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  1. Stackups
  2. Application & Data
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  4. Big Data Tools
  5. CDAP vs Google Cloud Data Fusion

CDAP vs Google Cloud Data Fusion

OverviewComparisonAlternatives

Overview

CDAP
CDAP
Stacks41
Followers108
Votes0
Google Cloud Data Fusion
Google Cloud Data Fusion
Stacks25
Followers156
Votes1

CDAP vs Google Cloud Data Fusion: What are the differences?

Introduction

CDAP (Cask Data Application Platform) and Google Cloud Data Fusion are two popular data integration platforms used for building and managing data pipelines. While both platforms offer similar capabilities, there are some key differences between CDAP and Google Cloud Data Fusion that set them apart.

  1. Architecture: CDAP is an open-source platform that provides a comprehensive set of tools and services for building and managing data workflows. It is built on Apache Hadoop and Apache Spark, allowing for distributed data processing and analysis. On the other hand, Google Cloud Data Fusion is a fully-managed service that allows you to visually create, deploy, and manage data pipelines. It is powered by Google Cloud Platform, offering scalability and reliability.

  2. Ease of use: Google Cloud Data Fusion offers a simple and intuitive web-based interface that allows users to visually create and manage data pipelines without requiring any coding skills. It provides a wide range of pre-built connectors and transformations, making it easier to integrate with various data sources and perform data transformations. In contrast, CDAP requires some coding knowledge as it uses a Java-based API for pipeline development and customization.

  3. Integration with other services: CDAP provides seamless integration with various data storage and processing technologies, including Hadoop, Spark, and NoSQL databases. It also supports integration with external systems through custom plugins. On the other hand, Google Cloud Data Fusion is tightly integrated with the Google Cloud Platform ecosystem, allowing users to leverage services like BigQuery, Pub/Sub, and Cloud Storage for data storage, processing, and analytics.

  4. Scalability and reliability: Being built on Apache Hadoop and Spark, CDAP offers high scalability and fault-tolerance, making it suitable for processing large volumes of data. It also provides cluster management features for scaling resources up and down based on demand. Google Cloud Data Fusion, being a managed service on Google Cloud Platform, offers automatic scaling and provides built-in load balancing for handling large workloads. It also ensures high availability and reliability through redundancy and fault tolerance.

  5. Cost and pricing model: CDAP is an open-source platform, which means it is free to use, and there are no additional licensing costs. However, there may be costs associated with the infrastructure required to run CDAP clusters. On the other hand, Google Cloud Data Fusion follows a pay-as-you-go pricing model, where you pay for the resources used, such as data ingestion, processing, and storage. The cost varies based on the amount of data processed and the services utilized within the Google Cloud Platform ecosystem.

  6. Community and support: CDAP has a strong and active open-source community, providing support and resources for users. There are forums, documentation, and community-contributed plugins available for users to share knowledge and troubleshoot issues. Google Cloud Data Fusion, being a managed service by Google, offers comprehensive support from the Google Cloud support team. It provides documentation, tutorials, and also offers customer support for any technical issues or questions.

In summary, CDAP is an open-source platform built on Apache Hadoop and Spark, offering scalability, flexibility, and integration with various data technologies. Google Cloud Data Fusion, on the other hand, is a fully-managed service on Google Cloud Platform, providing a visual interface, seamless integration with Google Cloud services, automatic scaling, and a pay-as-you-go pricing model.

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Detailed Comparison

CDAP
CDAP
Google Cloud Data Fusion
Google Cloud Data Fusion

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.

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.

Streams for data ingestion;Reusable libraries for common Big Data access patterns;Data available to multiple applications and different paradigms;Framework level guarantees;Full development lifecycle and production deployment;Standardization of applications across programming paradigms
Code-free self-service; Collaborative data engineering; GCP-native; Enterprise-grade security; Integration metadata and lineage; Seamless operations; Comprehensive integration toolkit; Hybrid enablement
Statistics
Stacks
41
Stacks
25
Followers
108
Followers
156
Votes
0
Votes
1
Pros & Cons
No community feedback yet
Pros
  • 1
    Lower total cost of pipeline ownership
Integrations
Hadoop
Hadoop
Google Cloud Storage
Google Cloud Storage
Google BigQuery
Google BigQuery

What are some alternatives to CDAP, Google Cloud Data Fusion?

Apache Spark

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.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

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

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.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

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.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

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