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Spark and Scala

Course Description

Apache Spark is a powerful open-source monitoring and distributed general-purpose in-memory computing engine which is written in the Scala programming language. Apache Spark and Scala provide fault-tolerant in-memory data parallelism at a very fast, standard interface and ease of use. Most of the big data developers choosing Scala programming language in the Apache Spark framework to build big data applications with faster data analysis.

Scope of Apache Spark and Scala is immense because of its demandable key features like less complexity, fast memory computations, dynamic programming, inbuilt machine learning libraries, data streaming, real-time analysis,  graphs libraries, high productivity, and performance. 

Hachion Apache Spark and Scala Online training curated by industry experts from scratch. Our Spark and scala tutorial covers all basic fundamental concepts to updated advanced topics in the course content.  Enhance full knowledge with the course that includes Scala programming language, Spark architecture, RDD for creating apps in Apache Spark, SparkSQL, streaming, batch processing, ML programming, and graph analytics. Master your skills and get hands-on experience with given real-time projects to become a big data developer.

Course Fee : 198 USD

Spark and Scala Learners from Hachion: 47
Course Schedule

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Choose the best training mode which suits to your requirement
Live online training

USD 198

Training Fee: USD 220 10% Discount

  • Live interactive online training
  • Daily Assignments and Lab exercises
  • Resume and certification guidance
  • Mock interview and live project assistance
  • Resume marketing and job assistance
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Mentoring mode training
  • Live interactive online training
  • Daily Assignments and Lab exercises
  • Resume and certification guidance
  • Mock interview and live project assistance
  • Resume marketing and job assistance
Live online training and internship
  • Live interactive online training
  • Daily Assignments and Lab exercises
  • Resume and certification guidance
  • Mock interview and live project assistance
  • Resume marketing and job assistance

Course Content

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  • What Is Apache Spark?

  • A Unified Stack

  • Spark Core

  • Spark SQL

  • Spark Streaming

  • MLlib

  • GraphX

  • Cluster Managers

  • Who Uses Spark, and for What?

  • Data Science Tasks

  • Data Processing Applications

  • A Brief History of Spark

  • Spark Versions and Releases

  • Storage Layers for Spark

  • Downloading Spark

  • Introduction to Spark’s Python and Scala Shells

  • Introduction to Core Spark Concepts

  • Standalone Applications

  • Initializing a SparkContext

  • Building

  • Standalone Applications

  • RDD Basics

  • Creating RDDs

  • RDD Operations

  • Transformations

  • Actions

  • Lazy Evaluation

  • Passing Functions to Spark

  • Common Transformations and Actions

  • Basic RDDs

  • Converting Between RDD Types

  • Persistence (Caching)

  • Motivation

  • Creating Pair RDDs

  • Transformations on Pair RDDs

  • Aggregations

  • Grouping Data

  • Joins

  • Sorting Data

  • Actions Available on Pair RDDs

  • Data Partitioning (Advanced)

  • Determining an RDD’s Partitioner

  • Operations That Benefit from Partitioning

  • Operations That Affect Partitioning

  • Example: PageRank

  • Custom Partitioners

  • Motivation

  • File Formats

  • Text Files

  • JSON

  • Comma-Separated Values and Tab-Separated Values

  • SequenceFiles

  • Object Files

  • Hadoop Input and Output Formats

  • File Compression

  • Filesystems

  • Local/“Regular” FS

  • Amazon S

  • HDFS

  • Structured Data with Spark SQL

  • Apache Hive

  • JSON

  • Databases

  • Java Database Connectivity

  • Cassandra

  • HBase

  • Elasticsearch

  • Introduction

  • Accumulators

  • Accumulators and Fault Tolerance

  • Custom Accumulators

  • Broadcast Variables

  • Optimizing Broadcasts

  • Working on a Per-Partition Basis

  • Piping to External Programs

  • Numeric RDD Operations

  • Introduction

  • Spark Runtime Architecture

  • The Driver

  • Executors

  • Cluster Manager

  • Launching a Program

  • Packaging Your Code and Dependencies

  • A Java Spark Application Built with Maven

  • A Scala Spark Application Built with sbt

  • Dependency Conflicts

  • Scheduling Within and Between Spark Applications

  • Cluster Managers

  • Standalone Cluster Manager

  • Hadoop YARN

  • Apache Mesos

  • Amazon EC

  • Which Cluster Manager to Use?

  • Configuring Spark with SparkConf

  • Components of Execution: Jobs, Tasks, and Stages

  • Finding Information

  • Spark Web UI

  • Driver and Executor Logs

  • Key Performance Considerations

  • Level of Parallelism

  • Serialization Format

  • Memory Management

  • Hardware Provisioning

  • Linking with Spark SQL

  • Using Spark SQL in Applications

  • Initializing Spark SQL

  • Basic Query Example

  • SchemaRDDs

  • Caching

  • Loading and Saving Data

  • Apache Hive

  • Parquet

  • JSON

  • From RDDs

  • JDBC/ODBC Server

  • Working with Beeline

  • Long-Lived Tables and Queries

  • User-Defined Functions

  • Spark SQL UDFs

  • Hive UDFs

  • Spark SQL Performance

  • Performance Tuning Options

  • A Simple Example

  • Architecture and Abstraction

  • Transformations

  • Stateless Transformations

  • Stateful Transformations

  • Output Operations

  • Input Sources

  • Core Sources

  • Additional Sources

  • Multiple Sources and Cluster Sizing / Operation

  • Checkpointing

  • Driver Fault Tolerance

  • Worker Fault Tolerance

  • Receiver Fault Tolerance

  • Processing Guarantees

  • Streaming UI

  • Performance Considerations

  • Batch and Window Sizes

  • Level of Parallelism

  • Garbage Collection and Memory Usage

  • Overview

  • System Requirements

  • Machine Learning Basics

  • Example: Spam Classification

  • Data Types

  • Working with Vectors

  • Algorithms

  • Feature Extraction

  • Statistics

  • Classification and Regression

  • Clustering

  • Collaborative Filtering and Recommendation

  • Dimensionality Reduction

  • Model Evaluation

  • Tips and Performance Considerations

  • Preparing Features

  • Configuring Algorithms

  • Table of Contents | vii

  • Caching RDDs to Reuse

  • Recognizing Sparsity

  • Level of Parallelism

  • Pipeline API

Spark and Scala Training FAQs

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Apache Spark is an open-source cluster computing framework that was initially developed at UC Berkeley in the AMPLab.As compared to the disk-based, two-stage MapReduce of Hadoop, Spark provides up to 100 times faster performance for a few applications with in-memory primitives. Scala is a modern and multi-paradigm programming language. It has been designed for expressing general programming patterns in an elegant, precise, and type-safe way. One of the prime features is that it integrates the features of both object-oriented and functional languages smoothly.

Although there are many spark classroom training Portals. Hachion is one of the best Spark classroom training Institutes, with the expert instructors who provide great support at the time of learning. As the course, is all about learning to manage and process volumes of data, it is recommended for those who work upon managing big data. Also, for the matter of fact that, Apache spark technology is the foundation to understand the architecture and functionalities of software technology like big data.

Hachion's Spark & Scala training program covers from basic to advanced concepts in Spark and Scala. Our course curriculum is designed by industry experts in the IT industry. We provide 100% job assistance with certification guidance.

We provide 100% job assistance to the Hachion students, once they complete the course. We also provide resume writing, mock interviews and resume marketing services as part of our job assistance program. 

We offer three modes of training in the Spark and Scala online training program.

  • Self Placed
  • Mentorship
  • Instructor-Led

The basic prerequisite of the Apache Spark and Scala Tutorial is a fundamental knowledge of any programming language is a prerequisite for the tutorial. Participants are expected to have a basic understanding of any database, SQL, and query language for databases. Working knowledge of Linux or Unix based systems, while not mandatory, is an added advantage for this tutorial.

The average salary for "big data scala spark" ranges from approximately $92,445 per year for Developer to $138,252 per year for Machine Learning Engineer.

1. Software engineers

2. Data engineers

3. ETL developers

4. Aspiring students

5. Graduates

6. Job seekers

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