At the crux of data analysis is the ability to decipher raw data, process it and arrive at meaningful and actionable insights that can shape business strategies. According to the latest research, nearly 2.5 quintillion bytes of data is created every day, and the number is slowly edging upwards. The storage and processing power needed to handle these large volumes of data cannot be handled in an efficient manner with traditional frameworks and platforms. So, there arose a need to explore distributed storages and parallel processing operations in order to understand and make sense of these large volumes of data or big data. Hadoop by Apache provides the much-needed power that is required to manage such situations to handle Big Data. Based on data produced by Wanted analytics it was found out that the top five industries hiring Big Data related expertise include Professional, Scientific and Technical Services (25%), Information Technology (17%), Manufacturing (15%), Finance and Insurance (9%) and Retail Trade (8%).
Simply put, big data would be the problem and Hadoop would be one of the solutions leveraged to make sense of it. With the inclusion of a much needed HDFS component, the distributed storage problem is taken care of while the MapReduce component optimizes parallel data processing. According to Gartner data, nearly 26% of the analysts are leveraging Hadoop in their daily tasks which makes it imperative to learn the platform and stay ahead of the curve. In addition to its ability to handle concurrent tasks, Hadoop is scalable and cost-effective as well, making the lives of analysts much easier than before.
With most businesses facing a data deluge, the Hadoop platform helps in processing these large volumes of data in a rapid manner, thereby offering numerous benefits at both the organization and individual level.
Undergoing training in Hadoop and big data is quite advantageous to the individual in this data-driven world:
Training in Big Data and Hadoop has certain organizational benefits as well:
Given the ease with which it allows you to make sense of huge volumes of data and leverage frameworks to transform the same into actionable insights, training and certification courses for Hadoop & Big Data are in great demand in the field of data science.
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Understand what Big Data is and gain in-depth knowledge of Big Data Analytics concepts and tools.
Learn to Process large data sets with Big Data tools to extract information from disparate sources.
Learn about MapReduce, Hadoop Distributed File System (HDFS), YARN, and how to write MapReduce code.
Learn best practices and considerations for Hadoop development as well as debugging techniques.
Learn how to use Hadoop frameworks like ApachePig™, ApacheHive™, Sqoop, Flume, among other projects.
Perform real-world analytics by learning advanced Hadoop API topics with an e-courseware.
Before undertaking a Big Data and Hadoop course, a candidate is recommended to have a basic knowledge of programming languages like Python, Scala, Java and a better understanding of SQL and RDBMS.
Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.
Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the latest training!
Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Get reviews and feedback on your final projects from professional developers.
This module will introduce you to the various concepts of big data analytics, and the seven Vs of big data—Volume, Velocity, Veracity, Variety, Value, Vision, and Visualization. Explore big data concepts, platforms, analytics, and their applications using the power of Hadoop 3.
Hands-on: No hands-on
Here you will learn the features in Hadoop 3.x and how it improves reliability and performance. Also, get introduced to MapReduce Framework and know the difference between MapReduce and YARN.
Hands-on: Install Hadoop 3.x
Learning Objectives: Learn to install and configure a Hadoop Cluster.
Hands-on: Install and configure eclipse on VM
Learn about various components of the MapReduce framework, and the various patterns in the MapReduce paradigm, which can be used to design and develop MapReduce code to meet specific objectives.
Hands-on :Use case - Sales calculation using M/R
Learn about Apache Spark and how to use it for big data analytics based on a batch processing model. Get to know the origin of DataFrames and how Spark SQL provides the SQL interface on top of DataFrame.
Look at various APIs to create and manipulate DataFrames and dig deeper into the sophisticated features of aggregations, including groupBy, Window, rollup, and cubes. Also look at the concept of joining datasets and the various types of joins possible such as inner, outer, cross, and so on
Understand the concepts of the stream-processing system, Spark Streaming, DStreams in Apache Spark, DStreams, DAG and DStream lineages, and transformations and actions.
Hands-on: Process Twitter tweets using Spark Streaming
Learn to simplify Hadoop programming to create complex end-to-end Enterprise Big Data solutions with Pig.
Learn about the tools to enable easy data ETL, a mechanism to put structures on the data, and the capability for querying and analysis of large data sets stored in Hadoop files.
Look at demos on HBase Bulk Loading & HBase Filters. Also learn what Zookeeper is all about, how it helps in monitoring a cluster & why HBase uses Zookeeper.
Learn how to import and export data between RDBMS and HDFS.
Understand how multiple Hadoop ecosystem components work together to solve Big Data problems. This module will also cover Flume demo, Apache Oozie Workflow Scheduler for Hadoop Jobs.
Learn to constantly make sense of data and manipulate its usage and interpretation; it is easier if we can visualize the data instead of reading it from tables, columns, or text files. We tend to understand anything graphical better than anything textual or numerical.
Hands-on: Use Data Visualization tools to create a powerful visualization of data and insights.
Learn a simple way to access servers, storage, databases, and a broad set of application services over the internet.
Hands-on: Implement Cloud computing and deploy models.
Aadhar card Database is the largest biometric project of its kind currently in the world. The Indian government needs to analyse the database, divide the data state-wise and calculate how many people are still not registered, how many cards are approved and how they can bifurcate it according to gender, age, location, etc.
The Citi group of banks is one of the world’s largest providers of financial services, In recent years, they adopted a fully Big Data-driven approach to drive business growth and enhance the services provided to customers because traditional systems are not able to handle the huge amount of data pouring in. Using Hadoop, they will be storing and analyzing banking data to come up with multiple insights.
On Ecommerce Web sites, clickstream analysis is the process of collecting, analyzing and reporting aggregate data about which pages a website visitor visits and in what order. With increasing number of ecommerce businesses, there is a need to track and analyse clickstream data. When using traditional databases to load and process clickstream data, there are several complexities in storing and streaming customer information and it also requires a huge amount of processing time to analyse and visualize it.
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Hadoop has now become the de facto technology for storing, handling, evaluating and retrieving large volumes of data. Big Data analytics has proven to provide significant business benefits and more and more organizations are seeking to hire professionals who can extract crucial information from structured and unstructured data. KnowledgeHut brings you a full-fledged course on Big Data Analytics and Hadoop development that will teach you how to develop, maintain and use your Hadoop cluster for organizational benefit.
This course will prepare you for everything you need to learn about Big Data while gaining practical experience on Hadoop.
After completing our course, you will be able to understand:
There are no restrictions but participants would benefit if they have elementary computer knowledge.
Yes, KnowledgeHut offers this training online.
Your instructors are Hadoop experts who have years of industry experience.
Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details: https://www.knowledgehut.com/refund
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What is BIG DATA & HADOOP? In the current trend of continually advancing technologies, companies hold an extensive amount of complex data that cannot be processed using conventional or ordinary applications. These vast complex data pose many challenges, namely; analysis of data, the capture of data, data curation, storage of data, transfer, visualisation, and information privacy. Big data provides us with advanced methods to analyse, process, curate, extract valuable information from data and sort them into a particular size of data. Talking about Hadoop, Hadoop is Java-based programming. It is an open and a free framework, that allows & supports, processing of vast amount of data sets in a distributed computing system. Every aspiring new-age developer, with an avid thirst to ace the web world, needs an in-depth training in Big Data and Hadoop. Acquire skills that will take you a level abovebig data and hadoop Certification in melbourne
Big data should have high precision to support decision making, which in turn helps to increase operational efficiency, better results, lesser risk, and cost reduction. Employing of Hadoop makes it feasible to run applications on systems with hundreds and thousands of nodes involving hundreds and thousands of terabytes. Hadoop developed and structured file system helps in rapid data transfer rates among nodes. This helps to lower the risk of system failure.
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