Hadoop

1.7K
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Spring Batch

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Hadoop vs Spring Batch: 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; Spring Batch: A lightweight, comprehensive batch framework. It is designed to enable the development of robust batch applications vital for the daily operations of enterprise systems It also provides reusable functions that are essential in processing large volumes of records, including logging/tracing, transaction management, job processing statistics, job restart, skip, and resource management..

Hadoop belongs to "Databases" category of the tech stack, while Spring Batch can be primarily classified under "Frameworks (Full Stack)".

Hadoop and Spring Batch are both open source tools. It seems that Hadoop with 9.27K GitHub stars and 5.78K forks on GitHub has more adoption than Spring Batch with 1.3K GitHub stars and 1.3K GitHub forks.

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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 Spring Batch?

    It is designed to enable the development of robust batch applications vital for the daily operations of enterprise systems. It also provides reusable functions that are essential in processing large volumes of records, including logging/tracing, transaction management, job processing statistics, job restart, skip, and resource management.
    What companies use Hadoop?
    What companies use Spring Batch?

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    What tools integrate with Hadoop?
    What tools integrate with Spring Batch?

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    What are some alternatives to Hadoop and Spring Batch?
    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
    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
    Snowflake
    Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
    See all alternatives
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