Master big data processing with Hadoop in this advanced course designed for data science professionals. Build a comprehensive big data solution that enhances your skills and career prospects in data analytics and engineering roles.

Big Data Mastery Course with Hadoop

# Unlock the Secrets of Big Data with Hadoop!

Embark on a transformative journey into the world of big data technologies. This advanced course is designed for data science professionals eager to deepen their expertise in Hadoop. You will master the architecture, data processing, and analysis of vast datasets, ultimately building a comprehensive big data solution that will elevate your career prospects in data analytics and engineering roles.


### Who is it for?

This course is designed for advanced learners like you—data science professionals with a solid understanding of data principles, yet eager to deepen your expertise in big data technologies.

- Data science professionals looking to enhance their skills in big data technologies.
- Analytics engineers aiming to master Hadoop for better job prospects.
- Data analysts wanting to process and analyze large datasets effectively.

**Recommended skill level: Advanced**


### Prerequisites

To embark on this transformative journey, you need a solid foundation in data science concepts and experience with programming languages like Python or Java.

- 🧑‍💻 Proficiency in data science concepts
- 💻 Experience with programming languages like Python or Java
- 📊 Familiarity with basic data processing techniques

### What's inside

This course is packed with practical learning experiences that will equip you with the skills needed to excel in big data analytics.

- Decoding Hadoop Architecture
- Setting Up Your Hadoop Environment
- Mastering Data Processing with MapReduce
- Harnessing Data Storage with HDFS
- Integrating Cutting-Edge Big Data Tools
- Unlocking Insights from Large Datasets
- Showcasing Your Big Data Solution

**Interactive Quizzes**

Quizzes will be integrated at the end of each module to reinforce your understanding and application of key concepts in Hadoop and big data processing.

**Homework**

Engage in hands-on assignments that challenge you to apply what you've learned, including developing MapReduce programs and executing HDFS commands.

**Practical Project**

Build a comprehensive big data solution using Hadoop to process and analyze a large dataset, demonstrating your understanding of Hadoop architecture and data processing techniques.

**Before You Start**

Before diving into the course, ensure you have the required software installed and familiarize yourself with the course layout to maximize your learning experience.

**Books to Read**

Suggested readings will enhance your understanding of big data concepts and provide additional insights into Hadoop and its ecosystem.

**Glossary**

A glossary of key terms will be provided to help you navigate the technical language of big data and Hadoop.


### What will you learn

By the end of this course, you will have mastered essential big data skills that are in high demand in the job market.

- Master the architecture of Hadoop and its core components.
- Implement and optimize MapReduce for efficient data processing.
- Utilize HDFS for robust data storage and retrieval.

**Time to complete this course: Invest 8-12 weeks in this course, dedicating just 15-20 hours per week to unlock a world of opportunities in big data analytics.**


### 🚀 Enroll now and start your journey to becoming a big data expert!

A solid grasp of data science principles is crucial for understanding big data technologies. Familiarity with statistics, data analysis, and machine learning will enhance your ability to apply Hadoop effectively.

Proficiency in Data Science Concepts

Hands-on experience with languages like Python or Java is essential. You'll be writing code for MapReduce and integrating other big data tools, making programming skills vital for success.

Experience with Programming Languages

Understanding foundational data processing concepts will enable you to grasp Hadoop's functionalities better. You'll need to know how to clean, transform, and analyze data effectively.

Familiarity with Basic Data Processing Techniques

## Unlock the Secrets of Big Data with Hadoop!
Embark on a transformative journey into the world of big data technologies. This advanced course is designed for data science professionals eager to deepen their expertise in Hadoop. You will master the architecture, data processing, and analysis of vast datasets, ultimately building a comprehensive big data solution that will elevate your career prospects in data analytics and engineering roles.

### Who is it For?
This course is designed for advanced learners like you—data science professionals with a solid understanding of data principles, yet eager to deepen your expertise in big data technologies.
- **Skill Level:** Advanced
- **Audience:**
  - Data science professionals looking to enhance their skills in big data technologies.
  - Analytics engineers aiming to master Hadoop for better job prospects.
  - Data analysts wanting to process and analyze large datasets effectively.

### Prerequisites
To embark on this transformative journey, you need a solid foundation in data science concepts and experience with programming languages like Python or Java.
- **Requirements:**
  - Proficiency in data science concepts
  - Experience with programming languages like Python or Java
  - Familiarity with basic data processing techniques

### What's Inside?
This course is packed with practical learning experiences that will equip you with the skills needed to excel in big data analytics.
- **Modules:**
  - Decoding Hadoop Architecture
  - Setting Up Your Hadoop Environment
  - Mastering Data Processing with MapReduce
  - Harnessing Data Storage with HDFS
  - Integrating Cutting-Edge Big Data Tools
  - Unlocking Insights from Large Datasets
  - Showcasing Your Big Data Solution
- **Quizzes:** Quizzes will be integrated at the end of each module to reinforce your understanding and application of key concepts in Hadoop and big data processing.
- **Assignments:** Engage in hands-on assignments that challenge you to apply what you've learned, including developing MapReduce programs and executing HDFS commands.
- **Practical Project:** Build a comprehensive big data solution using Hadoop to process and analyze a large dataset, demonstrating your understanding of Hadoop architecture and data processing techniques.
- **Before You Start:** Ensure you have the required software installed and familiarize yourself with the course layout to maximize your learning experience.
- **Books to Read:** Suggested readings will enhance your understanding of big data concepts and provide additional insights into Hadoop and its ecosystem.
- **Glossary:** A glossary of key terms will be provided to help you navigate the technical language of big data and Hadoop.

### What Will You Learn?
By the end of this course, you will have mastered essential big data skills that are in high demand in the job market.
- **Skills:**
  - Master the architecture of Hadoop and its core components.
  - Implement and optimize MapReduce for efficient data processing.
  - Utilize HDFS for robust data storage and retrieval.

### Time to Complete
Invest 8-12 weeks in this course, dedicating just 15-20 hours per week to unlock a world of opportunities in big data analytics.

### Enroll now and start your journey to becoming a big data expert!

An open-source framework for distributed storage and processing of large datasets across clusters of computers.

Hadoop Distributed File System; a scalable and fault-tolerant storage system for managing large datasets.

A programming model for processing large data sets with a distributed algorithm on a cluster.

Yet Another Resource Negotiator; manages resources and scheduling for Hadoop applications.

A node in HDFS that stores actual data and serves read/write requests from clients.

The master node in HDFS that manages metadata and regulates access to files.

A group of interconnected computers that work together to process large datasets.

A division of input data into manageable chunks for processing in MapReduce.

A unit of work submitted to the Hadoop cluster for processing.

A high-level platform for creating programs that run on Hadoop, using a language called Pig Latin.

A data warehouse infrastructure built on Hadoop for querying and managing large datasets using SQL-like language.

An open-source data processing engine that can run on Hadoop, known for its speed and ease of use.

The science of analyzing raw data to uncover trends, patterns, and insights.

An approach for analyzing datasets to summarize their main characteristics, often using visual methods.

The graphical representation of information and data to communicate insights clearly.

The ability of a system to continue operating in the event of a failure of some of its components.

The process of duplicating data across multiple nodes in HDFS for reliability and availability.

Extract, Transform, Load; a process for integrating data from multiple sources into a single database.

The capability of a system to handle a growing amount of work or its potential to accommodate growth.

A centralized repository that allows you to store all your structured and unstructured data at any scale.

Extremely large datasets that may be analyzed computationally to reveal patterns, trends, and associations.

A multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge from data.

A subset of AI that uses statistical techniques to enable computers to improve at tasks with experience.

An in-depth analysis of a particular instance or example within a real-world context.

Application Programming Interface; a set of protocols for building and interacting with software applications.

The Hadoop ecosystem is a complex framework that allows for the distributed processing of large data sets across clusters of computers. At its core, it comprises several key components, each playing a vital role in data management and processing.

Hadoop consists of various components, but the three primary ones are HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), and MapReduce. Understanding their roles is essential for leveraging Hadoop effectively.

**HDFS**: This is the storage layer of Hadoop, designed to store large files across multiple machines. It ensures data redundancy and fault tolerance through replication.

**YARN**: This is the resource management layer, responsible for allocating system resources to various applications running in a Hadoop cluster.

**MapReduce**: This is the processing layer that allows for the execution of data processing tasks in a distributed manner.

🔍 **Tip**: Always visualize the data flow between these components to better understand how they interact during data processing.

Creating a visual representation of the Hadoop ecosystem can significantly enhance your understanding. Consider using diagrams to map out how data flows between HDFS, YARN, and MapReduce.

Create a visual diagram illustrating the Hadoop ecosystem and the data flow between its components.

To fully grasp how Hadoop processes data, it's crucial to understand the data flow. When a user submits a job, YARN allocates resources, MapReduce processes the data, and HDFS stores the results.

"The beauty of Hadoop lies in its ability to scale and handle vast amounts of data efficiently." — Data Scientist

To solidify your understanding, engage in a hands-on exercise where you map out the data flow for a sample job submission. This exercise will help you visualize how the components interact.

Map out the data flow for a sample job submission in Hadoop, detailing how HDFS, YARN, and MapReduce work together.

As you explore the Hadoop ecosystem, be aware of common mistakes. One frequent pitfall is underestimating the importance of data replication in HDFS, which can lead to data loss.

⚠️ **Common Mistake**: Ignoring the configuration settings for HDFS can result in inefficient data storage and retrieval. Always review and optimize these settings.

Industry experts emphasize the importance of mastering the Hadoop ecosystem. Understanding how each component interacts will not only enhance your technical skills but also prepare you for real-world challenges in big data analytics.

By the end of this lesson, you should have a clear understanding of the Hadoop ecosystem's components, their roles, and how they interact to process large datasets effectively.

Hadoop Ecosystem Exploration

Unveiling the Hadoop Ecosystem

Hadoop Ecosystem Quiz

Distributed computing is a revolutionary approach that enables the processing of large datasets across multiple machines, significantly enhancing performance and reliability. In the context of Hadoop, distributed computing allows for the seamless handling of data across clusters, providing a robust solution for modern data challenges.

Distributed systems consist of multiple interconnected computers that work together to achieve a common goal. Unlike traditional databases, which rely on a single server, distributed systems spread the workload across multiple machines, allowing for parallel processing and improved efficiency.

Key characteristics of distributed systems include:

Scalability: Easily add more machines to handle increased data loads.

Fault tolerance: Automatically recover from hardware failures without data loss.

High availability: Ensure that services remain operational even during failures.

Advantages of Distributed Computing in Hadoop

Hadoop leverages distributed computing to provide several key advantages over traditional databases:

1. **Scalability**: Hadoop can scale horizontally by adding more nodes to the cluster, allowing organizations to handle growing data volumes without significant redesign.

2. **Fault Tolerance**: Hadoop's architecture automatically replicates data across multiple nodes, ensuring that if one node fails, data remains accessible from other nodes.

3. **Cost Efficiency**: By utilizing commodity hardware, organizations can build large clusters at a fraction of the cost of traditional database solutions.

Comparing Hadoop with Traditional Databases

To understand the power of distributed computing, it's essential to compare Hadoop with traditional databases. Traditional databases typically require expensive, high-performance hardware to handle large datasets, while Hadoop can run on inexpensive commodity hardware.

💡 **Tip**: When considering a big data solution, always evaluate the total cost of ownership, including hardware, software, and maintenance costs.

Performance Metrics in Distributed Computing

When analyzing the performance of distributed systems like Hadoop, consider the following metrics:

Throughput: The amount of data processed in a given time frame.

Latency: The time taken to process a single request.

Resource Utilization: The efficiency of resource usage across the cluster.

Hands-On Exercise: Analyzing Distributed System Performance

To solidify your understanding of distributed computing, let's perform a hands-on exercise:

Set up a small Hadoop cluster (can be local or cloud-based) and run a sample MapReduce job.

Monitor the performance metrics during the job execution, focusing on throughput and latency.

Document your findings and analyze how distributed computing impacts performance.

Understanding the power of distributed computing is crucial for leveraging Hadoop effectively. By recognizing its advantages, comparing it with traditional databases, and analyzing performance metrics, you will be better equipped to implement big data solutions that drive insights and innovation.

Homework Assignment: Exploring Distributed Computing

The Power of Distributed Computing

Distributed Computing Quiz

HDFS is designed to store large files across multiple machines, providing high throughput access to application data. It follows a master/slave architecture, where the NameNode acts as the master server, managing metadata and namespace, while DataNodes store the actual data.

To interact with HDFS, you need to be familiar with various commands. Here are some essential commands you should master:

`hdfs dfs -ls /` - Lists files in the root directory.

`hdfs dfs -put localfile.txt /user/hadoop/` - Uploads a file from the local filesystem to HDFS.

`hdfs dfs -get /user/hadoop/remotefile.txt ./` - Downloads a file from HDFS to the local filesystem.

`hdfs dfs -rm /user/hadoop/remotefile.txt` - Deletes a file from HDFS.

HDFS ensures data reliability through replication. By default, each file is replicated three times across different DataNodes. This redundancy helps in recovering data in case of hardware failure.

The benefits of data replication in HDFS include:

To solidify your understanding, perform the following tasks in your Hadoop environment:

List the files in your HDFS root directory.

Download a file from HDFS to your local system.

Consider a scenario where a financial institution needs to store transaction data securely. HDFS allows them to store large amounts of data reliably, ensuring that even if a DataNode fails, the data remains accessible.

Understanding HDFS storage policies can optimize your data management. You can set different replication factors based on the importance of data, ensuring critical data is stored with higher redundancy.

"Data is the new oil, but it's only valuable if you can manage it effectively." - Unknown

Research the different storage policies in HDFS.

Experiment with changing the replication factor for different files.

Document the impact of these changes on performance and storage.

HDFS Practical Assignment

Navigating HDFS: The Storage Backbone

HDFS Mastery Quiz

MapReduce is a programming model that allows for the processing of large data sets in a distributed computing environment. Understanding its workflow is crucial for anyone working with big data.

The MapReduce model consists of two primary functions: the **Map** function and the **Reduce** function. The Map function processes input data and produces a set of intermediate key-value pairs. The Reduce function then merges these intermediate values into a smaller set of values.

🔍 **Tip:** Visualizing the MapReduce workflow can enhance your understanding. Consider drawing the flow from input data to output results, highlighting the roles of Mapper and Reducer.

Writing a MapReduce job involves defining the Mapper and Reducer classes. Each class must implement specific methods that handle the input and output data.

public class MyMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        // Your mapping logic here
    }
}

public class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        // Your reducing logic here
    }
}

Once you have executed your MapReduce job, analyzing the output is critical. This involves interpreting the results generated by the Reducer and ensuring they align with your expectations.

💡 **Common Pitfall:** Ensure that your input data is clean and formatted correctly to avoid errors during processing. Data quality is key to successful MapReduce jobs.

Practical Exercise: Write a MapReduce Job

As a practical exercise, write a MapReduce job that counts the frequency of words in a given text file. Use the Mapper to emit each word with a count of one, and use the Reducer to sum these counts.

1. Implement the Mapper and Reducer classes as described.
2. Test your MapReduce job with a sample text file.
3. Analyze the output to ensure accuracy.

Optimization can significantly enhance the performance of your MapReduce jobs. Techniques include:
- **Combiner:** A mini-reducer that runs on the Mapper's output to reduce the amount of data transferred to the Reducer.
- **Partitioner:** Controls the distribution of keys to reducers, improving load balancing.

"Optimization is the key to making your MapReduce jobs efficient and effective." - Data Engineer

By mastering the MapReduce programming model, you are well on your way to effectively processing large datasets. In the next lesson, we will explore advanced optimization techniques and their impact on job performance.

MapReduce Job Implementation

MapReduce: The Heart of Data Processing

MapReduce Mastery Quiz

Understanding MapReduce Performance Metrics

To optimize your MapReduce jobs effectively, it's crucial to understand the performance metrics that indicate how well your jobs are running. Key metrics include job duration, resource utilization (CPU, memory, and disk I/O), and data locality. Monitoring these metrics helps you identify bottlenecks and areas for improvement.

There are several techniques to optimize MapReduce jobs, including:
- **Combiner Functions**: These reduce the amount of data transferred between the Mapper and Reducer phases, which can significantly decrease network I/O.
- **Data Locality**: Ensure that your data is processed close to where it is stored to minimize data transfer times.
- **Partitioning**: Properly partition your data to balance the load across Mappers and Reducers.

💡 Tip: Always profile your jobs to understand where the time is being spent. Use tools like Hadoop's JobTracker or ResourceManager for insights.

Best Practices for Writing Efficient MapReduce Jobs

When writing MapReduce jobs, consider the following best practices:
- **Avoiding Small Files**: Small files can lead to performance degradation. Use tools like Hadoop Archive (HAR) or SequenceFile to combine small files.
- **Choosing the Right Input Format**: Use the appropriate input format for your data (e.g., TextInputFormat, SequenceFileInputFormat) to optimize reading performance.
- **Efficient Data Serialization**: Use efficient serialization formats like Avro or Protocol Buffers to reduce the overhead during data transfer.

Experimenting with Optimization Strategies

Hands-on experimentation is key to mastering optimization. Set up a test environment where you can:
- Write different versions of a MapReduce job, each implementing a different optimization technique.
- Measure and compare the performance of each version using Hadoop's built-in metrics.

// Example of a simple MapReduce job with a Combiner function
public class WordCount {
    public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {
        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();

        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            StringTokenizer itr = new StringTokenizer(value.toString());
            while (itr.hasMoreTokens()) {
                word.set(itr.nextToken());
                context.write(word, one);
            }
        }
    }

    public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
        private IntWritable result = new IntWritable();

        public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable val : values) {
                sum += val.get();
            }
            result.set(sum);
            context.write(key, result);
        }
    }
}

Once you have optimized your MapReduce jobs, it's essential to analyze the outputs effectively. This includes:
- **Generating Reports**: Create reports that summarize the performance metrics of your jobs.
- **Visualizing Results**: Use data visualization tools to represent the results of your MapReduce jobs, making it easier to interpret and present findings.

📊 Common Pitfall: Failing to document your optimizations can lead to repeated mistakes. Keep a detailed log of what works and what doesn’t.

MapReduce Optimization Homework

Optimizing MapReduce for Big Data

MapReduce Optimization Quiz

To effectively work with Hadoop, it's crucial to understand the functionalities of complementary tools like Hive, Pig, and Spark. Each tool serves a unique purpose in the data processing ecosystem, allowing you to perform various operations efficiently.

**Hive**: A data warehouse infrastructure that provides data summarization and ad-hoc querying using a SQL-like language.

**Pig**: A high-level platform for creating programs that run on Hadoop, using a language called Pig Latin.

**Spark**: A fast and general-purpose cluster computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.

Integrating Hive with Hadoop allows you to perform SQL-like queries on large datasets, making data manipulation easier for those familiar with traditional databases.

Install Hive and configure it to work with your Hadoop setup.

Write basic Hive queries to retrieve data from your Hadoop datasets.

Test the performance of Hive queries against MapReduce jobs.

Pig provides a flexible way to process data in Hadoop. Its language, Pig Latin, is designed to handle data flows and is easier to learn than Java.

Write a Pig script that processes a dataset in Hadoop.

Optimize your Pig scripts for performance.

Document the advantages and challenges of using Pig with Hadoop.

Spark excels in real-time data processing, making it a powerful addition to your Hadoop environment. Its ability to handle streaming data sets it apart from traditional MapReduce.

Install and configure Spark to integrate with your Hadoop environment.

Implement a simple Spark job to process a dataset.

Compare the performance of Spark with MapReduce for the same data processing task.

To solidify your understanding, you will implement a project that integrates Hive, Pig, and Spark with Hadoop. This exercise will help you see how these tools work together.

Create a project that utilizes Hive, Pig, and Spark to process a dataset.

Document the integration process and the outcomes of using each tool.

Prepare a presentation summarizing your project and the advantages of using these tools.

"The best way to predict the future is to invent it." - Alan Kay

🔍 **Tip:** Always test the performance of your queries and scripts. This will help you optimize your data processing workflows.

Homework: Tool Integration

Integrating Tools: Hive, Pig, and Spark

Tool Integration Quiz

Data visualization is a critical skill for data scientists, especially when dealing with large datasets. It allows complex data to be presented in a way that is accessible and understandable, enabling stakeholders to make informed decisions.

Performing Exploratory Data Analysis (EDA)

Homework: Data Visualization Project

Data Visualization and Insights

Data Visualization Quiz

"Data visualization is not just about making things pretty; it's about making data understandable." - Unknown

📊 **Tip**: Always ask for feedback on your visualizations to improve clarity and effectiveness!

The Importance of a Professional Portfolio

A professional portfolio is more than just a collection of your work; it's a narrative that showcases your skills, experiences, and growth. In the field of big data, where technical skills are paramount, a well-structured portfolio can set you apart from other candidates. It allows potential employers or clients to see not just what you've done, but how you've approached problems and derived solutions.

Key components of a strong portfolio include:
- A clear introduction that outlines your skills and experiences.
- Detailed case studies of your projects, including objectives, methodologies, and outcomes.
- Visualizations or reports that illustrate your findings.

When compiling your big data project, consider the following steps to ensure clarity and professionalism:

Start with an executive summary that outlines the project's goals and results.

Include a detailed methodology section that explains your approach and the technologies used.

Present your findings through visualizations, making complex data easily digestible.

Conclude with reflections on what you learned and how you can apply these insights moving forward.

📝 **Tip:** Use storytelling techniques to make your project narrative engaging. Highlight challenges faced and how you overcame them to add depth to your showcase.

Effective presentation skills are crucial when showcasing your work. Here are some strategies to enhance your presentation:

Practice clear and concise communication, avoiding jargon unless necessary.

Engage your audience with questions to encourage interaction.

Use visual aids to support your points, ensuring they are easy to understand.

Feedback is essential for growth. Consider the following ways to gather and apply feedback on your project presentation:

Present your project to peers and solicit constructive criticism.

Incorporate feedback into your final presentation to enhance clarity and impact.

Reflect on the feedback process, noting how it can improve your future projects.

"Feedback is the breakfast of champions." - Ken Blanchard

Hands-On Exercise: Building Your Portfolio

To put your learning into practice, it's time to create your professional portfolio. Follow these steps:

Compile all project deliverables and documentation into a cohesive portfolio.

Develop a presentation summarizing your project journey and outcomes.

Gather feedback from peers on your portfolio and presentation.

Refine your project based on the feedback received.

Prepare for potential interviews by practicing your presentation.

Document lessons learned throughout the project.

Homework: Portfolio and Presentation Development

Showcasing Your Big Data Project

Showcasing Your Big Data Project Quiz

Dive into the intricate architecture of Hadoop, exploring its core components and their interactions. This foundational module sets the stage for effective implementation of big data solutions, focusing on HDFS, YARN, and MapReduce.

Decoding Hadoop Architecture

Before diving into the installation process, it's essential to understand the prerequisites and the overall architecture of Hadoop. Hadoop consists of several components, including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), and MapReduce, all of which play a crucial role in big data processing.

🔍 **Tip:** Make sure your system meets the hardware and software requirements for Hadoop installation. A minimum of 8GB RAM and a 64-bit operating system are recommended for optimal performance.

Follow these steps to install Hadoop on your local machine or cloud platform:

Download the latest stable release of Hadoop from the official Apache website.

Extract the downloaded tar file to your preferred directory.

Set up environment variables by editing your `.bashrc` or `.bash_profile` file. Add the following lines:

```bash
export HADOOP_HOME=/path/to/hadoop
environment
export PATH=$PATH:$HADOOP_HOME/bin
```

Installation of Hadoop

Establish your hands-on Hadoop environment through a step-by-step guide on installation and configuration. This module ensures you are equipped for practical application, optimizing settings for big data processing.

Quick Navigation

HADOOP#1

HDFS#2

MAPREDUCE#3

YARN#4

DATA NODE#5

NAME NODE#6

CLUSTER#7

SPLIT#8

JOB#9

PIG#10

HIVE#11

SPARK#12

DATA ANALYTICS#13

EXPLORATORY DATA ANALYSIS#14

DATA VISUALIZATION#15

FAULT TOLERANCE#16

REPLICATION#17

ETL#18

SCALABILITY#19

DATA LAKE#20

BIG DATA#21

DATA SCIENCE#22

MACHINE LEARNING#23

CASE STUDY#24

API#25