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CDAP vs Corral

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CDAP
23
99
+ 1
0
Corral
0
4
+ 1
0
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CDAP vs Corral: What are the differences?

Developers describe CDAP as "Open source virtualization platform for Hadoop data and apps". Cask Data Application Platform (CDAP) is an open source application development platform for the Hadoop ecosystem that provides developers with data and application virtualization to accelerate application development, address a broader range of real-time and batch use cases, and deploy applications into production while satisfying enterprise requirements. On the other hand, Corral is detailed as "A serverless MapReduce framework written for AWS Lambda". Corral is a MapReduce framework designed to be deployed to serverless platforms, like AWS Lambda. It presents a lightweight alternative to Hadoop MapReduce.

CDAP and Corral belong to "Big Data Tools" category of the tech stack.

CDAP and Corral are both open source tools. It seems that Corral with 611 GitHub stars and 16 forks on GitHub has more adoption than CDAP with 346 GitHub stars and 178 GitHub forks.

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What is CDAP?

Cask Data Application Platform (CDAP) is an open source application development platform for the Hadoop ecosystem that provides developers with data and application virtualization to accelerate application development, address a broader range of real-time and batch use cases, and deploy applications into production while satisfying enterprise requirements.

What is Corral?

Corral is a MapReduce framework designed to be deployed to serverless platforms, like AWS Lambda. It presents a lightweight alternative to Hadoop MapReduce.

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What companies use CDAP?
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    What tools integrate with CDAP?
    What tools integrate with Corral?
    What are some alternatives to CDAP and Corral?
    Airflow
    Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
    Apache Spark
    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
    Akutan
    A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world.
    Apache NiFi
    An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
    StreamSets
    An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.
    See all alternatives

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