Learn Hadoop with Free Lessons & Tips

To start learning Hadoop, familiarize yourself with big data concepts and Hadoop architecture. Enroll in online courses, set up a local Hadoop environment for hands-on practice, read official documentation, and work on small projects to apply your knowledge. Joining communities can also provide support and networking opportunities.

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Hadoop is used mainly to solve three types of components: HDFS to store, YARN for resource management, and MapReduce for parallel processing. Big Data is used in many businesses across all sectors, including IT, the retail industry, banking and finance, healthcare, transportation, telecommunication, etc.

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RHIPE involves working with R and Hadoop integrated programming environment. We can use Python, Perl, or Java to read data sets in RHIPE. There are various functions in RHIPE which lets HDFS interact with HDFS. Hence, this way we can read, save the complete data which is created using RHIPE MapReduce.

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Both Hadoop and Spark are Apache's products and are open-source. Moreover, they're complementary to each other. Hadoop provides distributed file system with HDFS and Spark has in-time memory which helps in processing of real-time. In an ideal scenario, Hadoop and Spark complement each other perfectly.

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Prerequisites for learning Hadoop include a basic understanding of programming (preferably in Java or Python), familiarity with Linux commands, knowledge of distributed computing concepts, and some experience with data handling and manipulation. Understanding SQL can also be beneficial for working with Hadoop-related tools.

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Unlike RDBMS, Hadoop is not a database, but rather a distributed file system that can store and process a massive amount of data clusters across computers.

However, RDBMS is a structured database approach in which data is stored in rows and columns which can be updated with SQL and presented in different tables.

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Choosing between **Hadoop** and **Spark** depends on your specific needs and use cases. Here’s a comparison to help you decide: ### Hadoop: - **Architecture**: Based on a distributed file system (HDFS) and uses MapReduce for data processing. - **Batch Processing**: Best suited for batch processing large datasets. - **Storage**: Efficient for storing vast amounts of data and can handle various data types. - **Fault Tolerance**: Provides strong fault tolerance and reliability through data replication. - **Resource Management**: Uses YARN for resource management. - **Processing Speed**: Generally slower due to the disk-based storage of MapReduce. ### Spark: - **Architecture**: In-memory processing engine, which allows for faster data processing compared to Hadoop. - **Speed**: Significantly faster than Hadoop for both batch and real-time processing due to its in-memory capabilities. - **Flexibility**: Supports various programming languages (Java, Scala, Python, R) and libraries for machine learning (MLlib) and graph processing (GraphX). - **Real-time Processing**: Excellent for real-time data processing with Spark Streaming. - **Ease of Use**: Offers a simpler API and interactive shell for data manipulation. ### Conclusion: - **Use Hadoop** if you need to process very large datasets with a focus on batch processing and data storage. - **Use Spark** if you require faster processing speeds, real-time analytics, and a more flexible programming model. In many scenarios, organizations use both together, leveraging Hadoop for storage and Spark for processing, making it a powerful combination for big data applications. read less