What are pre-requisites for learning Hadoop?

Learning Hadoop involves understanding various concepts related to distributed computing, large-scale data processing, and specific technologies within the Hadoop ecosystem. Here are some prerequisites that can help you get started with learning Hadoop:

1. Foundational Computer Science Knowledge:

   • A strong foundation in computer science concepts, algorithms, and data structures is beneficial. Understanding the basics of programming and computational thinking is important.

2. Linux Command Line:

   • Proficiency in using the Linux command line is valuable, as Hadoop is often deployed on Unix-based systems. Basic commands for navigating the file system, manipulating files, and executing commands are essential.

3. Database and SQL Knowledge:

   • Familiarity with relational databases and SQL (Structured Query Language) is beneficial. Hadoop ecosystem tools like Apache Hive use a SQL-like language for querying and data manipulation.

4. Networking Basics:

   • Understanding fundamental networking concepts, such as IP addresses, ports, and protocols, is useful for setting up and configuring Hadoop clusters.

5. Java Programming (Optional but Recommended):

   • While not mandatory, having a basic understanding of Java is advantageous. Hadoop's core components are implemented in Java, and knowledge of Java can be helpful for troubleshooting or customizing Hadoop configurations.

6. Parallel and Distributed Computing Concepts:

   • Familiarity with parallel and distributed computing concepts is crucial. Concepts such as parallel processing, scalability, and fault tolerance are fundamental to understanding how Hadoop processes data across a distributed cluster.

7. Basic Understanding of Data:

   • An understanding of data formats (e.g., JSON, XML, CSV) and data types is important. Hadoop often deals with diverse and large datasets, so knowledge of data structures is beneficial.

8. Virtualization and Cloud Computing (Optional):

   • Familiarity with virtualization technologies and cloud computing platforms (e.g., AWS, Azure, Google Cloud) is optional but can be useful for setting up Hadoop clusters in a cloud environment.

9. Data Warehousing Concepts (Optional):

   • Understanding data warehousing concepts, such as data modeling, ETL (Extract, Transform, Load) processes, and dimensional modeling, can be beneficial when working with big data analytics tools.

10. Mathematics and Statistics (Optional):

    • While not strictly required, a basic understanding of mathematics and statistics can be beneficial for performing data analysis and deriving insights from big data.

11. Hands-on Experience with Databases (Optional):

    • Familiarity with traditional databases (e.g., MySQL, PostgreSQL) and experience in writing simple queries can be beneficial when working with big data storage solutions like Apache HBase.

Keep in mind that while these prerequisites can provide a strong foundation, they are not strict requirements. Learning Hadoop is a continuous process, and practical experience is crucial. As you start working with Hadoop, you'll gain hands-on experience and a deeper understanding of its components and capabilities. Additionally, consider taking online courses, reading relevant books, and engaging with the Hadoop community to enhance your learning journey.