
Explore big data concepts and Hadoop's role in storing and processing vast data across clusters, with structured, semi-structured, and unstructured data types and actionable insights.
Explore basic Hadoop operations and practice commands for listing, copying, moving, creating directories, navigating, and configuring file permissions across local and distributed environments.
Explore Apache NiFi concepts, including flow files, flow file processors, ports and connections, and flow control, and learn how processors route, transform, and orchestrate data between systems.
Explore Apache NIFI's UI canvas components, including the top toolbar, status bar, operator palette, search, and global menu, plus bird's eye view, breadcrumbs, and templates for data flow navigation.
Explore Apache Solr basic concepts and configuration files, including core, schema, and memory management, and understand distributed Solr cloud terms like node, collection, shard, replica, and leader using ZooKeeper.
Learn how Hive processes a query end-to-end—from the command line driver and decompiler to the compiler, plan, and execution on the execution engine for map reduce job, then fetch results.
Set up hive tables to receive JSON format data by following step-by-step installation, patch extraction, directory setup, and service restart processes described in the lecture.
Explore word stemming as a preprocessing step that reduces variants by trimming suffixes (e.g., shining to shine) using the Porter stemmer and tokenization before NLP analysis.
Learn how to save and reuse a trained machine learning model with Python pickle, enabling fast loading and prediction of sentiment without retraining.
Learn to build a real-time Twitter sentiment analysis pipeline that streams live tweets, classifies sentiment as positive or negative, and uses pickling to speed up repeated predictions.
The demand for Big Data Hadoop Developers, Architects, Data Scientists, Machine Learning Engineers is increasing day by day and one of the main reason is that companies are more keen these days to get more accurate predictions & forecasting result using data. They want to make sense of data and wants to provide 360 view of customers thereby providing better customer experience.
This course is designed in such a way that you will get an understanding of best of both worlds i.e. both Hadoop as well as Data Science. You will not only be able to perform Hadoop related operations to gather data from the source directly but also they can perform Data Science specific tasks and build model on the data collected. Also, you will be able to do transformations using Hadoop Ecosystem tools. So in a nutshell, this course will help the students to learn both Hadoop and Data Science Natural Language Processing in one course.
Companies like Google, Amazon, Facebook, Ebay, LinkedIn, Twitter, and Yahoo! are using Hadoop on a larger scale these days and more and more companies have already started adopting these digital technologies. If we talk about Text Analytics, there are several applications of Text Analytics (given below) and hence companies prefer to have both of these skillset in the professionals.
Companies these days are leaning towards candidates who are equipped with best of both worlds and this course will proved to be a very good starting point. This course covers complete pipeline of modern day ELT (Extract, Load and Transform) and Analytics as shown below:
Get data from Source --> Load data into Structured/Semi Structured/Unstructured form --> Perform Transformations --> Pre-process the Data further --> Build the Data Science Model --> Visualize the Results
Learn and get started with the popular Hadoop Ecosystem technologies as well one the most of the most hot topics in Data Science called Natural Language Processing. In this course you will :