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Best Hadoop Online Training Institutes In Hyderabad
Best Hadoop Online Training In Hyderabad
Big Data Hadoop Certification Training : It is a comprehensive Hadoop Big Data training course designed by industry experts considering current industry job requirements to provide in-depth learning on big data and Hadoop Modules. This is an industry recognized Big Data certification training course that is a combination of the training courses in Hadoop developer, Hadoop administrator, Hadoop testing, and analytics. This Cloudera Hadoop training will prepare you to clear big data certification.
What you will learn in this Big Data Hadoop online training Course?
- Master fundamentals of Hadoop 2.7 and YARN and write applications using them
- Setting up Pseudo node and Multi node cluster on Amazon EC2
- Master HDFS, MapReduce, Hive, Pig, Oozie, Sqoop, Flume, Zookeeper, HBase
- Learn Spark, Spark RDD, Graphx, MLlib writing Spark applications
- Master Hadoop administration activities like cluster managing,monitoring,administration and troubleshooting
- Configuring ETL tools like Pentaho/Talend to work with MapReduce, Hive, Pig, etc
- Detailed understanding of Big Data analytics
- Hadoop testing applications using MR Unit and other automation tools.
- Work with Avro data formats
- Practice real-life projects using Hadoop and Apache Spark
- Be equipped to clear Big Data Hadoop Certification.
Who should take this Big Data Hadoop Online Training Course?
- Programming Developers and System Administrators
- Experienced working professionals , Project managers
- Big DataHadoop Developers eager to learn other verticals like Testing, Analytics, Administration
- Mainframe Professionals, Architects & Testing Professionals
- Business Intelligence, Data warehousing and Analytics Professionals
- Graduates, undergraduates eager to learn the latest Big Data technology can take this Big Data Hadoop Certification online training
Apache Spark online training institute in hyderabad
Apache Spark is a lightning-fast cluster computing technology, designed for fast computation. It is based on Hadoop MapReduce and it extends the MapReduce model to efficiently use it for more types of computations, which includes interactive queries and stream processing. The main feature of Spark is its in-memory cluster computing that increases the processing speed of an application.
Spark is designed to cover a wide range of workloads such as batch applications, iterative algorithms, interactive queries and streaming. Apart from supporting all these workload in a respective system, it reduces the management burden of maintaining separate tools.
Spark is one of Hadoop’s sub project developed in 2009 in UC Berkeley’s AMPLab by Matei Zaharia. It was Open Sourced in 2010 under a BSD license. It was donated to Apache software foundation in 2013, and now Apache Spark has become a top level Apache project from Feb-2014.
Apache Spark has following features.
- Speed − Spark helps to run an application in Hadoop cluster, up to 100 times faster in memory, and 10 times faster when running on disk. This is possible by reducing number of read/write operations to disk. It stores the intermediate processing data in memory.
- Supports multiple languages − Spark provides built-in APIs in Java, Scala, or Python. Therefore, you can write applications in different languages. Spark comes up with 80 high-level operators for interactive querying.
- Advanced Analytics − Spark not only supports ‘Map’ and ‘reduce’. It also supports SQL queries, Streaming data, Machine learning (ML), and Graph algorithms.
Best hadoop online training institute in hyderabad
Big data is really critical to our life and its emerging as one of the most important technologies in modern world. Follow are just few benefits which are very much known to all of us:
- Using the information kept in the social network like Facebook, the marketing agencies are learning about the response for their campaigns, promotions, and other advertising mediums.
- Using the information in the social media like preferences and product perception of their consumers, product companies and retail organizations are planning their production.
- Using the data regarding the previous medical history of patients, hospitals are providing better and quick service.
Big data technologies are important in providing more accurate analysis, which may lead to more concrete decision-making resulting in greater operational efficiencies, cost reductions, and reduced risks for the business.
To harness the power of big data, you would require an infrastructure that can manage and process huge volumes of structured and unstructured data in realtime and can protect data privacy and security.
There are various technologies in the market from different vendors including Amazon, IBM, Microsoft, etc., to handle big data. While looking into the technologies that handle big data, we examine the following two classes of technology:
This include systems like MongoDB that provide operational capabilities for real-time, interactive workloads where data is primarily captured and stored.
NoSQL Big Data systems are designed to take advantage of new cloud computing architectures that have emerged over the past decade to allow massive computations to be run inexpensively and efficiently. This makes operational big data workloads much easier to manage, cheaper, and faster to implement.
Some NoSQL systems can provide insights into patterns and trends based on real-time data with minimal coding and without the need for data scientists and additional infrastructure.
Analytical Big Data
This includes systems like Massively Parallel Processing (MPP) database systems and MapReduce that provide analytical capabilities for retrospective and complex analysis that may touch most or all of the data.
MapReduce provides a new method of analyzing data that is complementary to the capabilities provided by SQL, and a system based on MapReduce that can be scaled up from single servers to thousands of high and low end machines.
hadoop architect online training
Hadoop consists of the Hadoop Common package, which provides file system and operating system level abstractions, a MapReduce engine (either MapReduce/MR1 or YARN/MR2) and the Hadoop Distributed File System (HDFS). The Hadoop Common package contains the Java ARchive (JAR) files and scripts needed to start Hadoop.
For effective scheduling of work, every Hadoop-compatible file system should provide location awareness – the name of the rack (or, more precisely, of the network switch) where a worker node is. Hadoop applications can use this information to execute code on the node where the data is, and, failing that, on the same rack/switch to reduce backbone traffic. HDFS uses this method when replicating data for data redundancy across multiple racks. This approach reduces the impact of a rack power outage or switch failure; if any of these hardware failures occurs, the data will remain available
BigData Online Training
Apache Hadoop’s MapReduce and HDFS components were inspired by Google papers on their MapReduce and Google File System.
The Hadoop framework itself is mostly written in the Java programming language, with some native code in C and command line utilities written as shell scripts. Though MapReduce Java code is common, any programming language can be used with “Hadoop Streaming” to implement the “map” and “reduce” parts of the user’s program. Other projects in the Hadoop ecosystem expose richer user interfaces.