Hadoop (Big Data) is one of the courses provided by Technogeeks. When you look for training Hadoop (Big Data), you need to choose an institute which is providing complete real-time based training and here is Technogeeks which provides you the whole course training which is “Practical” Oriented. We provide best Hadoop (Big Data) training in Pune by Prince Arora Trainer, who is really helping people to get train and work on Hadoop.
Technogeeks also provide FREE Technical Seminar on Hadoop every Saturday. Will also provide some brief Idea about Hadoop and Its components so that if you are planning to learn Hadoop, then you can get some brief idea before that.
It is an open-source software framework used for distributed storage. To process data set of big data it uses map reduce programming model. It is an open source tool from Apache which assures that its codes are easily available.
It is used for distributed storage and it does processing on dataset of Big Data. It is used for distributed storage.
It possesses computer clusters built from commodity hardware. It assumes hardware failures as a common thing & accordingly module are designed in it.
Extremely large sets of Data are called Big Data. It exists in various forms and in numerous sizes. It can vary from small data to very big Data. Extremely large sets of Data are called Big Data.
It cannot be accommodated in Hard disk or in a single system & hence it is called as Big Data. Its size is larger than 1000s of GBs.
Technogeeks provides the following components in this course:
Pig: It is a high- level language platform to analyze and query the tremendous dataset stored in HDFS. Language used in Pig is known as Pig Latin which resembles with SQL. Its use is for data loading, applying the necessary filters and dumping the data in the required format.
Pig was created to simplify the burden of writing complex Java codes to perform MapReduce jobs. Earlier Hadoop developers had to write complex java code for performance of data analysis.
To perform analysis using Apache Pig, it is necessary for the programmers to write scripts using Pig Latin language to process data stored in HDFS. Internally all these scripts get converted to Map and Reduce tasks.
HIVE: Apache Hive is a solution for data warehousing for Hadoop which provides data summarization, runs ad- hoc queries, and ad-hoc analysis. It runs Ad-hoc queries for the data analytics. Just submission of SQL queries is enough without writing complex map reduce jobs. It is used to process structured and semi-structured data in Hadoop. It supports analysis of large datasets stored in HDFS and also in Amazon S3 files system. Name of its query language is HiveQL.
Hue: It provides UI for the file system, Pig, Hive, job browser, the file system and basically everything in the big data domain. It provides UI for the Hive, file system, Pig, the file system, job browser, and generally everything in the big data domain. It’s saved queries one can directly run by specifying the parameters. It is an open source user interface for Hadoop components. Hue right is accessible from within the browser which enhances the Hadoop developers’ productivity. No necessity for users to use command line interface for Hadoop’s use.
Hadoop Distributed File System (HDFS): It’s a distributed file-system. It stores data on commodity machines. It is a part of Apache Hadoop project. It is the world’s most reliable storage system. Its design is to storing large file and it provides high throughput.
Whenever any file has to be written in HDFS, it is broken into small pieces of data known as blocks. HDFS has a default block size of 128 MB which can be increased as per the requirements.
Flume: This framework is populating Hadoop with data. It is a configurable tool. Agents are populated inside web servers, mobile devices, application servers and, for example, for data collection and its integration into Hadoop. It collects, aggregates and transports streaming data like events, log files, etc., from various sources to a centralized data store. It is reliable as well as highly distributed.
Spark & Scala: Spark is a library. It makes possible parallel computation via function calls. Apache Spark is a fast cluster computing technology for fast computation. It is based on Hadoop MapReduce. It also uses MapReduce model for more types of computations, which includes interactive queries and stream processing. Scala is a freeware software application. It has versions supporting Linux, Windows, and OSX. Creation of musical scales and its archive is possible along with its analysis.
AWS Integration: Using Hadoop on AWS platform increases agility of organizations by reducing the cost and time it takes to allocate resources for experimentation and development. Amazon EMR addresses Hadoop infrastructure requirements as it is a managed service & so one can focus on core business there by avoiding complications of Hadoop configuration, networking, server installation, security configuration, and ongoing administrative maintenance. Hadoop environment can be integrated with other services such as Amazon S3, Amazon DynamoDB, Amazon Redshift, and Amazon Kinesis and to enable data movement, workflows, and analytics across numerous diverse services on the AWS platform.
Tableau Integration: Tableau gives quick & easy access to business users of valuable insights in gigantic Hadoop datasets. Hadoop is another data source to Tableau. Native connectors make linking Tableau to Hadoop easy for which special configuration is not necessary. Tableau also makes working with XML files, unpacking and processing on the fly for true flexibility easier.
Sqoop: It is a command-line interface application which transfers data between Hadoop & relational databases. It is helpful for incremental loads of a single table or a free form SQL query and also saved jobs which can go on multiple times to import updates given to a database since the last import. It is a tool for transference of data between relational database servers & Hadoop. For importation of data from relational databases such as Oracle to Hadoop HDFS, MySQL, and export from Hadoop file system to relational databases.
Yarn Framework: It is a platform responsible for managing computing resources in clusters and using them for scheduling users' applications. Now when the other technologies are evolving yarn extends the power of Hadoop to these other evolving technologies which makes it possible for these technologies to take benefits of HDFS which is very reliable as well as popular storage system and economic cluster. Apache Hadoop Yarn allows various data processing engines like for example: batch processing, stream processing, interactive processing & also graph processing to run and processing of data stored in HDFS.
Map Reduce: It implements large-scale data processing. It is the processing layer of Hadoop. It is designed for processing large volumes of data in parallel by dividing the work into a set of independent tasks. One just needs to put business logic in the way MapReduce works and everything else will be taken care by the framework.
Oozie: Hadoop is highly popular for its ease-of-use in handling tasks related to big data analysis. Big data analysis tasks require multiple jobs to be created in the analysis process. For this an efficient process of job handling is a necessity & here Oozie plays the role. It makes the workflow easier and coordination between various jobs convenient. Oozie is an open source project. By using OOZIE project, users of Hadoop can define different actions or jobs and the inter-dependency between the jobs. After this, Oozie takes over the control of the job scheduling process.
Faculty: Our multidisciplinary faculty are ‘Working Professionals’ from IT companies. These experts have more than Ten years working experience & they are leading research in a variety of areas.
Professional Tie Ups: Technogeeks provides Job Assistance through ‘Resume Preparation’ & by providing openings. Having tie ups with IT companies.
Hadoop training will give you so many career options to perform well, to earn well! And for that “TECHNOGEEKS” is here to help you to start with in Pune Location in India.
So, you can visit and attend Free Seminar on weekends as they usually provide Free Seminar on Hadoop Training in Pune by IT working professionals every weekend.