Overview of Big Data and Hadoop
The quantity of data is growing exponentially for many reasons these days. 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 the 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 much simpler storage infrastructure facilities. Let us discuss more about Apache Spark further in this Spark tutorial.
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In this Apache Spark tutorial, let’s first understand how data can be categorized as big data. But before that, let’s have a look at what we will be talking about throughout this Apache Spark tutorial:
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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 which we have broken down into 5 Vs:
Velocity refers to the speed at which data arrives. Every day, huge amounts of data are generated, stored, and analyzed. This 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 very time it gets generated.
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 traditional computing systems can no longer handle it the way we want.
Variety refers to the different types of data. Data is mainly categorized into structured and unstructured data. Structured data has a schema and well-defined tables to store information. Data without a schema and a pre-defined data model is called the unstructured data. In fact, more than 75 percent of the world’s data exists in the unstructured form. The unstructured data includes images, videos, social media-generated data, etc.
Veracity refers to the quality of the data. Let’s suppose that we are storing some 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.
Value is the most important part of big data. Organizations use big data to find hidden values from it. This data analysis 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 on 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 a 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 choose Apache Spark over Hadoop?
Both Hadoop and Spark are open-source projects from 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/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
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 streaming live from Twitter, Facebook, Instagram, etc. are efficiently processed by Spark.
Here, MapReduce fails as it cannot handle real-time data processing. It is meant to perform only batch processing on huge volumes of data.
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.
Apache Spark can be used with Hadoop or Hadoop YARN together. It can be deployed on Hadoop in three ways: Standalone, YARN, and SIMR.
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 as well. It will help us start experimenting with Spark to explore more.
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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. Meanwhile, Spark used on top of Hadoop can leverage its storage and cluster management.
Though Spark does not provide its own storage system, it 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 task scheduling across a cluster.
Apache Spark can use the disaster recovery capabilities of Hadoop as well. 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.
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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.
Let us finally get into our main section of this Apache Spark tutorial, where we will be discussing ‘What is Apache Spark?’
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What is Apache Spark?
Apache Spark is a lightning-fast cluster computing framework designed for real-time processing. Spark is an open-source project from Apache Software Foundation. Spark overcomes the limitations of Hadoop MapReduce, and it extends the MapReduce model to be efficiently used for data processing.
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 on disks.
This Apache Spark tutorial will take you through a series of blogs on 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.
Evolution of Apache Spark
Before Spark, first, 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 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 so important to us.
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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 initially 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.
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.
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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 streaming data is in an unstructured format, coming in thick and fast continuously. Here in this Apache Spark tutorial, we look at how Spark is used successfully in different industries.
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. Apache Spark is also used to analyze social media profiles, forum discussions, customer support chat, and emails. This way of analyzing data helps organizations make better business decisions.
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 share their findings 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.
Apache Spark is a powerful computation engine to perform advanced analytics on patient records. It helps keep track of patients’ health records easily. The healthcare industry uses Spark to deploy services to get insights such as patient feedbacks, hospital services, and to keep track of medical data.
Many gaming companies use Apache Spark for finding patterns from their real-time in-game events. With this, they can derive further business opportunities by customizing such as adjusting the complexity-level of the game automatically according to players’ performance, etc. Some media companies, like Yahoo, use 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.
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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.
Apache Spark: Use Cases
Our Apache Spark tutorial won’t be complete without talking about the interesting use cases of Apache Spark. 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, it has to manage its data arriving at a fast rate on a huge scale. It uses Hadoop cluster with more than 40,000 nodes to process data. So, it wanted a lightning-fast computing framework for data processing. Hence, Yahoo! adopted Apache Spark to solve its problem.
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. The most interesting fact here is that both can be used together through YARN.
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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 huge volumes of this real-time streaming data.
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.
Hopefully, 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 at various fronts. We will be learning Spark in detail in the coming sections of this Apache Spark tutorial. Well, in the next section, we will discuss the features of Apache Spark.
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