Big Data Analysis with Scala and Spark Training

Live Online & Classroom Enterprise Training

Big Data Analysis with Scala and Spark focuses on processing and analyzing large datasets using Apache Spark with Scala. It covers data transformations, distributed computing, and building scalable data pipelines for analytics.

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What is Big Data Analysis with Scala and Spark Training about?

This course focuses on building a solid foundation in big data analytics using Apache Spark and Scala. Learners will explore how to process structured and unstructured data, perform distributed computations, and apply advanced analytics and machine learning techniques on large-scale datasets. The course covers Spark Core, Spark SQL, and Spark MLlib with Scala to implement scalable real-world big data solutions. By the end of the course, participants will be able to design and deploy efficient data pipelines for business and research applications.

What are the objectives of Big Data Analysis with Scala and Spark Training ?

Understand big data concepts and Spark’s distributed architecture.
Use Scala to build Spark applications for data transformation and analysis.
Work with Spark SQL and DataFrames for structured data processing.
Apply Spark MLlib for predictive modeling and machine learning.
Implement end-to-end big data pipelines for real-world applications.

Who is Big Data Analysis with Scala and Spark Training for?

Data engineers and big data developers.
Data scientists working with large datasets.
Software engineers interested in distributed computing.
Business analysts exploring big data solutions.
Students and professionals pursuing careers in big data and AI.

What are the prerequisites for Big Data Analysis with Scala and Spark Training?

Prerequisites:

Basic knowledge of programming (Scala, Java, or Python preferred).
Understanding of SQL and databases.
Familiarity with Linux command line and scripting.
Knowledge of data structures and algorithms.
Optional: Prior exposure to Hadoop or distributed systems.

Learning Path:

Introduction to big data and Apache Spark ecosystem.
Scala programming essentials for Spark.
Spark Core and DataFrames for large-scale data analysis.
Spark SQL and MLlib for advanced analytics.
Building and deploying real-world big data projects.

Related Courses:

Apache Spark and Scala
Big Data Analytics with Hadoop
Machine Learning with Spark MLlib
Data Engineering with PySpark

Available Training Modes

Live Online Training

5 Days

Course Outline Expand All

Expand All

Module 1- Spark Basics

From parallel to distributed

Latency

RDDs, Spark’s distributed collection

RDDs: Transformations and actions

Evaluation in Spark: Unlike scala collections

Cluster Topology matters

Module 2- Reduction Operations and Distributed Key-Value Pairs

Reduction operations

Pair RDDs

Transformations and actions on pair RDDs

Joins

Module 3- Partitioning and Shuffling

Shuffling: What it is and why it’s important

Partitioning

Optimizing with partitioners

Wide vs narrow dependencies

Module 4- Structured data: SQL, Dataframes, and Datasets

Structured vs unstructured data

Spark SQL

DataFrames

Datasets

Who is the instructor for this training?

The trainer for this Big Data Analysis with Scala and Spark Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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