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What is Google Cloud Data Fusion?

Open core technology enables the seamless integration of hybrid and multi-cloud ecosystems. Based on the open-source project CDAP, Data Fusion ensures that users can easily transport their data pipelines wherever needed. The broad compatibility of CDAP with both on-premises solutions and public cloud platforms allows users of Cloud Data Fusion to break down data silos and tap into valuable insights that were previously inaccessible. Furthermore, its effortless compatibility with Google’s premier big data tools significantly enhances user satisfaction. By utilizing Google Cloud, Data Fusion not only bolsters data security but also guarantees that data is instantly available for comprehensive analysis. Whether you are building a data lake with Cloud Storage and Dataproc, loading data into BigQuery for extensive warehousing, or preparing data for a relational database like Cloud Spanner, the integration capabilities of Cloud Data Fusion enable fast and effective development while supporting rapid iterations. This all-encompassing strategy ultimately empowers organizations to unlock greater potential from their data resources, fostering innovation and informed decision-making. In an increasingly data-driven world, leveraging such technologies is crucial for maintaining a competitive edge.

What is Apache Hive?

Apache Hive serves as a data warehousing framework that empowers users to access, manipulate, and oversee large datasets spread across distributed systems using a SQL-like language. It facilitates the structuring of pre-existing data stored in various formats. Users have the option to interact with Hive through a command line interface or a JDBC driver. As a project under the auspices of the Apache Software Foundation, Apache Hive is continually supported by a group of dedicated volunteers. Originally integrated into the Apache® Hadoop® ecosystem, it has matured into a fully-fledged top-level project with its own identity. We encourage individuals to delve deeper into the project and contribute their expertise. To perform SQL operations on distributed datasets, conventional SQL queries must be run through the MapReduce Java API. However, Hive streamlines this task by providing a SQL abstraction, allowing users to execute queries in the form of HiveQL, thus eliminating the need for low-level Java API implementations. This results in a much more user-friendly and efficient experience for those accustomed to SQL, leading to greater productivity when dealing with vast amounts of data. Moreover, the adaptability of Hive makes it a valuable tool for a diverse range of data processing tasks.

Media

Media

Integrations Supported

Airtool
Amadea
Coginiti
Data Sentinel
Dataiku
Datalogz
Dropbox Dash
Google Cloud Data Catalog
Hackolade
IBM Cloud Mass Data Migration
LT Browser
Microsoft Power Query
Omniscope Evo
Privacera
Protegrity
StreamFlux
Tableau Catalog
TiMi
Timbr.ai
Xtendlabs

Integrations Supported

Airtool
Amadea
Coginiti
Data Sentinel
Dataiku
Datalogz
Dropbox Dash
Google Cloud Data Catalog
Hackolade
IBM Cloud Mass Data Migration
LT Browser
Microsoft Power Query
Omniscope Evo
Privacera
Protegrity
StreamFlux
Tableau Catalog
TiMi
Timbr.ai
Xtendlabs

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Google

Company Location

United States

Company Website

cloud.google.com/data-fusion

Company Facts

Organization Name

Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

hive.apache.org

Categories and Features

ETL

Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control

Categories and Features

ETL

Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control

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