Spark Fundamentals Training

Live Online & Classroom Enterprise Training

Spark Fundamentals covers the core concepts of Apache Spark, including its in-memory computing, distributed data processing, and resilience. It introduces Spark’s architecture, RDDs, DataFrames, and key libraries for big data analytics.

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What is Spark Fundamentals Training about?

The "Spark Fundamentals" course provides a comprehensive introduction to Apache Spark, a leading open-source big data processing framework. This course covers Spark’s core components, including Spark SQL, Spark Streaming, and machine learning capabilities. Participants will learn how to process, analyze, and manage large datasets efficiently using Spark’s distributed computing architecture. Hands-on exercises ensure learners gain practical experience working with Spark in real-world scenarios.

What are the objectives of Spark Fundamentals Training ?

Understand Spark Architecture: Learn about Spark’s cluster-computing architecture and its key components.
Process Big Data with Spark: Gain the skills to process large datasets using Spark RDDs (Resilient Distributed Datasets) and DataFrames.
Master Spark SQL: Use Spark SQL for querying structured data efficiently.
Work with Streaming Data: Analyze and process real-time data streams with Spark Streaming.
Introduction to Machine Learning: Apply Spark’s MLlib for machine learning tasks like classification and regression.

Who is Spark Fundamentals Training for?

Data Engineers: Professionals responsible for building and maintaining data pipelines.
Data Analysts: Analysts looking to process and analyze big data efficiently.
Developers: Software developers interested in distributed data processing frameworks.
IT Professionals: Professionals transitioning to big data technologies.
Students and Enthusiasts: Beginners interested in learning Spark for data processing and analytics.

What are the prerequisites for Spark Fundamentals Training?

Basic Programming Knowledge: Familiarity with programming languages such as Python, Java, or Scala.
Understanding of Big Data Concepts (Optional): Knowledge of big data processing frameworks like Hadoop is beneficial.
Experience with Data Processing (Optional): Some experience with data analytics or data engineering tools.

Available Training Modes

Live Online Training

2 Days

Self-Paced Training

10 Hours

Course Outline Expand All

Expand All

Module 1: Introduction to Apache Spark

Overview of big data and distributed computing

Introduction to Apache Spark and its ecosystem

Key features and advantages of Spark

Module 2: Spark Architecture and Components

Spark core architecture: Driver, executors, and cluster manager

Resilient Distributed Datasets (RDDs) and transformations/actions

Spark deployment modes (Standalone, YARN, Mesos)

Module 3: Working with Spark RDDs

Creating and manipulating RDDs

Key-value pair RDDs and common operations

Optimizing RDD performance

Module 4: Spark DataFrames and Spark SQL

Introduction to DataFrames and their advantages over RDDs

Querying structured data with Spark SQL

Schema inference and working with structured datasets

Module 5: Real-Time Data Processing with Spark Streaming

Introduction to Spark Streaming and DStreams

Processing streaming data from sources like Kafka and socket streams

Windowing, stateful processing, and fault tolerance

Module 6: Machine Learning with Spark MLlib

Overview of MLlib and its capabilities

Performing basic machine learning tasks: classification, regression, clustering

Building and evaluating machine learning models

Module 7: Advanced Spark Concepts (Optional)

Working with GraphFrames for graph analytics

Integrating Spark with other big data tools like Hadoop and Cassandra

Optimizing and tuning Spark applications

Module 8: Hands-On Projects and Use Cases

End-to-end big data pipeline implementation with Spark

Real-world use cases: log analysis, recommendation systems, and more

Who is the instructor for this training?

The trainer for this Spark Fundamentals Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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