Hadoop is a project of Apache.org and it is a software library and an action framework that allows the distributed processing of large data sets, known as big data, through thousands of conventional systems that offer power processing and storage space. Hadoop is, in essence, the most powerful design in the big data analytics space.

Several modules participate in the creation of its framework and among the main ones we find the following:

Hadoop Common (Utilities and libraries that support other Hadoop modules)

Hadoop Distributed File Systems (HDFS)

Hadoop YARN (Yet Another Resource Negociator), cluster management technology.

Hadoop Mapreduce (programming model that supports massive parallel computing)

Although the four modules mentioned above make up the central core of Hadoop, there are others. Among them, as quoted by Hess, are Ambari, Avro, Cassandra, Hive, Pig, Oozie, Flume, and Sqoop. All of them serve to extend and extend the power of Hadoop and be included in big data applications and processing of large data sets.

Many companies use Hadoop for their large data and analytics sets. It has become the de facto standard in big data applications. Hess notes that Hadoop was originally designed to handle crawling functions and search millions of web pages while collecting information from a database. The result of that desire to browse and search the Web ended up being Hadoop HDFS and its distributed processing engine, MapReduce.

According to Hess, Hadoop is useful for companies when the data sets are so large and so complex that the solutions they already have cannot process the information effectively and in what the business needs define as reasonable times.

MapReduce is an excellent word-processing engine, and that's because crawling and web search, its first challenges, are text-based tasks.

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Apache Spark
Spark is also an open source project from the Apache foundation that was born in 2012 as an enhancement to Hadoop's Map Reduce paradigm . It has high-level programming abstractions and allows working with SQL language . Among its APIs it has two real-time data processing (Spark Streaming and Spark Structured Streaming), one to apply distributed Machine Learning (Spark MLlib) and another to work with graphs (Spark GraphX).

Although Spark also has its own resource manager (Standalone), it does not have as much maturity as Hadoop Yarn, so the main module that stands out from Spark is its distributed processing paradigm.

For this reason it does not make much sense to compare Spark vs Hadoop and it is more accurate to compare Spark with Hadoop Map Reduce since they both perform the same functions. Let's see the advantages and disadvantages of some of its features:

Apache Spark is up to 100 times faster than Map Reduce since it works in RAM memory (unlike Map Reduce that stores intermediate results on disk) thus greatly speeding up processing times.

In addition, the great advantage of Spark is that it has a scheduler called DAG that sets the tasks to be performed and optimizes the calculations .

Development complexity
Map Reduce is mainly programmed in Java although it has compatibility with other languages . The programming in Map Reduce follows a specific methodology which means that it is necessary to model the problems according to this way of working.

Spark, on the other hand, is easier to program today thanks to the enormous effort of the community to improve this framework.

Spark is compatible with Java, Scala, Python and R which makes it a great tool not only for Data Engineers but also for Data Scientists to perform analysis on data .

In terms of computational costs, Map Reduce requires a cluster that has more disks and is faster for processing. Spark, on the other hand, needs a cluster that has a lot of RAM.

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