Big Data Hadoop
iiTworkforce offer Big Data Hadoop live project training from industry experts
What is Big Data Hadoop?
Until recently, the importance of the data was not recognized. A tremendous amount of data was discarded by stores, hospitals, transport, stock exchange, NGOs, government organizations, research organizations, social media sites, and companies due to lack of information on how data can lead to groundbreaking discoveries. It was in 2005 when the term ‘Big Data’ was first coined by Roger Magoulas of O’Reilly media.
The term Big Data translates to its literal meaning; a colossal amount of data that is impossible to handle or to make any meaningful interpretations from using traditional business intelligence tools. That was when Hadoop evolved. In 2006, Hadoop was originally created by Yahoo on top of Google’s Map Reduce.
Benefits of Big Data:
Big Data is large datasets of information which has proved to be of immense value in performing in-depth analysis. A few of the examples are listed below to understand the uses of Big Data.
- Based on past customer purchases, retail stores like Walmart can build recommendation systems and discounts on the items they are likely to purchase.
- The banking sector, with the help of past trends, is able to identify probable defaulters before issuing loans.
- The healthcare industry and insurance companies are relying on Big Data for disease diagnosis despite faulty testing practices.
Challenges of Big Data:
Up to 2003, when a majority of data was not saved, the entire set of data amounted to 5 billion gigabytes. This is equivalent to the amount of data collected every ten minutes in 2013. And yet, there is no stopping the massive amount of data produced every minute today. Naturally, containing such an immense quantity of data is challenging. The biggest challenges associated with Big Data are the following:
- Capturing the data
- Storing the data
- Analyzing the data
Other challenges with big data pose to be sharing, transferring, and presenting it.
What does Big Data include?
Big Data is broadly defined as 6 V’s, namely Volume, Value, Velocity, Variety, Veracity (authenticity), and Variability based on its features. Big Data can be classified into 3 types:
- Structured Data – Relational Data
- Semi-Structured Data – XML Data
- Unstructured Data – Voice, audio, text, logs, time
How does Hadoop help in managing Big Data?
The biggest challenge associated with Big Data is the non-uniform data. The collected data over various platforms could be structured, semi-structured or unstructured like texts, videos, audios, pictures, Facebook posts, etc which the RDBMS (Relational DataBase Management System) cannot handle. This led to the emergence of newer database technologies like Hadoop.
Hadoop is a framework that allows the enormous amount of unconventional data to be stored through a distributed management system and can simultaneously process it. Hadoop encompasses two components – Hadoop Storage (HDFS), where any unstructured data can be dumped among the clusters, and Hadoop YARN, where the resource management is handled.
Hadoop Distributed File System (HDFS) is considered to be the most reliable and fault-tolerant database management solution. Though at large HDFS appears to be a single unit where one can dump all kinds of data, it inherently follows a distributed management of data. It pursues the master-slave architecture where the NameNode acts as the master daemon which stores the information about the DataNodes where the actual data is stored.
- Stores the information about the DataNodes – metadata, size of files, permissions, hierarchy, etc.
- Maintains the heartbeats of the DataNodes and block report.
- Stores the actual data.
- Sends heartbeat to NameNode in every 3 seconds by default.
Advantages of HDFS to overcome the challenges of Big Data:
HDFS caters to meet the demand for a large amount of data storage:
The HDFS is considered to be the most trusted database management solution due to its ability to replicate the data blocks. The default replication factor is 3, which means there are three copies of each data block. Each NameNode receives heartbeat information from its respective DataNode in 3 seconds by default. In any case, if the DataNode fails to send the information for a specific period due to network failure or power failure, that DataNode is assumed to be dead and another copy of DataNode is made to maintain the set number of 3 copies.
By default, each data block can store 128 MB of data, which means if there is 712 MB of data, it creates 6 blocks of data across different DataNodes. Each DataNode is again replicated by default 3 times, which means for 712 MB of data there will be 18 DataNodes.
The HDFS solves the scaling problem through horizontal scaling/scale-out method instead of vertical scaling/scale-up where more nodes are added to the clusters to handle the increasing demand for resources.
HDFS allows the storage of unstructured data.
Unlike RDBMS technology, the HDFS does not need the schema for storing data and it supports once-write ready-many times structure. This enables an in-depth analysis of data.
HDFS resolves the slower processing speed due to the huge amount of data:
HDFS employs a reverse approach for processing the data, where the data is not called for processing, but the processing is moved to the data. The processing code is sent via MapReduce to the DataNodes to process the data instead of calling the DataNodes. The processed results are then merged with the NameNodes and the response is sent back.
The YARN is the resources management component of Hadoop. The handling is done with the help of ResourceManagers and NodeManagers. Again, the ResourceManager is the master node and the NodeManager becomes the slave node. The NodeManagers are installed on the DataNodes. The ResourceManager receives the processing request and in turn, passes it to the corresponding NodeManager.
How does Big Data Hadoop help in Data Analysis?
Hadoop is an open-source distributed storage and processing framework which has the potential to manage and analyze the growing Big Data ecosystem. It is Hadoop that can help in performing the behavioral, predictive analysis of both structured and unstructured data.
Hadoop with its top tools like Spark, MapReduce, Apache Impala, Apache Hive, and Mahout, executes complex data processing and analysis.
- Hadoop with Map Reduce is considered to be ideal for data searching, streaming, data reporting, and indexing the files.
- Mahout helps in the analysis of large datasets in the least amount of time.
- Impala processes the data effortlessly.
- Spark, the successor of MapReduce is becoming the default data execution engine on both batch and streaming data.
What are the advantages of taking the Big Data Hadoop course?
Big Data Hadoop is becoming the most sought-after course with the rising demand for data analytics and AI.
- Large companies like Facebook, LinkedIn, and Yahoo use the Hadoop data management system for storing and processing their data.
- Popular companies like Microsoft, IBM, Oracle, SAP, Dell, etc are investing in data management and analytics, which translates to mass hiring of Big Data Hadoop certified professionals.
- The salary package of a Hadoop Administrator is higher than an average software professional.
- With the amount of Big Data growing every minute and its importance now discovered, the demand for Hadoop engineers and Administrators has increased multifold.
What topics are covered in the Big Data Hadoop course?
A typical 40-hours Big Data Hadoop course encompasses the following modules:
- HDFS: Hadoop Distributed File System
- SPARK WITH SCALA
Who is eligible for the Big Data Hadoop real-time project?
Enrolling for Big Data Hadoop real-time project opens doors for countless possibilities in the field of Data analytics.
To be a part of real-time project-based training in Big Data Hadoop technology, the learner should have the knowledge of HDFS, and the tools of Hadoop – Spark, MapReduce, Hive, PIG.
However, beginners can enroll at www.h2kinfosys.com for Big Data Hadoop Certification Course.
What are the advantages of doing a real-time Big Data Hadoop Project?
With the immense potential of Hadoop to work best with Big Data, the opportunities to bag a high-paying job in the field of data analytics are multiplied after the completion of a real-time Big Data Hadoop project.
- The intern will learn to implement the latest algorithms and effective strategies and techniques in data analytics.
- The intern will catch up with the industrial needs with more confidence and intellect.
- Every intern will learn to collaborate with different teams involved in the process of data analysis.
- Every bit of real-time project experience counts and adds value to the trainee’s resume.
- The hands-on knowledge of Big Data Hadoop helps in cracking interviews with flying colors.
What will you learn in the real-time project-based training in Big Data Hadoop?
The real-time project in Big Data Hadoop can help the learners in becoming Big Data Hadoop engineers/Administrators/consultants etc.
They witness the real-time scenarios in development, administration, designing and application of various Hadoop tools and HDFS for data analysis. They will have an understanding of the following by the end of the project:
- Hadoop Architecture
- Role of Hadoop components
- Hadoop Ecosystem
- Integration of R/Python/Spark with Hadoop
- Integrating NoSQL with Hadoop
- Various career opportunities in the Hadoop ecosystem like Developer, Administrator.
What can you expect by the end of the Big Data Hadoop project?
The real-time Big Data Hadoop project-based training is vital for any aspiring Hadoop consultant as it bridges the gap between classroom learning and hands-on practice. After the project, every intern will be able to vouch for the complete understanding of various Hadoop tools and its applications.
All the interns would have witnessed the various phases of the data analysis project and how the various teams come together to deliver the end result.
The real-time project translates to job experience on the resume. It pumps the confidence levels of the intern and aids in cracking crucial job interviews with ease.
What happens after completion of the Big Data Hadoop real-time project?
On successful completion of the Big Data Hadoop real-time project, the Project Lead conducts a one on one interaction with the intern to provide them with insights on a resume-building exercise. The resume experts at iiT WorkForce put together the intern’s resume and follow up with a mentoring session by the trainers.
Once the intern is adept at all the Hadoop concepts, the interviews are lined up based on the skill sets of the intern.
All our projects come with a 100% placement guarantee.