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Pune Hadoop Corporate Workshop

Pune Hadoop Corporate Workshop

What To Expect:

Mode of Delivery – The classes are held both online and in physical classrooms.

Audience – We have a global audience that logs in to work hand in hand with our world-class instructors.

Certification – Available in 25+ Countries NobleProg Certification is accepted globally

What is the potential of Hadoop?
Hadoop is among the major big data technologies and has a vast scope in the future. Being cost-effective, scalable and reliable, most of the world’s biggest organizations are employing Hadoop technology to deal with their massive data for research and production.
It includes storing data on a cluster without any machine or hardware failure, adding a new hardware to the nodes etc.
Several newbies in IT sector often arise a question that what is the scope of Hadoop in the future. Well, it can be traced out by the fact that the availability of tons of data through social networking and other means has been increased and goes on increasing as the world approaches digitalization.
Why should you go for Hadoop?
REASON 1: DATA EXPLORATION WITH FULL DATASETS
Data scientists love their working environment. Whether using R, SAS, Matlab or Python, they always need a laptop with lots of memory to analyze data and build models. In the world of big data, laptop memory is never enough, and sometimes not even close.
A common approach is to use a sample of the large dataset, a large a sample as can fit in memory. With Hadoop, you can now run many exploratory data analysis tasks on full datasets, without sampling. Just write a map-reduce job, PIG or HIVE script, launch it directly on Hadoop over the full dataset, and get the results right back to your laptop.
REASON 2: MINING LARGER DATASETS
In many cases, machine-learning algorithms achieve better results when they have more data to learn from, particularly for techniques such as clustering, outlier detection and product recommenders.
Historically, large datasets were not available or too expensive to acquire and store, and so machine-learning practitioners had to find innovative ways to improve models with rather limited datasets. With Hadoop as a platform that provides linearly scalable storage and processing power, you can now store ALL of the data in RAW format, and use the full dataset to build better, more accurate models.
REASON 3: LARGE SCALE PRE-PROCESSING OF RAW DATA
As many data scientists will tell you, 80% of data science work is typically with data acquisition, transformation, cleanup and feature extraction. This “pre-processing” step transforms the raw data into a format consumable by the machine-learning algorithm, typically in a form of a feature matrix.
Hadoop is an ideal platform for implementing this sort of pre-processing efficiently and in a distributed manner over large datasets, using map-reduce or tools like PIG, HIVE, and scripting languages like Python. For example, if your application involves text processing, it is often needed to represent data in word-vector format using TFIDF, which involves counting word frequencies over large corpus of documents, ideal for a batch map-reduce job.
Similarly, if your application requires joining large tables with billions of rows to create feature vectors for each data object, HIVE or PIG are very useful and efficient for this task.
REASON 4: DATA AGILITY
It is often mentioned that Hadoop is “schema on read”, as opposed to most traditional RDBMS systems which require a strict schema definition before any data can be ingeted into them.
“Schema on read” creates “data agility”: when a new data field is needed, one is not required to go through a lengthy project of schema redesign and database migration in production, which can last months. The positive impact ripples through an organization and very quickly everyone wants to use Hadoop for their project, to achieve the same level of agility, and gain competitive advantage for their business and product line.
Why Is This Program Different:

The Instructors – Our instructors are industry experts, people who have been there and done that. They not only encourage questioning but also give solutions that are practical and applicable at an enterprise level.

The Practice – We provide an actual cluster for hands-on practicing. It removes the need to install virtual machines and makes learning easier and fun.

The Curriculum – Created by industry experts to equip attendees to hit the ground running. Our interactive sessions along with the curated curriculum make starting a project at work or attending an interview or just upscaling your career a cake walk.

Overview
The program is dedicated to IT specialists that are looking for a solution to store and process large data sets in distributed system environment.
Topic Coverage

Introduction to Cloud Computing and Big Data solutions

Apache Hadoop evolution: HDFS, MapReduce, YARN

Installation and configuration of Hadoop in Pseudo-distributed mode

Running MapReduce jobs on Hadoop cluster

Hadoop cluster planning, installation and configuration

Hadoop ecosystem: Pig, Hive, Sqoop, HBase

Big Data future: Impala, Cassandra

Who should take this program?
Anyone having zeal to learn new technology can go for it. Students and professionals aspiring to make a career in Hadoop should opt for the program.

Banking/Finance professionals

Software developers

Corporate Executives looking to connect corproate strategy to technology

Government Executives looking to better understand opportunities

High school & college students

Supply Chain Managers

CEO’s, Boards, and Senior VP’s

Entrepreneurs looking for something new

Consultants and Professional Service Providers

Technology Enthusiasts

Anyone looking to better prepare for long term career potential in the future

For any enquiries you can always reach us at dHJhaW5pbmcgfCBub2JsZXByb2cgISBpbg== or call us at +91 88 001 555 18, +91 98 18 063 614

Pune Hadoop Corporate Workshop was last modified: by
June 14 2018

Details

Date: June 14
Time: 11:30 am
Event Categories: ,

Venue

NobleProg, Mundhwa Road,, Pune, India

India