Apache Spark with Scala Online Training

Apache Spark with Scala online training is created to help you to master, with Scala and the Spark Ecosystem, which includes RDD, SQL, and MLlib.

Also, this Apache Spark training is live, instructor-led & helps you master key Apache Spark concepts, with hands-on demonstrations.

This Apache course is fully immersive where you can learn and interact with the instructor and your peers.

This certification training course is ideal for professionals aspiring for a career in the field of real-time big data analytics, analytics professionals, research professionals, IT developers and testers, data scientists, BI and reporting professionals, and students who want to gain a thorough understanding of Apache Spark.

Those wishing to take the Apache Spark certification training course should have a fundamental knowledge of any programming language and a basic understanding of any database, SQL, and query language for databases.
Working knowledge of Linux- or Unix-based systems is also beneficial.
  • Target audience
    • Students
    • Big Data and Hadoop Professionals
    • IT professionals

    Prerequisites

    Professionals with experience in IT and Students eager to learn can undergo this training.

    Why Should I choose Lara Online Training?

    • We provide Very in-depth course material with Real Time Scenarios for each topic with its Solutions for, Apache with Scala Online Training
    •  At Lara, we provide case studies in real-time applications with a professional explanation.
    •  We do Schedule the sessions based upon your comfort by our Highly Qualified Trainers and Real-time Experts.
    •  Class recordings are available immediately after the session for future reference.
    •  We do Normal Track, Fast Track, and Weekend Batches for Apache Spark with Scala Online Training.
    •  We assist you with cost Effective and Flexible Payment Schemes.
    •  At Lara, we provide Placement Assistance.
    •  We provide Assessment and Mock Interviews

Introduction to Big Data Hadoop and Spark

1
What is Big Data?
2
Big Data Customer Scenarios
3
Limitations and Solutions of Existing Data Analytics Architecture with Uber Use Case
4
How Hadoop Solves the Big Data Problem?
5
What is Hadoop?
6
Hadoop’s Key Characteristics
7
Hadoop Ecosystem and HDFS
8
Hadoop Core Components
9
Rack Awareness and Block Replication
10
YARN and its Advantage
11
Hadoop Cluster and its Architecture
12
Hadoop: Different Cluster Modes
13
Hadoop Terminal Commands
14
Big Data Analytics with Batch & Real-time Processing
15
Why Spark is needed?
16
What is Spark?
17
How Spark differs from other frameworks?
18
Spark at Yahoo!

Introduction to Scala for Apache Spark

1
What is Scala?
2
Why Scala for Spark?
3
Scala in other Frameworks
4
Introduction to Scala REPL
5
Basic Scala Operations
6
Variable Types in Scala
7
Control Structures in Scala
8
Foreach loop, Functions and Procedures
9
Collections in Scala- Array
10
ArrayBuffer, Map, Tuples, Lists, and more

Functional Programming and OOPs Concepts in Scala

1
Functional Programming
2
Higher Order Functions
3
Anonymous Functions
4
Class in Scala
5
Getters and Setters
6
Custom Getters and Setters
7
Properties with only Getters
8
Auxiliary Constructor and Primary Constructor
9
Singletons
10
Extending a Class
11
Overriding Methods
12
Traits as Interfaces and Layered Traits

Deep Dive into Apache Spark Framework

1
Spark’s Place in Hadoop Ecosystem
2
Spark Components & its Architecture
3
Spark Deployment Modes
4
Introduction to Spark Shell
5
Writing your first Spark Job Using SBT
6
Submitting Spark Job
7
Spark Web UI
8
Data Ingestion using Sqoop

Playing with Spark RDDs

1
Challenges in Existing Computing Methods
2
Probable Solution & How RDD Solves the Problem
3
What is RDD, It’s Operations, Transformations & Actions
4
Data Loading and Saving Through RDDs
5
Key-Value Pair RDDs
6
Other Pair RDDs, Two Pair RDDs
7
RDD Lineage
8
RDD Persistence
9
WordCount Program Using RDD Concepts
10
RDD Partitioning & How It Helps Achieve Parallelization
11
Passing Functions to Spark

DataFrames and Spark SQL

1
Need for Spark SQL
2
What is Spark SQL?
3
Spark SQL Architecture
4
SQL Context in Spark SQL
5
User Defined Functions
6
Data Frames & Datasets
7
Interoperating with RDDs
8
JSON and Parquet File Formats
9
Loading Data through Different Sources
10
Spark – Hive Integration

Machine Learning using Spark MLlib

1
Why Machine Learning?
2
What is Machine Learning?
3
Where Machine Learning is Used?
4
Face Detection: USE CASE
5
Different Types of Machine Learning Techniques
6
Introduction to MLlib
7
Features of MLlib and MLlib Tools
8
Various ML algorithms supported by MLlib

Deep Dive into Spark MLlib

1
Supervised Learning – Linear Regression, Logistic Regression, Decision Tree, Random Forest
2
Unsupervised Learning – K-Means Clustering & How It Works with MLlib
3
Analysis on US Election Data using MLlib (K-Means)

Understanding Apache Kafka and Apache Flume

1
Need for Kafka
2
What is Kafka?
3
Core Concepts of Kafka
4
Kafka Architecture
5
Where is Kafka Used?
6
Understanding the Components of Kafka Cluster
7
Configuring Kafka Cluster
8
Kafka Producer and Consumer Java API
9
Need of Apache Flume
10
What is Apache Flume?
11
What is Apache Flume?
12
Basic Flume Architecture
13
Flume Sources
14
Flume Sinks
15
Flume Channels
16
Flume Configuration
17
Integrating Apache Flume and Apache Kafka

Apache Spark Streaming - Processing Multiple Batches

1
Drawbacks in Existing Computing Methods
2
Why Streaming is Necessary?
3
What is Spark Streaming?
4
Spark Streaming Features
5
Spark Streaming Workflow
6
How Uber Uses Streaming Data
7
Streaming Context & DStreams
8
Transformations on DStreams
9
Describe Windowed Operators and Why it is Useful
10
Important Windowed Operators
11
Slice, Window and ReduceByWindow Operators
12
Stateful Operators

Apache Spark Streaming - Data Sources

1
Apache Spark Streaming: Data Sources
2
Streaming Data Source Overview
3
Apache Flume and Apache Kafka Data Sources
4
Example: Using a Kafka Direct Data Source
5
Perform Twitter Sentimental Analysis Using Spark Streaming

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