Introduction to Big Data Hadoop and Spark.

Learning Objectives: Understand Big Data and its components such as HDFS. You will learn about the Hadoop Cluster Architecture, Introduction to Spark and the difference between batch processing and real-time processing.

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
Hadoop Terminal Commands
Big Data Analytics with Batch & Real-time Processing
Why Spark is needed?
What is Spark?
How Spark differs from other frameworks?
Spark at Yahoo!

Introduction to Scala for Apache Spark
Functional Programming and OOPs Concepts in Scala
Deep Dive into Apache Spark Framework
Playing with Spark RDDs
DataFrames and Spark SQL
Machine Learning using Spark MLlib
Deep Dive into Spark MLlib
Understanding Apache Kafka and Apache Flume
Apache Spark Streaming – Processing Multiple Batches
Apache Spark Streaming – Data Sources

What are the objectives of our Online Spark Training Course?

Spark Certification Training is designed by industry experts to make you a Certified Spark Developer. The Spark Scala Course offers:
Overview of Big Data & Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator)
Comprehensive knowledge of various tools that fall in Spark Ecosystem like Spark SQL, Spark MlLib, Sqoop, Kafka, Flume and Spark Streaming
The capability to ingest data in HDFS using Sqoop & Flume, and analyze those large datasets stored in the HDFS The power of handling real time data feeds through a publish-subscribe messaging system like Kafka
The exposure to many real-life industry-based projects which will be executed using Edureka’s CloudLab Projects which are diverse in nature covering banking, telecommunication, social media, and govenment domains Rigorous involvement of a SME throughout the Spark Training to learn industry standards and best practices

Why should you go for Online Spark Training?

Spark is one of the most growing and widely used tool for Big Data & Analytics. It has been adopted by multiple companies falling into various domains around the globe and therefore, offers promising career opportunities. In order to take part in these kind of opportunities, you need a structured training that is aligned as per Cloudera Hadoop and Spark Developer Certification (CCA175) and current industry requirements and best practices.

Besides strong theoretical understanding, it is quite essential to have a strong hands-on experience. Hence, during the Edureka’s Spark and Scala course, you will be working on various industry-based use-cases and projects incorporating big data and spark tools as a part of solution strategy.

Additionally, all your doubts will be addressed by the industry professional, currently working on real life big data and analytics projects

What are the skills that you will be learning with our Spark Certification Training?

The Edureka’s Spark Training is designed to help you become a successful Spark developer. During this course, our expert instructors will train you to- Write Scala Programs to build Spark Application Master the concepts of HDFS Understand Hadoop 2.x Architecture Understand Spark and its Ecosystem Implement Spark operations on Spark Shell Implement Spark applications on YARN (Hadoop) Write Spark Applications using Spark RDD concepts Learn data ingestion using Sqoop Perform SQL queries using Spark SQL Implement various machine learning algorithms in Spark MLlib API and Clustering Explain Kafka and its components Understand Flume and its components Integrate Kafka with real time streaming systems like Flume Use Kafka to produce and consume messages Build Spark Streaming Application Process Multiplem Batches in Spark Streaming Implement different streaming data sources

who should take this course?

Market for Big Data Analytics is growing tremendously across the world and such strong growth pattern followed by market demand is a great opportunity for all IT Professionals. Here are a few Professional IT groups, who are continuously enjoying the benefits and perks of moving into Big Data domain.
Developers and Architects
BI /ETL/DW Professionals
Senior IT Professionals
Testing Professionals
Mainframe Professionals
Big Data Enthusiasts
Software Architects, Engineers and Developers
Data Scientists and Analytics Professionals

How will Spark and Scala Online Training help your career?

The stats provided below will provide you a glimpse of growing popularity and adoption rate of Big Data tools like Spark in the current as well as upcoming years: 56% of Enterprises Will Increase Their Investment in Big Data over the Next Three Years – Forbes McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts Average Salary of Spark Developers is $113k According to a McKinsey report, US alone will deal with shortage of nearly 190,000 data scientists and 1.5 million data analysts and Big Data managers by 2018 As you know, nowadays, many organisations are showing interest in Big Data and are adopting Spark as a part of solution strategy, the demand of jobs in Big Data and Spark is rising rapidly. So, it is high time to pursue your career in the field of Big Data & Analytics with our Spark and Scala Certification Training Course.

Best Features of Spark

Let’s learn the Sparkling features of Spark.

  1. In-memory computation

Apache Spark is a cluster-computing platform, and it is designed to be fast for interactive queries and this is possible by In-memory cluster computation. It enables Spark to run iterative algorithms.

The data inside RDD are stored in memory for as long as you want to store. We can improve the performance by an order of magnitudes by keeping the data in-memory.

  1. Lazy Evaluation

Lazy evaluation means the data inside RDDS are not executed in a go. When we apply data it forms a DAG and the computation is performed only after an action is triggered. When an action is triggered all the transformation on RDDs then executed. Thus, it limits how much work it has to do.

  1. Fault Tolerance

In Spark, we achieve fault tolerance by using DAG. When the worker node fails by using DAG we can find that in which node has the problem. Then we can re-compute the lost partition of RDD from the original one. Thus, we can easily recover the lost data.

  1. Fast Processing

Today we are generating a huge amount of data and we want that our processing speed should be very fast. So while using Hadoopthe processing speed of MapReduce was not fast. That’s why we are using Spark as it gives very good speed.

  1. Persistence

We can use RDD in in-memory and we can also retrieve them directly from memory. There is no need to go in the disk, this speed up the execution. On the same data, we can perform multiple operations. We can do this by storing the data explicitly in memory by calling persist() or cache() function.

  1. Partitioning

RDD partition the records logically and distributes the data across various nodes in the cluster. The logical divisions are only for processing and internally it has no division. Thus, it provides parallelism.

  1. Parallel

In Spark, RDD process the data parallelly

  1. Location-Stickiness

To compute partitions RDDs are capable of defining placement preference. Placement preference refers to information about the location of RDD. The DAG scheduler places the partitions in such way that task should be close to data. Due to this computation speed increases.

  1. Coarse-grained Operation

We apply coarse-grained transformations to RDD. It means that the operation applies not on an individual element but to the whole dataset in the data set of RDD.

  1. No limitation

Large-Scale Data Processing Frameworks – What Is Apache Spark?

Apache Spark is the latest data processing framework from open source. It is a large-scale data processing engine that will most likely replace Hadoop’s MapReduce. Apache Spark and Scala are inseparable terms in the sense that the easiest way to begin using Spark is via the Scala shell. But it also offers support for Java and python. The framework was produced in UC Berkeley’s AMP Lab in 2009. So far there is a big group of four hundred developers from more than fifty companies building on Spark. It is clearly a huge investment.

A brief description

Apache Spark is a general use cluster computing framework that is also very quick and able to produce very high APIs. In memory, the system executes programs up to 100 times quicker than Hadoop’s MapReduce. On disk, it runs 10 times quicker than MapReduce. Spark comes with many sample programs written in Java, Python and Scala. The system is also made to support a set of other high-level functions: interactive SQL and NoSQL, MLlib(for machine learning), GraphX(for processing graphs) structured data processing and streaming. Spark introduces a fault tolerant abstraction for in-memory cluster computing called Resilient distributed datasets (RDD). This is a form of restricted distributed shared memory. When working with spark, what we want is to have concise API for users as well as work on large datasets. In this scenario many scripting languages does not fit but Scala has that capability because of its statically typed nature.

Usage tips

As a developer who is eager to use Apache Spark for bulk data processing or other activities, you should learn how to use it first. The latest documentation on how to use Apache Spark, including the programming guide, can be found on the official project website. You need to download a README file first, and then follow simple set up instructions. It is advisable to download a pre-built package to avoid building it from scratch. Those who choose to build Spark and Scala will have to use Apache Maven. Note that a configuration guide is also downloadable. Remember to check out the examples directory, which displays many sample examples that you can run.


Spark is built for Windows, Linux and Mac Operating Systems. You can run it locally on a single computer as long as you have an already installed java on your system Path. The system will run on Scala 2.10, Java 6+ and Python 2.6+.

Spark and Hadoop

The two large-scale data processing engines are interrelated. Spark depends on Hadoop’s core library to interact with HDFS and also uses most of its storage systems. Hadoop has been available for long and different versions of it have been released. So you have to create Spark against the same sort of Hadoop that your cluster runs. The main innovation behind Spark was to introduce an in-memory caching abstraction. This makes Spark ideal for workloads where multiple operations access the same input data.

Users can instruct Spark to cache input data sets in memory, so they don’t need to be read from disk for each operation. Thus, Spark is first and foremost in-memory technology, and hence a lot faster.It is also offered for free, being an open source product. However, Hadoop is complicated and hard to deploy. For instance, different systems must be deployed to support different workloads. In other words, when using Hadoop, you would have to learn how to use a separate system for machine learning, graph processing and so on. With Spark you find everything you need in one place. Learning one difficult system after another is unpleasant and it won’t happen with Apache Spark and Scala data processing engine. Each workload that you will choose to run will be supported by a core library, meaning that you won’t have to learn and build it. Three words that could summarize Apache spark include quick performance, simplicity and versatility.

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