas of Spark, including its architecture, parts, and programming approach. Participants gain knowledge of Resilient Distributed Datasets (RDDs), the fundamental data structures of Spark, as well as the numerous Spark operations that may be used to modify and change data. Additionally, they learn about the memory management and optimization strategies used by Spark, which enables them to create scalable and effective Spark applications.

Participants explore more complex subjects as the course goes on, such as Spark’s DataFrame and Dataset APIs, which provide higher-level abstractions for working with structured data. They investigate Spark SQL, which uses SQL-like syntax to query structured data, and discover Spark’s machine learning library (MLlib), which enables the creation and deployment of machine learning models. This Spark course also covers GraphX’s graph processing capabilities and Spark Streaming for analyzing real-time data.

Participants apply their knowledge to situations from the real world during a sizable chunk of the course that is devoted to practical projects. They engage in activities that need them to import, analyse, and analyze huge datasets using Spark. They get competence in Spark development thanks to this practical experience. Participants also gain knowledge of performance tuning techniques, such as data partitioning, caching, and optimization methods, to guarantee the smooth operation of their Spark applications.

Data engineers, data analysts, and developers looking to improve their big data processing abilities should take the Spark course. It offers practical activities and hands-on training to make sure attendees can use Spark to tackle real-world data difficulties. This Spark course’s curriculum covers a variety of Spark topics, such as machine learning, stream processing in real-time, and batch processing. Participants will gain knowledge about how to build up Spark clusters, create effective data processing pipelines, and speed up Spark processes.

This Spark course’s lecturers are skilled experts with in-depth understanding of big data technologies and their application in the real world. Participants will learn how to utilise Spark to handle challenging data analysis jobs during the course and receive insightful knowledge on best practises for large data processing.

The major goal of the Spark course is to give learners the information and abilities needed to fully utilise Apache Spark’s capabilities for large data processing and analytics. The goal of this course is to provide students a thorough grasp of Spark’s architecture, parts, and capabilities. Students will be able to after finishing the course:

  • Know the core ideas behind large data processing and distributed computing.
  • Use the fundamental APIs of Spark to convert, filter, and manipulate massive amounts of data.
  • Resilient Distributed Datasets (RDDs) from Spark should be used for concurrent, fault-tolerant processing.
  • Investigate the high-level Spark APIs for structured data processing, such as DataFrame and Dataset.
  • Utilise the scalable machine learning models that Spark’s MLlib (machine learning library) offers.
  • Process real-time data streams with Spark Streaming.

Spark and Scala Course Curriculum

Features of Spark Online Course

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