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CDAP

14
49
+ 1
0
Delta Lake

44
181
+ 1
0
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CDAP vs Delta Lake: What are the differences?

Developers describe CDAP as "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. On the other hand, Delta Lake is detailed as "Reliable Data Lakes at Scale". An open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads.

CDAP and Delta Lake 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, Delta Lake provides the following key features:

  • ACID Transactions
  • Scalable Metadata Handling
  • Time Travel (data versioning)

CDAP and Delta Lake are both open source tools. It seems that Delta Lake with 1.26K GitHub stars and 210 forks on GitHub has more adoption 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 Delta Lake?

An open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads.

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What companies use CDAP?
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What tools integrate with CDAP?
What tools integrate with Delta Lake?
What are some alternatives to CDAP and Delta Lake?
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.
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