The amount of data generated has increased by leaps and bounds over the years. And this data comes in all forms and formats - and at a very high speed too. In the past, managing and handling were usually manual because of the limited amount of data, however, that is not the case now. With the increase in the volume of data generated, the data has become more difficult to store, process, and analyze - known as Big Data. The next pertinent question is how do we manage Big Data. And here’s where Hadoop comes into play — a framework used to store, process, and analyze Big Data.
Our Hadoop tutorial will help you understand what it is and why is Hadoop needed use cases, and more. The Hadoop tutorial also covers various skills and topics from HDFS to MapReduce and YARN, and even prepare you for a Big Data and Hadoop interview. So watch the Hadoop tutorial to understand the Hadoop framework, and how various components of the Hadoop ecosystem fit into the Big Data processing lifecycle and get ready for a successful career in Big Data and Hadoop.
The topics covered in this Hadoop tutorial are:
Hadoop is one of the top platforms for business data processing and analysis, and here are the significant benefits of learning Hadoop tutorial for a bright career ahead:
Today, Hadoop has been implemented across multiple verticals to match their specific needs. Yahoo was among the first companies to embrace Hadoop. Since then, several top businesses, including Facebook, Twitter, and Adobe, have implemented this in their architecture to benefit their organization.
In Banking and Securities, Big Data can monitor fraudulent activities, give early warnings, detect card fraud, audit trails, credit risk reporting, and manage customer data analytics to ease security issues in the financial sector. Securities Exchange Commission (SEC) is now utilizing Big Data to track and monitor activities with network analytics and natural language processing.
In Healthcare, the Big Data framework can help in a complete analysis of information within premises for availability, rising costs, and even tracking the spread of chronic disease.
In Media and Entertainment, Big Data is used to collect, analyze, and get actionable consumer insights. It leverages social media elements, media content, and brings out patterns from real-time analytics to further refine business procedures. The Grand Slam Wimbledon Championship in Tennis uses Big Data to offer sentiment analysis for TV, mobile, and online users in real-time efficiently.
For Higher Education, Big Data was applied across The University of Tasmania, an Australian university, to track the activities of 26000 people and manage their progress. Similarly, it was used to measure a teacher's effectiveness with student's experience for learning, marks obtained, behavior, demographics, and other variables.
In the Manufacturing and Natural resources segment, Big Data can add more capabilities to the supply chain to enhance productivity. Both sectors have a large amount of untapped data with increased volume and velocity. Integrating Big Data technologies can make their system more efficient, reliable, improve the overall quality, and add more profit to the businesses.
Governments have also streamlined various activities using Big Data frameworks. The integration and interoperability of Big Data often create challenges with the public size. The Food and Drug Administration is now utilizing Big Data to check and detect patterns between food-related illnesses and diseases for user behavior and response to multiple variables.
In the Transportation sector, Hadoop has been implemented in managing traffic, creating intelligent transport systems, route planning, and avoiding congestion. Especially for the logistics department, Big Data can be used to track shipments, travel moments, and further, save fuel by adopting best practices and instructions to vehicles.
In Energy and Utilities, a more sophisticated electric grid will be implemented with smart meters to track the reading every 15 minutes. This granular data will help to analyze data from various devices and then mix it with customer feedback to make the system perform better.
In the Retail and Wholesale sectors, Big Data can track user buying behavior and compare them with sale techniques to add more value to the business. Similarly, it can be used for a customer loyalty card, RFID, POS scanners, local events, inventory management, and can even reduce frauds too.
In the Insurance sector, Big Data can track customer insights for simplifying products and predicting behavior from GPS devices, social media interactions, and investment opportunities. Optimized ideas can help with claim management in delivering faster services.
In this Hadoop tutorial, we will learn the study use cases of their implementation and business-specific solutions:
You don't need any degree or a Ph.D. to start learning Hadoop fundamentals. Interested individuals with a primary programming curve can begin their training to embark on a bright career with Big Data. (Although our Hadoop tutorial can help you)
Hadoop courses perfectly suit middle and senior-level management to upgrade their skills. It is especially useful for software developers, architects, programmers, and individuals with experience in Database handling. Also, professionals with background experience in Business Intelligence, ETL, Data Warehousing, mainframe, testing, as well as project managers in IT organizations can broaden their learning with this Hadoop tutorial. Non-IT professionals or freshers with a focus on Big Data careers can directly opt for Hadoop certification to become leaders of tomorrow.
Though any background expertise is not needed, basic knowledge in the following areas will help you to get the best out of Hadoop tutorial in general:
Hadoop runs on a combination of programming languages. For instance, R or Python for analysis, Java for development, etc. However, beginners with a non-IT background or with no programming knowledge can also learn Hadoop from scratch.
Knowledge of SQL is crucial regardless of the role you want in Big Data. One can benefit from prior knowledge of SQL to use newer tools and technologies to apply with datasets towards processing frameworks.
Most of the Hadoop deployments across industries are Linux based; thus, it's helpful to have a prior basic working knowledge of Linux.
Hadoop, as a Big Data framework, provides businesses with the ability to distribute data storage, parallel processing, and process data at higher volume, higher velocity, variety, value, and veracity. HDFS, MapReduce, and YARN are the three major components for this Hadoop tutorial.
Hadoop HDFS uses name nodes and data nodes to store extensive data. MapReduce manages these nodes for processing, and YARN acts as an Operating system for Hadoop in managing cluster resources.
Hadoop is a collection of multiple tools and frameworks to manage, store, the process effectively, and analyze broad data. HDFS acts as a distributed file system to store large datasets across commodity hardware. YARN is the Hadoop resource manager to handle a cluster of nodes, allocate RAM, memory, and other resources depending on the application requirements.
MapReduce handles the data processing, Sqoop for transferring data from the current Hadoop database, and other external databases, Flume for data collection and indigestion tool, Pig as script framework, Hive for querying through distributed storage, Spark for real-time data processing and analyzing, Mahout for algorithms, and Apache Ambari for real-time tracking.
Hadoop cluster setup on ubuntu requires a lot of software to work together. First of all, you need to download the Oracle VM box and the Linux disc image to start with a virtual software setting up a cluster. You must carefully select precise configurations for RAM, dynamically allocate for hard disk, bridge adapter for Network, and install ubuntu.
You must then download and install Cloudera QuickStart VM for choosing a Virtual box as the option. Now, download Oracle JDK 1.8 and compatible Hadoop package to install them on your system. Once completed, you can check the current Hadoop version by using a command (source .bashrc).
Hadoop architecture has four essential components that offer support for parallel processing in storing humongous data with a node system. Hadoop HDFS for storing data in multiple slave machines, Hadoop YARN in managing resources across a cluster of machines, Hadoop MapReduce to process and analyze distributed data, and Zookeeper to sync the system across multiple hardware. Hadoop architecture is the basis for understanding this Big Data framework and generating actionable insights to help businesses scale in the right direction.
Hadoop Distributed File System (HDFS) offers comprehensive support for huge files. HDFS can manage data in the size of petabytes and zettabytes data. HDFS comes packed with the ability to write or read terabytes of data per second, distribute data across multiple nodes in a single seek operation, and come at zero licensing costs. HDFS can work on heterogeneous platforms, support large datasets in batches, scan millions of rows, and has a significant very high fault-tolerance.
Built specifically for separating the processing engine and management function in MapReduce, YARN is Hadoop's resource manager. YARN is responsible for monitoring and managing workloads, bringing availability features in Hadoop, maintaining a multi-tenant environment, and applying security controls throughout the system. YARN infrastructure provides resources for executing applications. The MapReduce framework runs on YARN to divide functionalities with resource management and job scheduling for comprehensive monitoring.
MapReduce is the primary processing engine of Hadoop. The MapReduce programming model is based on two phases as Mapping and Reducing. Mapping classifies data into nodes, and the Reducer class generates the final product by aggregating and reducing the output. It can process and compute significantly large volumes of data. MapReduce helps businesses determine costs for their products to reap profits, weather predictions, twitter trending topics, web clicks, advertising models, and explore new opportunities.
In the Hadoop tutorial, Pig is the leading scripting platform to process and analyze Big Datasets. It can use structured and unstructured data to get actionable insights and then stores the result in HDFS. Pig has two essential components; first, a Pig Latin script language along with a runtime engine to process and analyze MapReduce programs. Pig operates in three stages first by loading data and writing script, then Pig operations, and then execution of the plan. Pig offers its support to the data model as Atom, Tuple, Bag, and map in different forms.
Acting as a Data warehouse software, Hive uses SQL like language, HiveQL, for querying through distributed databases. There are mainly two Hive data types; first, as Primitive data types with numeric, string, date/time, and miscellaneous data types, and secondary Complex data types include arrays, maps, structs, and units. Similarly, the Hive has two differences with Local Mode and Mapreduce Mode. Hive architecture first performs a compiler for checking and analyzing, then optimizes with MapReduce and HDFS tasks and executors to accomplish the query. In this Hadoop Tutorial section, Hive Data modeling comprises Tables, Partitions, and Buckets.
Modeled on Google's Bigtable, HBase is a complete storage system built with the primary aim of managing billions of rows and millions of columns across community hardware. HBase enables data to store in tabular form, thus making it exceptionally easy for fast reads and writes. HBase doesn't use a fixed schema and can work with both structured and semi-structured streams of data. Regions and Zookers are the two main architectural components of HBase. HBase has been implemented across several global organizations, including Yahoo, Twitter, Facebook, and Abode.
Sqoop acts as a tool or medium to load data from any external relational database management system (RDBMS) to the Hadoop system and then further to export to RDBMS, respectively. Sqpoop comes packed with exclusive features like parallel import/export, import results of an SQL query, Connectors among RDBMS, Kerberos security integration, and complements as an incremental and full load. Sqoop architecture offers ease of import and export using commands and is quite straightforward to implement.
Prepare for your Hadoop interview with these top 80 Hadoop interview questions and answers to begin your career as a Hadoop developer. These questions can help you understand the crux of the Hadoop tutorial and framework full of tricks and mastery. Questions are based on a different set of levels with beginner, intermediate, and advanced learning. Make sure to go through answers and test your skills to master this course, as well as increase your chances of successfully passing the interview.
Hadoop is a modern-day solution for handling a substantial amount of data efficiently. Big data also brought several challenges in storing, processing, and analyzing raw information. Combining multiple open source utilities, Hadoop acts as a framework to use distributed storage and parallel processing in controlling Big data. Comprising three main components with HDFS as storage, MapReduce as processing, and YARN as resource management, Hadoop has been successfully implemented across multiple industry verticals. Hadoop Ecosystems also consist of various fundamental tools and technologies across a complete Big Data life cycle such as Hive, Impala, Spark, HBase, Pig, Sqoop, - and you can learn all of it here, in this Hadoop tutorial.
You can know more about installation, ecosystems, components, architecture, working, and managing Big Data with details in the next lessons. Each lesson offers a step-by-step learning curve for this Hadoop tutorial to familiarize yourself with Hadoop's fundamentals.
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