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CDAP
CDAP

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StreamSets

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CDAP vs StreamSets: What are the differences?

What is 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.

What is StreamSets? Where DevOps Meets Data Integration. The industry's first data operations platform for full life-cycle management of data in motion.

CDAP and StreamSets 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, StreamSets provides the following key features:

  • Build Batch & Streaming Pipelines in Hours
  • Map and Monitor Runtime Performance
  • Protect Sensitive Data as it Arrives

CDAP is an open source tool with 355 GitHub stars and 182 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 StreamSets?

The industry's first data operations platform for full life-cycle management of data in motion.
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          What are some alternatives to CDAP and StreamSets?
          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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