The Big Data and Hadoop training in Lucknow gives you vital knowledge of Big Data’s framework. This hands-on Big Data and Hadoop course in Lucknow uses tools in its Integrated Labs, such as Hadoop and Spark in relevant industry tasks. The Big Data and Hadoop course in Lucknow provides marketable experience in handling Big Data.
Lifetime access to self-paced e learning content
By signing up for Simplilearn's Big Data and Hadoop Training in Lucknow, learners are taking advantage of a growing field. For example, in 2019, the HADOOP-AS-A-SERVICE (HAAS) market has climbed as high as USD 7.35 billion. Experts say this market will grow at a CAGR of 39.3%, reaching USD 74.84 Billion by 2026. That’s why you need Big Data and Hadoop Training in Lucknow.
Simplilearn gives you a course completion certificate when you finish the Big Data and Hadoop course in Lucknow. To earn the CCA175 - Spark and Hadoop certificate from Cloudera, you must pass a separate exam. The Big Data and Hadoop training in Lucknow prepares you for that exam.
The Big Data and Hadoop training in Lucknow provides enrollees with technical knowledge of the Hadoop environment, and teaches the Big Data tools and practical methodologies employed by big data engineers. Simplilearn’s course completion certification calls attention to your newly acquired Big Data skills and related on-the-job, hands-on expertise. Graduates will emerge from Big Data and Hadoop training in Lucknow knowing how to use Hadoop’s ecosystem tools, including HBase, Flume, HDFS, Hive, Kafka, MapReduce, and others.
There are no prerequisites for learning this course. However, knowledge of Core Java and SQL will be beneficial, but certainly not a mandate. If you wish to brush up your Core-Java skills, Simplilearn offers a complimentary self-paced course "Java essentials for Hadoop" when you enroll for this course. For Spark, this course uses Python and Scala, and an e-book is provided to support your learning.
Online Classroom: Big Data and Hadoop training in Lucknow learners have to attend one full batch, need to bring one project to fruition, and have to pass a simulation test with a score of 80% or higher.
Online Self-learning: At a minimum, learners are required to finish 85% of the Big Data and Hadoop course in Lucknow, guide a project to completion, and score 80% or more on a simulation test.
Successful completion of the Big Data and Hadoop training in Lucknow takes from 45 to 50 hours.
Simplilearn provides you with support and guidance for taking the CCA175 Hadoop certification exam. This Big Data and Hadoop training in Lucknow supports students with the practical instruction they need to pass the certification test on their first try. But in the event that you do fail, you’ll receive three more attempts to successfully ace the exam.
The global Big Data and data engineering services market is expected to grow at a CAGR of 31.3 percent by 2025, so this is the perfect time to pursue a career in this field.
The world is getting increasingly digital, and this means big data is here to stay. The importance of big data and data analytics is going to continue growing in the coming years. Choosing a career in the field of big data and analytics might be the type of role that you have been trying to find to meet your career expectations. Professionals who are working in this field can expect an impressive salary, the median salary for a data engineer is $137,776, with more than 130K jobs in this field worldwide. As more and more companies realize the need for specialists in big data and analytics, the number of these jobs will continue to grow. A role in this domain places you on the path to an exciting, evolving career that is predicted to grow sharply into 2025 and beyond.
According to Forbes, Big Data & Hadoop Market is expected to reach $99.31B by 2022.
This Big Data Hadoop Certification course is designed to give you an in-depth knowledge of the Big Data framework using Hadoop and Spark, including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop, Flume, and Kafka for data ingestion with our significant data training.
You will master Spark and its core components, learn Spark’s architecture, and use Spark cluster in real-world - Development, QA, and Production. With our Big Data Hadoop course, you will also use Spark SQL to convert RDDs to DataFrames and Load existing data into a DataFrame.
As a part of the Big Data Hadoop course, you will be required to execute real-life, industry-based projects using Integrated Lab in the domains of Human Resource, Stock Exchange, BFSI, and Retail & Payments. This Big Data Hadoop training course will also prepare you for the Cloudera CCA175 significant Big Data certification exam.
Big Data Hadoop certification training will enable you to master the concepts of the Hadoop framework and its deployment in a cluster environment. By the end of this course, you will be able to:
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology in Big Data architecture. Big Data training is best suited for IT, data management, and analytics professionals looking to gain expertise in Big Data, including:
The Big Data Hadoop Training course includes four real-life, industry-based projects. Following are the projects that you will be working on:
Project 1: Analyzing employee sentiment
Objective: To use Hive features for data analysis and sharing the actionable insights into the HR team for taking corrective actions.
Domain: Human Resource
Background of the problem statement: The HR team is surfing social media to gather current and ex-employee feedback or sentiments. This information gathered will be used to derive actionable insights and take corrective actions to improve the employer-employee relationship. The data is web-scraped from Glassdoor and contains detailed reviews of 67K employees from Google, Amazon, Facebook, Apple, Microsoft, and Netflix.
Project 2: Analyzing Intraday price changes
Objective: To use hive features for data engineering or analysis and sharing the actionable insights.
Domain: Stock Exchange
Background of the problem statement: NewYork stock exchange data of seven years, between 2010 to 2016, is captured for 500+ listed companies. The data set comprises of intra-day prices and volume traded for each listed company. The data serves both for machine learning and exploratory analysis projects, to automate the trading process and to predict the next trading-day winners or losers.. The scope of this project is limited to exploratory data analysis.
Project 3: Analyzing Historical Insurance claims
Objective: To use the Hadoop features for data engineering or analysis of car insurance, share patterns, and actionable insights.
Domain: BFSI
Background of the problem statement: A car insurance company wants to look at its historical data to understand and predict the probability of a customer making a claim based on multiple features other than MVR_POINTS. The data set comprises 10K plus submitted claim records and 14 plus features. The scope of this project is limited to data engineering and analysis.
Project 4: Analyzing Product performance
Objective: To use the Big data stack for data engineering for the analysis of transactions, share patterns, and actionable insights.
Domain: Retail & Payments
Background of the problem statement: Amazon wants to launch new digital marketing campaigns for various categories for different brands to come up with new Christmas deal to:
1. Increase their sales by a certain percentage.
2. Promote products which are the least selling
3. Promote products which are giving more profits
They have provided a transactional data file that contains historical transactions of a few years along with product details across multiple categories. As an analytics consultant, your responsibility is to provide valuable product and customer insights to the marketing, sales, and procurement teams. You have to preprocess unstructured data into structured data and provide various statistics across products or brands or categories segments and tell which of these segments will increase the sales by performing well and, which segments need an improvement. The scope of this project is limited to data engineering and analysis.
The field of big data and analytics is a dynamic one, adapting rapidly as technology evolves over time. Those professionals who take the initiative and excel in big data and analytics are well-positioned to keep pace with changes in the technology space and fill growing job opportunities. Some trends in big data include: