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features
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support

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Description

Oracle Cloud Infrastructure (OCI) Data Flow is a comprehensive managed service for Apache Spark, enabling users to execute processing tasks on enormous data sets without the burden of deploying or managing infrastructure. This capability accelerates the delivery of applications, allowing developers to concentrate on building their apps rather than dealing with infrastructure concerns. OCI Data Flow autonomously manages the provisioning of infrastructure, network configurations, and dismantling after Spark jobs finish. It also oversees storage and security, significantly reducing the effort needed to create and maintain Spark applications for large-scale data analysis. Furthermore, with OCI Data Flow, there are no clusters that require installation, patching, or upgrading, which translates to both time savings and reduced operational expenses for various projects. Each Spark job is executed using private dedicated resources, which removes the necessity for prior capacity planning. Consequently, organizations benefit from a pay-as-you-go model, only incurring costs for the infrastructure resources utilized during the execution of Spark jobs. This innovative approach not only streamlines the process but also enhances scalability and flexibility for data-driven applications.

Description

A data lakehouse represents a contemporary, open architecture designed for storing, comprehending, and analyzing comprehensive data sets. It merges the robust capabilities of traditional data warehouses with the extensive flexibility offered by widely used open-source data technologies available today. Constructing a data lakehouse can be accomplished on Oracle Cloud Infrastructure (OCI), allowing seamless integration with cutting-edge AI frameworks and pre-configured AI services such as Oracle’s language processing capabilities. With Data Flow, a serverless Spark service, users can concentrate on their Spark workloads without needing to manage underlying infrastructure. Many Oracle clients aim to develop sophisticated analytics powered by machine learning, applied to their Oracle SaaS data or other SaaS data sources. Furthermore, our user-friendly data integration connectors streamline the process of establishing a lakehouse, facilitating thorough analysis of all data in conjunction with your SaaS data and significantly accelerating the time to achieve solutions. This innovative approach not only optimizes data management but also enhances analytical capabilities for businesses looking to leverage their data effectively.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Apache Spark Yes 
NLSQL No 
Oracle Cloud Infrastructure Yes 

Integrations

Apache Spark No 
NLSQL Yes 
Oracle Cloud Infrastructure No 

Pricing Details

$0.0085 per GB per hour
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Oracle

Founded

1977

Country

United States

Website

www.oracle.com/big-data/data-flow/

Vendor Details

Company Name

Oracle

Founded

1977

Country

United States

Website

www.oracle.com/data-lakehouse/

Product Features

Big Data

Collaboration No 
Data Blends No 
Data Cleansing No 
Data Mining No 
Data Visualization No 
Data Warehousing No 
High Volume Processing No 
No-Code Sandbox No 
Predictive Analytics No 
Templates No 

Data Science

Access Control No 
Advanced Modeling No 
Audit Logs No 
Data Discovery No 
Data Ingestion No 
Data Preparation No 
Data Visualization No 
Model Deployment No 
Reports No 

Product Features

Data Management

Customer Data No 
Data Analysis No 
Data Capture No 
Data Integration No 
Data Migration No 
Data Quality Control No 
Data Security No 
Information Governance No 
Master Data Management No 
Match & Merge No 

Data Warehouse

Ad hoc Query No 
Analytics No 
Data Integration No 
Data Migration No 
Data Quality Control No 
ETL - Extract / Transfer / Load No 
In-Memory Processing No 
Match & Merge No 

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