CDAP vs Google Cloud Data Fusion

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CDAP

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103
+ 1
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Google Cloud Data Fusion

25
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1
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CDAP vs Google Cloud Data Fusion: 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; Google Cloud Data Fusion: Fully managed, code-free data integration at any scale. A fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines. With a graphical interface and a broad open-source library of preconfigured connectors and transformations, and more.

CDAP and Google Cloud Data Fusion can be primarily classified as "Big Data" tools.

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, Google Cloud Data Fusion provides the following key features:

  • Code-free self-service
  • Collaborative data engineering
  • GCP-native

CDAP is an open source tool with 346 GitHub stars and 178 GitHub forks. Here's a link to CDAP's open source repository on GitHub.

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Pros of CDAP
Pros of Google Cloud Data Fusion
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      Lower total cost of pipeline ownership

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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 Google Cloud Data Fusion?

    A fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines. With a graphical interface and a broad open-source library of preconfigured connectors and transformations, and more.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use CDAP?
    What companies use Google Cloud Data Fusion?
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      What tools integrate with CDAP?
      What tools integrate with Google Cloud Data Fusion?
      What are some alternatives to CDAP and Google Cloud Data Fusion?
      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