After the successful completion of Apache Spark training at Tekslate, the participant will be able to

• Get an overview of Big Data & Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator).

• Gain knowledge of Apache Spark and Scala Programming implementation.

• Gain comprehensive knowledge of various tools that fall in Spark Ecosystem like Spark SQL, Spark MlLib, Sqoop, Kafka, Flume and Spark Streaming.

• Write Spark applications using Scala.

• Understand RDD, its Operations, Transformations & Actions along with the implementation of Spark algorithms.

• Gain knowledge on Scala classes concept and execution patterns.

• Understand data ingestion using Sqoop.

• Perform SQL queries using Spark SQL.

• Use Kafka to produce and consume messages.

• Top companies like Microsoft, Amazon, IBM, etc., are incorporating Apache Spark in their deployments.

• There is a high market demand for certified Apache Spark developers and high salary packages are being offered to them.

• The average pay of a Certified Apache Spark developer stands at 105,700 USD per annum.

The following job roles will get benefited from learning this course:

• Aspirants looking for a career in this field.

• Analytics professionals

• Research professionals

• IT developers and testers

• Data scientists

• BI and reporting professionals

• Professionals who want to enhance their skills in Big Data analytics.

As such, there are no prerequisites for learning Apache Spark course. Anyone who is interested in learning this course can join this training.

• Having basic knowledge of databases, SQL and query language will be beneficial, but not mandatory.

We will provide two real-time projects under the guidance of a professional trainer, who will explain you on how to acquire in-depth knowledge on all the concepts involved in these projects.

• What is Big Data?

• Big Data Customer Scenarios

• Limitations and Solutions of Existing Data Analytics Architecture with Uber Use Case

• How Hadoop Solves the Big Data Problem?

• What is Hadoop?

• Hadoop’s Key Characteristics

• Hadoop Ecosystem and HDFS

• Hadoop Core Components

• Rack Awareness and Block Replication

• YARN and its Advantage

• Hadoop Cluster and its Architecture

• Hadoop: Different Cluster Modes

• Big Data Analytics with Batch & Real-time Processing

• Why Apache Spark is needed?

• What is Apache Spark?

• How Apache Spark differs from other frameworks?

• What is Scala?

• Why Scala for Apache Spark?

• Scala in other Frameworks

• Introduction to Scala REPL

• Basic Scala Operations

• Variable Types in Scala

• Control Structures in Scala

• For each loop, Functions and Procedures

• Collections in Scala- Array

• ArrayBuffer, Map, Tuples, Lists, and more

• Apache Spark at Yahoo!

• Functional Programming

• Higher Order Functions

• Anonymous Functions

• Class in Scala

• Getters and Setters

• Custom Getters and Setters

• Properties with only Getters

• Auxiliary Constructor and Primary Constructor

• Singletons

• Extending a Class

• Overriding Methods

• Traits as Interfaces and Layered Traits

• Apache Spark’s Place in the Hadoop Ecosystem

• Apache Spark Components & its Architecture

• Apache Spark Deployment Modes

• Introduction to Apache Spark Shell

• Writing your first Apache Spark Job Using SBT

• Submitting Apache Spark Job

• Apache Spark Web UI

• Data Ingestion using Sqoop

• Challenges in Existing Computing Methods

• Probable Solution & How RDD Solves the Problem

• What is RDD, Its Operations, Transformations & Actions

• Data Loading and Saving Through RDDs

• Key-Value Pair RDDs

• Other Pair RDDs, Two Pair RDDs

• RDD Lineage

• RDD Persistence

• WordCount Program Using RDD Concepts

• RDD Partitioning & How It Helps Achieve Parallelization

• Passing Functions to Apache Spark

• Need for Apache Spark SQL

• What is Apache Spark SQL?

• Apache Spark SQL Architecture

• SQL Context in Apache Spark SQL

• User Defined Functions

• Data Frames & Datasets

• Interoperating with RDDs

• JSON and Parquet File Formats

• Loading Data through Different Sources

• Apache Spark – Hive Integration

• Why Machine Learning?

• What is Machine Learning?

• Where Machine Learning is Used?

• Face Detection: USE CASE

• Different Types of Machine Learning Techniques

• Introduction to MLlib

• Features of MLlib and MLlib Tools

• Various ML algorithms supported by MLlib

• Supervised Learning - Linear Regression, Logistic Regression, Decision Tree, Random Forest

• Unsupervised Learning - K-Means Clustering & How It Works with MLlib

• Analysis of US Election Data using MLlib (K-Means)

• Need for Kafka

• What is Kafka?

• Core Concepts of Kafka

• Kafka Architecture

• Where is Kafka Used?

• Understanding the Components of Kafka Cluster

• Configuring Kafka Cluster

• Kafka Producer and Consumer Java API

• The need of Apache Flume

• What is Apache Flume?

• Basic Flume Architecture

• Flume Sources

• Flume Sinks

• Flume Channels

• Flume Configuration

• Integrating Apache Flume and Apache Kafka

• Drawbacks in Existing Computing Methods

• Why Streaming is Necessary?

• What is Apache Spark Streaming?

• Apache Spark Streaming Features

• Apache Spark Streaming Workflow

• How Uber Uses Streaming Data

• Streaming Context & DStreams

• Transformations on DStreams

• Describe Windowed Operators and Why it is Useful

• Important Windowed Operators

• Slice, Window and ReduceByWindow Operators

• Stateful Operators

• Apache Spark Streaming: Data Sources

• Streaming Data Source Overview

• Apache Flume and Apache Kafka Data Sources

• Example: Using a Kafka Direct Data Source

• Perform Twitter Sentimental Analysis Using Apache Spark Streaming

Our trainers are well experienced professionals and certified in working with front-end development technologies.

We will record all the Live sessions you go through, and we will send the recording of the class you missed.

For practical execution, our trainer will provide server access to the student. 

All our training classes are live. This is to solve all the student queries directly with the trainer.

Live online training means, our trainer will be in online with you to solve your issues.
Pre-recorded training means, there will be no trainer available to solve your issues.

You can contact our Tekslate support team, or you can send an email to info@tekslate.com for your queries.

Yes, you can access the course material after completing course by the recorded training videos we shared with you during training.

To avail all discounts offered by Tekslate, you need to visit the website regularly. However, there will be a group discount for 2 or more participants.

Yes, you will get your refund by cancelling some administration fee from the course. But, you should cancel your enrolment within 48 hours of registration, and then the refund will be retained within 30 days of a request.

The popular Certifications available for Apache Spark are listed below:

CCA Spark and Hadoop Developer Exam (CCA175)

HDP Apache Spark Developer Certification

Tekslate helps you with all the essentials required to clear your Apache Spark Certification Exam in your first submit itself.

However, it also offers a course completion certificate of this course after completion of training based on the candidate’s performance in the project implementation.