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Answered 4 days ago Learn Hadoop

Sadiq

C language Faculty (online Classes )

Whether Apache Hadoop or Apache Spark is better depends on your data analysis goals, the type of data processing you need to do, and your budget:
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Answered on 27 Sep Learn Hadoop

Sana Begum

My teaching experience 12 years

Spark is a good choice if you're working with machine learning algorithms or large-scale data. If you're working with giant data sets and want to store and process them, Hadoop is a better option. Hadoop is more cost-effective and easily scalable than Spark.
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Answered on 27 Sep Learn Hadoop

Sana Begum

My teaching experience 12 years

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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Answered on 27 Sep Learn Hadoop

Sana Begum

My teaching experience 12 years

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... read more
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. read less
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Answered on 27 Sep Learn Hadoop

Sana Begum

My teaching experience 12 years

Hadoop is a framework for distributed storage and processing of large datasets using the MapReduce programming model, which is disk-based and typically slower. In contrast, Spark is an in-memory data processing engine that can handle batch and real-time data, offering faster processing speeds and a more... read more
Hadoop is a framework for distributed storage and processing of large datasets using the MapReduce programming model, which is disk-based and typically slower. In contrast, Spark is an in-memory data processing engine that can handle batch and real-time data, offering faster processing speeds and a more flexible programming model with APIs in various languages. Spark can run independently or on top of Hadoop, leveraging HDFS for storage. read less
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Answered on 27 Sep Learn Hadoop

Sana Begum

My teaching experience 12 years

The Hadoop ecosystem is a collection of open-source software tools that allow you to store and process large amounts of data. The tools in this ecosystem include HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), and MapReduce.
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Answered on 27 Sep Learn Hadoop

Sana Begum

My teaching experience 12 years

Conclusion: Whether to learn Hadoop or Python depends on the career goals of the learner. If he/she is into big data and analytics, he/she should go for Hadoop. But if they want versatility and wide applicability, Python would be a better choice.
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Answered on 26 Sep Learn Hadoop

Sadiq

C language Faculty (online Classes )

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... read more

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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Answered on 27 Sep Learn Hadoop

Sana Begum

My teaching experience 12 years

Spark is best for real-time data processing, in-memory analytics, and machine learning, while Hadoop is ideal for batch processing and handling massive datasets that don't require real-time analysis.
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Answered on 27 Sep Learn Hadoop

Sana Begum

My teaching experience 12 years

The small file problem in Hadoop occurs when HDFS is inefficient at handling a large number of small files, as each file's metadata is stored in memory, causing excessive memory usage and reduced performance.
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