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What is Oracle Cloud Infrastructure Data Lakehouse?

A data lakehouse embodies a modern, open architecture tailored for the storage, understanding, and analysis of large data sets. It combines the strong features of traditional data warehouses with the considerable adaptability provided by popular open-source data technologies currently in use. Building a data lakehouse is feasible on Oracle Cloud Infrastructure (OCI), which supports effortless integration with advanced AI frameworks and pre-built AI services, including Oracle’s language processing tools. Users can utilize Data Flow, a serverless Spark service, enabling them to focus on their Spark tasks without the hassle of infrastructure management. Many clients of Oracle seek to create advanced analytics driven by machine learning, applicable to their Oracle SaaS data or other SaaS sources. In addition, our intuitive data integration connectors simplify the setup of a lakehouse, promoting comprehensive analysis of all data alongside your SaaS information and considerably speeding up the solution delivery process. This groundbreaking methodology not only streamlines data governance but also significantly boosts analytical prowess for organizations aiming to harness their data more efficiently. Ultimately, the integration of these technologies empowers businesses to make data-driven decisions with greater agility and insight.

What is Dremio?

Dremio offers rapid query capabilities along with a self-service semantic layer that interacts directly with your data lake storage, eliminating the need to transfer data into exclusive data warehouses, and avoiding the use of cubes, aggregation tables, or extracts. This empowers data architects with both flexibility and control while providing data consumers with a self-service experience. By leveraging technologies such as Apache Arrow, Data Reflections, Columnar Cloud Cache (C3), and Predictive Pipelining, Dremio simplifies the process of querying data stored in your lake. An abstraction layer facilitates the application of security and business context by IT, enabling analysts and data scientists to access and explore data freely, thus allowing for the creation of new virtual datasets. Additionally, Dremio's semantic layer acts as an integrated, searchable catalog that indexes all metadata, making it easier for business users to interpret their data effectively. This semantic layer comprises virtual datasets and spaces that are both indexed and searchable, ensuring a seamless experience for users looking to derive insights from their data. Overall, Dremio not only streamlines data access but also enhances collaboration among various stakeholders within an organization.

Media

Media

Integrations Supported

NLSQL

Integrations Supported

AccessOwl
Apache Superset
Azure Marketplace
BI Book
DataClarity Unlimited Analytics
HPE Ezmeral
Hex
Looker
Microsoft Power BI
Microsoft Power Query
Okera
Preset
Privacera
Protegrity
SQL
Tableau
Yurbi
data.world
witboost

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Not specified

Company Facts

Organization Name

Oracle

Date Founded

1977

Company Location

United States

Company Website

www.oracle.com/data-lakehouse/

Company Facts

Organization Name

Dremio

Date Founded

2015

Company Location

United States

Company Website

www.dremio.com

Categories and Features

Data Lake

Not specified

Data Management

Not specified

Data Warehouse

Not specified

Categories and Features

Big Data

Not specified

Data Engineering

Not specified

Data Lake

Not specified

Data Lineage

Not specified

Data Virtualization

Not specified

Data Warehouse

Not specified

Query Engines

Not specified

Semantic Layer

Not specified

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