Overview of Big Data and Hadoop

The quantity of data is growing exponentially for many reasons. Our day-to-day activities in various sources generate lots of data. So, the term ‘big data’ is used to denote a collection of large and complex datasets that is difficult to store and process using available database management tools or traditional data processing applications. Apache Hadoop was developed to enhance the usage of big data and solve the major issues related to it. Formally, Google invented a new methodology of processing data popularly known as MapReduce. Later, Doug Cutting and Mike Cafarella, inspired by the white paper of the MapReduce framework, developed Hadoop to apply MapReduce concepts to an open-source software framework that supported the Nutch search engine project. Considering the original case study, Hadoop was designed with a much simpler storage infrastructure facilities. Let us discuss more about Apache Spark in the further section of Spark Tutorial.

Check out this insightful video on Apache Spark Tutorial for Beginners:

Apache Spark Tutorial – Learn Spark from Experts Overview of Big Data and Hadoop The quantity of data is growing exponentially for many reasons. Our day-to-day activities in various sources generate lots of data. So, the term ‘big data’ is used to denote a collection of large and complex datasets that is difficult to store and process using

In this Apache Spark tutorial, let’s now understand how can data be categorized as big data? But before that let’s have a look at what we will be talking about in this Apache Spark Tutorial:

Read about Apache Spark from our Cloudera Spark Training and be an Apache Spark Specialist!

Five Vs of Big Data

Data can be categorized as big data based on various factors. The main concept common in all these factors is the amount of data. Let us understand the characteristics of big data that are broken down into 5 Vs:

5V's of Big Data

1. Velocity

Velocity refers to the speed at which data arrives. Every day, huge amounts of data is generated, stored, and analyzed. Data includes emails, images, financial reports, videos, etc. Data is being generated at lightning speed around the world. Big Data Analytics tools allow us to explore the data, at the time it gets generated.

2. Volume

Volume refers to the huge amount of data, generated from credit cards, social media, IoT devices, smart home gadgets, videos, etc. Data is growing so large that the traditional computing systems can no longer handle them.

3. Variety

Variety refers to the different types of data. Data is mainly categorized into structured and unstructured data. Structured data has schema and well-defined tables to store information. Data without schema and a pre-defined data model is called the unstructured data. In fact, more than 75 percent of the world’s data exist in the unstructured form. The unstructured data includes images, videos, social media-generated data, etc.

4. Veracity

Veracity refers to the quality of the data. Let’s suppose that we are storing data using high computational power. If this data is of no use in the future, then we are wasting our resources on it. Thus, we have to check the trustworthiness of the data before storing it. It depends on the reliability and accuracy of the content. We should not store loads of data if the content is not reliable or accurate.

5. Value

Value is the most important part of big data. Organizations use big data to find the hidden benefits of it. This data analyzing can help increase financial benefits. Having a vast amount of data is useless until we extract something meaningful from it.

Although Hadoop made a grasp itself in the market, there were some limitations. Hadoop is used to process data in various batches, therefore real-time data streaming is not possible with Hadoop. Apache Spark, unlike Hadoop clusters, allows real-time Data Analytics using Spark streaming. For this reason, Apache Spark has quite fast market growth these days. The median salary of a Data Scientist who uses Apache Spark is around US$100,000. Isn’t that crazy?

 

Why Apache Spark over Hadoop?

Both Hadoop and Spark are open-source projects by the Apache Software Foundation, and they are the flagship products used for Big Data Analytics. The key difference between MapReduce and Spark is their approach toward data processing. Spark can perform in-memory processing, while Hadoop MapReduce has to read from and write to a disk. Let us understand some major differences between Apache Spark and Hadoop in the next section of this Apache Spark tutorial.

Differences Between Hadoop and Spark

1. Speed

Spark is a general-purpose cluster computing tool. It runs applications up to 100 times faster in memory and 10 times faster on disk than Hadoop. For Spark, this is possible as it reduces the number of read/write cycles to disk and stores data in memory.

2. Easy to Manage

Spark can perform batch processing, interactive Data Analytics, Machine Learning, and streaming, everything in the same cluster. This functionality makes Apache Spark a complete Data Analytics engine. With Spark, there is no need for managing various Spark components for each task.

Hadoop MapReduce provides only the batch-processing engine. So, in Hadoop, we need a different engine for each task. It is very difficult to manage many components.

3. Real-time Analysis

Spark can easily process real-time data, i.e., real-time event streaming at a rate of millions of events/second, e.g., the data that is streaming live from Twitter, Facebook, Instagram, etc. Spark efficiently processes live streams.

Here, MapReduce fails as it cannot handle real-time data processing since it is meant to perform only batch processing on huge volumes of data.

Apache Spark

These are the major differences between Apache Spark and Hadoop. But, what if we use Apache Spark with Hadoop? When we use both technologies together, it provides a more powerful cluster computing with batch processing and real-time processing.

Next, in this Apache Spark tutorial, let us understand how Apache Spark fits in the Hadoop ecosystem.

How does Apache Spark fit in the Hadoop ecosystem?

Spark is designed for the enhancement of the Hadoop stack. Spark can perform read/write data operations with HDFS, HBase, or Amazon S3. Hadoop users can use Apache Spark to enhance the computational capabilities of their Hadoop MapReduce system.

Hadoop Ecosystem

Spark can be used with Hadoop or Hadoop YARN together. Apache Spark can be deployed on Hadoop in three ways: Standalone, YARN, and SIMR.

Standalone Deployment

Spark provides a simple standalone deployment mode. This allows Spark to allocate all resources or a subset of resources in a Hadoop cluster. We can also run Spark in parallel with Hadoop MapReduce. Spark jobs can be deployed easily using the HDFS data. Spark’s simple architecture makes it a preferred choice for Hadoop users.

Hadoop YARN Deployment

Apache Spark contains some configuration files for the Hadoop cluster. These config files can easily read/write to HDFS and YARN Resource Manager. We can easily run Spark on YARN without any pre-installation.

Spark in MapReduce (SIMR)

We can easily deploy Spark on MapReduce clusters. It will help you start experimenting with Spark to explore more.

Let us discuss some benefits of leveraging Hadoop and Spark together in the next section of this Apache Spark tutorial.

Why should we consider using Hadoop and Spark together?

Most people think of Spark as a replacement of Hadoop, but instead of replacing Hadoop we can consider Spark as a binding technology for Hadoop. However, Spark can run separately from Hadoop, where it can run on a standalone cluster. Spark used on top of Hadoop can leverage its storage and cluster management.

Though Spark does not provide its own storage system, we can take advantage of Hadoop for that. By this, we can make a powerful production environment using Hadoop capabilities. Spark can also use YARN Resource Manager for easy resource management. Spark can easily handle tasks scheduling across a cluster.

Apache Spark can use the disaster recovery capabilities of Hadoop. We can leverage Hadoop with Spark to receive better cluster administration and data management. Spark together with Hadoop provides better data security.

Spark Machine Learning provides capabilities that are not properly utilized in Hadoop MapReduce. Using a fast computation engine like Spark, these Machine Learning algorithms can now execute faster since they can be executed in memory. In MapReduce programs, on the other hand, the data gets moved in and out of the disks between different stages of the processing pipeline.

Next, in this Spark tutorial, we will check out some market leaders who have implemented Spark and Hadoop together.

Want to grasp a detailed knowledge of Spark? Read this extensive Spark RDD programming!

Industries Using Spark and Hadoop Together

Spark and Hadoop together make a powerful combination to handle Big Data Analytics. The following organizations are using Spark on Hadoop MapReduce and YARN.

Apache Spark Tutorial

Let us finally get into our main section of this Apache Spark tutorial, where we will be discussing ‘What is Apache Spark?’

Know more about the applications of Spark from this Apache Spark Tutorial!

What is Apache Spark?

Apache Spark is a lightning-fast cluster computing framework designed for real-time processing. Spark is an open-source project in Apache Software Foundation. Spark overcomes the limitations of Hadoop MapReduce and it extends the MapReduce model to be efficiently used for data processing.

spark tutorial

Spark is a market leader for big data processing. It is widely used across organizations in lots of ways. It has surpassed Hadoop by running 100 times faster in memory and 10 times faster in disks. This Apache Spark tutorial will take you through a series of blogs including Spark Streaming, Spark SQL, Spark MLlib, Spark GraphX, etc.

Let us learn about the evolution of Apache Spark in the next section of this Spark tutorial.

If you want to learn Apache Spark Installation, check out this!

Evolution of Apache Spark

Before Spark, there was MapReduce which was used as a processing framework. Then, Spark got initiated as one of the research projects in 2009 at UC Berkeley AMPLab. It was later open-sourced in 2010. The major intention behind this project was to create a cluster management framework that supports various computing systems based on clusters. After its release to the market, Spark grew and moved to the Apache Software Foundation in 2013. Now, most of the organizations across the world have incorporated Apache Spark for empowering their big data applications.

Let us now continue with our Apache Spark tutorial by checking out why Spark is important to us that we are so concerned about it.

Check out this insightful video on ‘Apache Spark Tutorial for Beginners’:

Apache Spark Tutorial – Learn Spark from Experts Overview of Big Data and Hadoop The quantity of data is growing exponentially for many reasons. Our day-to-day activities in various sources generate lots of data. So, the term ‘big data’ is used to denote a collection of large and complex datasets that is difficult to store and process using

Why do we need Apache Spark?

Most of the technology-based companies across the globe have moved toward Apache Spark. They were quick enough to understand the real value possessed by Spark such as Machine Learning and interactive querying. Industry leaders such as Amazon, Huawei, and IBM have already adopted Apache Spark. The firms that were based on Hadoop, such as Hortonworks, Cloudera, and MapR, have also moved to Apache Spark.

Big Data Hadoop professionals surely need to learn Apache Spark since it is the next most important technology in Hadoop data processing. Moreover, even ETL professionals, SQL professionals, and Project Managers can gain immensely if they master Apache Spark. Finally, Data Scientists also need to gain in-depth knowledge of Spark to excel in their careers. So, learn Spark through this Apache Spark tutorial.

Spark can be extensively deployed in Machine Learning scenarios. Data Scientists are expected to work in the Machine Learning domain, and hence they are the right candidates for Apache Spark training. Those who have an intrinsic desire to learn the latest emerging technologies can also learn Spark through this Apache Spark tutorial.

Prepare yourself for the industry by going through these Top Hadoop Interview Questions and Answers now!

Domain Scenarios of Apache Spark

Today, there is widespread deployment of Big Data tools. With each passing day, the requirements of enterprises increase, and therefore there is a need for a faster and more efficient form of data processing. Most of the streaming data is in an unstructured format, coming in thick and fast continuously. Here in this Apache Spark tutorial, we look at the different domain scenarios and how Spark is used in each of them.

Apache Spark Tutorial

Banking

Spark is being more and more adopted by the banking sector. It is mainly used here for financial fraud detection with the help of Spark ML. Banks use Spark to handle credit risk assessment, customer segmentation, and advertising as well. Apache Spark is also used to analyze social media profiles, forum discussions, customer support chat, and emails. This way of analyzing helps organizations make better business decisions.

E-commerce

Spark is widely used in the e-commerce industry. Spark Machine Learning, along with streaming, can be used for real-time data clustering. Businesses can combine results with other data sources to provide better recommendations to their customers. Recommendation systems are mostly used in the e-commerce industry to show new trends.

Healthcare

Apache Spark is a powerful computation engine to perform advanced analytics on patient records. It helps keep track of the patients’ health records easily. The healthcare industry uses Spark to deploy services to get insights like patient feedbacks, hospital services, and to keep track of medical data.

Media

Many gaming companies use Apache Spark for finding patterns from their real-time in-game events. With this, they can derive further business opportunities like adjusting the complexity-level of the game automatically according to the players’ performance. Some media companies like Yahoo uses Apache Spark for targeted marketing, customizing news pages based on readers’ interests, and so on. They use tools such as Machine Learning algorithms for identifying the ‘readers’ interests’ category. Eventually, they categorize such news stories in various sections and keep the reader updated on a timely basis.

Travel

Many people land up with travel planners to make their vacation a perfect one, and these travel companies depend on Apache Spark for offering various travel packages. TripAdvisor is one such company that uses Apache Spark to compare different travel packages from different providers. It scans through hundreds of websites to find the best and reasonable hotel price, trip package, etc.

Intellipaat provides the most comprehensive Spark Online Training Course to fast-track your career!

Apache Spark: Use Cases

Let’s now look at a few use cases of Apache Spark.

Finding a Spark at Yahoo!

Yahoo! has over 1 billion monthly users. Therefore, Yahoo! has to manage their data on a huge scale. It needs to handle huge volumes of data arriving at a fast rate. It uses Hadoop cluster with more than 40,000 nodes to process data. So, it looked for a lightning-fast computing framework for data processing. Hence, Yahoo! adopted Apache Spark to solve its problem.

Apache Spark

How Apache Spark Enhanced Data Science at Yahoo!

Although Spark is a quite fast computing engine, it is in demand for many other reasons as follows:
● It works with various programming languages.
● It has an efficient in-memory processing.
● It can be deployed over Hadoop through YARN.

Yahoo! checked Spark over Hadoop using a project, which was intended to explore the power of Spark and Hadoop together. The project was implemented using Spark’s Scala API, which gets executed much faster through Spark, where Hadoop took more time for the same process.

Although Spark’s speed and efficiency is impressive, Yahoo! isn’t removing its Hadoop architecture. They need both; Spark will be preferred for real-time streaming and Hadoop will be used for batch processing. Most interesting here is that both can be used together through YARN.

If you have any queries related to Spark and Hadoop, kindly refer to our Big Data Hadoop and Spark Community!

Apache Spark at eBay

An American multinational e-commerce corporation, eBay creates a huge amount of data every day. eBay has lots of existing users and it adds a huge number of new members every day. Except for sellers and buyers, the most important asset for eBay is data. eBay directly connects buyers and sellers. So, a lightning-fast engine is required to handle the huge volumes of this real-time streaming data.

Apache Spark at Ebay

Apache Spark is mainly used to redefine better customer experience and overall performance at eBay. Apache Spark and Hadoop YARN combine the powerful functionalities of both. Hadoop’s thousands of nodes can be leveraged with Spark through YARN.

Our Apache Spark tutorial won’t be complete without talking about the interesting use cases of Apache Spark. Let us see some of them.

Hope this tutorial gave you an insightful introduction to Apache Spark. Further, Spark Hadoop and Spark Scala are interlinked in this tutorial, and they are compared both at various fronts. Now, we will be learning Spark in detail in the coming sessions of this Apache Spark tutorial. Well, in the next session, we will discuss the features of Apache Spark.

Read about Apache Spark from our Cloudera Spark Training and be an Apache Spark Specialist!

Frequently Asked Questions

What is Spark?

An open-source engine developed specifically for handling large-scale data processing and analytics, Spark allows users to access data from multiple sources including HDFS, OpenStack Swift, Amazon S3, and Cassandra.

How do I start learning Spark?

You can learn Apache Spark from the Internet using this tutorial. To know more about this technology, you may also refer to our free and comprehensive video tutorial on YouTube: https://youtu.be/GFC2gOL1p9k

On top of that, we provide definitive Apache Spark training. Curated by industry experts, our training stands out in terms of quality and technical-richness. It can help you learn Spark from scratch.

What is Spark used for?

The applications of Apache Spark are many. Some of them can be listed as:

  • Machine Learning (for performing clustering, classification, dimensionality reduction, etc.)
  • Fog Computing (for decentralizing data)
  • Event Detection (keeping track of unusual data behavior for protecting the system)
  • Interactive Analysis (for processing exploratory queries without sampling)

What is the difference between Spark and PySpark?

Spark is an open-source engine developed for handling large-scale data processing and analytics.

PySpark is an API developed and released by Apache Spark which helps data scientists work with Resilient Distributed Datasets (RDD), data frames, and machine learning algorithms.

Is Spark difficult to learn?

Learning Spark is not difficult if you have a basic understanding of Python or any programming language, as Spark provides APIs in Java, Python, and Scala. You can take up this Spark Training to learn Spark from industry experts.

Is Apache Spark in demand?

In the present day, there are more than 1000 contributors to Apache Spark across 250+ companies worldwide. Numerous companies are solely relying upon Apache Spark for conducting their day-to-day business operations. The demand for Apache Spark is on the rise and this trend won’t change in the upcoming years.

Why Spark is faster than Hadoop?

Spark is significantly faster than Hadoop MapReduce because Spark processes data in the main memory of worker nodes and hence prevents unnecessary input/output operations with disks.

Can we install Spark without Hadoop?

As per Spark documentation, Spark can run without Hadoop. It can be done by making Spark run in the Standalone mode without any resource manager. But for running spark in a multi-node setup, resource managers are required.

Table of Contents

Spark Features

Key features of Spark: Developed in AMPLab of University of California, Berkeley, Apache Spark was developed for higher speed, ease of use and more in-depth analysis. Though it was built to be installed on top of Hadoop cluster, however its ability to parallel processing allows it run independently as well. Let's take a closer look at the features of Apache Read More

Apache Spark Architecture

Two Main Abstractions of Apache Spark: Apache Spark has a well-defined layer architecture which is designed on two main abstractions Resilient Distributed Dataset (RDD): RDD is an immutable (read-only), fundamental collection of elements or items that can be operated on many devices at the same time (parallel processing). Each dataset in an RDD can be divided into logical portions, which Read More

Apache Spark Applications

Applications on Apache Spark: Since the time of its inception in 2009 and its conversion to an open source technology, Apache Spark has taken the big data world by storm. It became one of the largest open source communities that includes over 200 contributors. The prime reason behind its success was its ability to process heavy data faster than ever Read More

Downloading Spark and Getting Started

Steps to install Spark: Step 1 : Ensure if Java is installed Before installing Spark, Java is a must have for your system. Following command will verify the version of Java- $java -version If Java is already installed on your system, you get to see the following output which is as follows: java version "1.7.0_71" Java(TM) SE Runtime Environment (build Read More

Spark Components

Introduction to Spark Components: The following procedure gives the clear picture of the different Spark components. Apache Spark Core Spark Core consists of general execution engine for spark platform that all required by other functionality which is built upon as per the requirement approach. It provides in-built memory computing and referencing datasets stored in external storage systems. Check out this Read More

Programming with RDD in Spark

Resilient Distributed Datasets (RDDs): RDDs are the main logical data unit in Spark. They are a distributed collection of objects, which are stored in memory or on disks of different machines of a cluster. A single RDD can be divided into multiple logical partitions so that these partitions can be stored and processed on different machines of a cluster. RDDs Read More

Working with Key/Value Pairs

7Using RDD in Spark: Motivation Spark provides special type of operations on RDDs containing key or value pairs. These RDDs are called pair RDDs operations. Pair RDDs are a useful building block in many programming language, as they expose operations that allow you to act on each key operations in parallel or regroup data across the network. Creating Pair RDDs Read More

Spark Dataframe

What is Spark Dataframe?: In Spark, Dataframes are distributed collections of data, organized into rows and columns. Each column in a Dataframe has a name and an associated type. Dataframes are similar to traditional database tables, which are structured and concise. We can say that, Dataframes are relational databases with better optimization techniques. Spark Dataframes can be created from various Read More

Loading and Saving your Data

Loading and Saving Data in Spark: File Formats : Spark provides a very simple manner to load and save data files in a very large number of file formats. Formats may range the formats from being the unstructured, like text, to semi structured way, like JSON, to structured, like Sequence Files. The input file formats that Spark wraps all are transparently Read More

Spark SQL

Why Did Spark SQL Come into the Picture?: Spark SQL is one of the main component of the Apache Spark Framework. It is mainly used for structured data processing. It provides various Application Programmable Interfaces (APIs) in Python, Java, Scala, and R. Spark SQL integrates relational data processing with the functional programming API of Spark. It provides a programming abstraction called Dataframe and can also act as a Read More

What is Pyspark? – Apache Spark with Python

Pyspark - Apache Spark with Python: Being able to analyse huge data sets is one of the most valuable technological skills these days and this tutorial will bring you up to speed on one of the most used technologies, Apache Spark, combined with one of the most popular programming languages, Python, to do just that. In this what is PySpark tutorial,  we Read More

Spark and RDD Cheat Sheet

Spark and RDD User Handbook: Are you a programmer looking for in-memory computation on large clusters? If yes, then you must take Spark into your consideration. This Spark and RDD cheat sheet is designed for the one who has already started learning about the memory management and using Spark as a tool, then this sheet will be handy reference sheet. Read More

Machine Learning with PySpark Tutorial

Introduction to Spark MLlib: Apache Spark comes with a library named MLlib to perform machine learning tasks using spark framework. Since we have a Python API for Apache spark, that is, as you already know, PySpark, we can also use this spark ml library in PySpark. MLlib contains many algorithms and machine learning utilities. Watch this Apache Spark for beginners video by Read More

PySpark SQL Cheat Sheet

PySpark SQL User Handbook: Are you a programmer looking for a powerful tool to work on Spark? If yes, then you must take PySpark SQL into consideration. This PySpark SQL cheat sheet is designed for the one who has already started learning about the Spark and using PySpark SQL as a tool, then this sheet will be handy reference. Don't Read More

Recommended Videos

4 thoughts on “Apache Spark Tutorial – Learn Spark from Experts”

  1. I really enjoyed this tutorial, it gave me lots of background to understand the basics of apache technologies.This is a wonderful startup tutorial.

Leave a Reply

Your email address will not be published. Required fields are marked *