CDAP vs Google Cloud Data Fusion

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CDAP
CDAP

7
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Google Cloud Data Fusion
Google Cloud Data Fusion

4
26
+ 1
0
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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.

- No public GitHub repository available -

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.
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            What are some alternatives to CDAP and Google Cloud Data Fusion?
            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.
            Amazon Athena
            Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
            Apache Flink
            Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
            Apache Hive
            Hive facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage.
            Druid
            Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
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