Spark and Scala Online Course Details

Spark and Scala

Course Fee : 177 USD

Spark and Scala Learners from Hachion: 47

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.

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Course Schedule

Upcoming course schedule will be updated soon

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Choose the best training mode which suits to your requirement

Live online training

Training Fee:

USD 196

10% Discount

USD 177

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

Recorded videos and Trainer support
Topic wise assignments and lab exercises
Resume and certification guidance
Mock interview and live project assistance
Resume marketing and job assistance

Live online training and internship

Recorded videos and Trainer support
Daily assignments and lab exercises
Resume and certification guidance
Project internship and certification
Resume marketing and job assistance

Course Content

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Introduction to Data Analysis with Spark

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 and Getting Started

Downloading Spark
Introduction to Spark’s Python and Scala Shells
Introduction to Core Spark Concepts
Standalone Applications
Initializing a SparkContext
Building

Programming with RDDs

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)

Working with Key/Value Pairs

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

Loading and Saving Your Data

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

Advanced Spark Programming

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

Running on a Cluster

Introduction
Spark Runtime Architecture
The Driver
Executors
Cluster Manager
Launching a Program

Deploying Applications with spark-submit

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?

Tuning and Debugging Spark

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

Spark SQL

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

Spark Streaming

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

Machine Learning with MLlib

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

What is Apache Spark & Scala?

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.

Why learn Apache Spark?

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.

Why should I join Hachion Spark & Scala training program?

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.

What about placement assistance?

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.

What are different modes of training at Hachion.co?

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

Self Placed
Mentorship
Instructor-Led

What are the prerequisites for this course?

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.

what is the average salary for “Scala Spark” professionals?

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.

Who can enroll into this course?

1. Software engineers

2. Data engineers

3. ETL developers

4. Aspiring students

5. Graduates

6. Job seekers