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CDAP
CDAP

5
9
+ 1
0
Mara
Mara

2
6
+ 1
3
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CDAP vs Mara: 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; Mara: A lightweight ETL framework. A lightweight ETL framework with a focus on transparency and complexity reduction.

CDAP and Mara can be categorized 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, Mara provides the following key features:

  • Data integration pipelines as code: pipelines, tasks and commands are created using declarative Python code.
  • PostgreSQL as a data processing engine.
  • Extensive web ui. The web browser as the main tool for inspecting, running and debugging pipelines.

CDAP and Mara are both open source tools. Mara with 1.24K GitHub stars and 51 forks on GitHub appears to be more popular than CDAP with 346 GitHub stars and 178 GitHub forks.

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 Mara?

A lightweight ETL framework with a focus on transparency and complexity reduction.
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            What are some alternatives to CDAP and Mara?
            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.
            Presto
            Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.
            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.
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