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CDAP

15
53
+ 1
0
Pachyderm

16
53
+ 1
5
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CDAP vs Pachyderm: What are the differences?

CDAP: 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; Pachyderm: MapReduce without Hadoop. Analyze massive datasets with Docker. Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.

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

Some of the features offered by CDAP are:

  • Streams for data ingestion
  • Reusable libraries for common Big Data access patterns
  • Data available to multiple applications and different paradigms

On the other hand, Pachyderm provides the following key features:

  • Git-like File System
  • Dockerized MapReduce
  • Microservice Architecture

CDAP and Pachyderm are both open source tools. Pachyderm with 3.81K GitHub stars and 369 forks on GitHub appears to be more popular 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 Pachyderm?

    Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.

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    What companies use Pachyderm?
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    What tools integrate with CDAP?
    What tools integrate with Pachyderm?
    What are some alternatives to CDAP and Pachyderm?
    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
    The industry's first data operations platform for full life-cycle management of data in motion.
    See all alternatives
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