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Hadoop
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Hadoop vs Spark Framework: What are the differences?

Hadoop: Open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage; Spark Framework: A micro framework for creating web applications in Kotlin and Java 8 with minimal effort. It is a simple and expressive Java/Kotlin web framework DSL built for rapid development. Its intention is to provide an alternative for Kotlin/Java developers that want to develop their web applications as expressive as possible and with minimal boilerplate.

Hadoop belongs to "Databases" category of the tech stack, while Spark Framework can be primarily classified under "Microframeworks (Backend)".

Hadoop is an open source tool with 9.4K GitHub stars and 5.85K GitHub forks. Here's a link to Hadoop's open source repository on GitHub.

Airbnb, Uber Technologies, and Netflix are some of the popular companies that use Hadoop, whereas Spark Framework is used by Kasa Smart, AfricanStockPhoto, and Khartec ltd. Hadoop has a broader approval, being mentioned in 309 company stacks & 623 developers stacks; compared to Spark Framework, which is listed in 5 company stacks and 4 developer stacks.

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What is Hadoop?

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

What is Spark Framework?

It is a simple and expressive Java/Kotlin web framework DSL built for rapid development. Its intention is to provide an alternative for Kotlin/Java developers that want to develop their web applications as expressive as possible and with minimal boilerplate.
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        What are some alternatives to Hadoop and Spark Framework?
        Cassandra
        Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
        MongoDB
        MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
        Elasticsearch
        Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
        Splunk
        Splunk Inc. provides the leading platform for Operational Intelligence. Customers use Splunk to search, monitor, analyze and visualize machine data.
        HBase
        Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
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        Decisions about Hadoop and Spark Framework
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        How developers use Hadoop and Spark Framework
        Avatar of Pinterest
        Pinterest uses HadoopHadoop

        The MapReduce workflow starts to process experiment data nightly when data of the previous day is copied over from Kafka. At this time, all the raw log requests are transformed into meaningful experiment results and in-depth analysis. To populate experiment data for the dashboard, we have around 50 jobs running to do all the calculations and transforms of data.

        Avatar of Yelp
        Yelp uses HadoopHadoop

        in 2009 we open sourced mrjob, which allows any engineer to write a MapReduce job without contending for resources. We’re only limited by the amount of machines in an Amazon data center (which is an issue we’ve rarely encountered).

        Avatar of Pinterest
        Pinterest uses HadoopHadoop

        The massive volume of discovery data that powers Pinterest and enables people to save Pins, create boards and follow other users, is generated through daily Hadoop jobs...

        Avatar of Robert Brown
        Robert Brown uses HadoopHadoop

        Importing/Exporting data, interpreting results. Possible integration with SAS

        Avatar of Rohith Nandakumar
        Rohith Nandakumar uses HadoopHadoop

        TBD. Good to have I think. Analytics on loads of data, recommendations?

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