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Description

AWS Lake Formation is a service designed to streamline the creation of a secure data lake in just a matter of days. A data lake serves as a centralized, carefully organized, and protected repository that accommodates all data, maintaining both its raw and processed formats for analytical purposes. By utilizing a data lake, organizations can eliminate data silos and integrate various analytical approaches, leading to deeper insights and more informed business choices. However, the traditional process of establishing and maintaining data lakes is often burdened with labor-intensive, complex, and time-consuming tasks. This includes activities such as importing data from various sources, overseeing data flows, configuring partitions, enabling encryption and managing encryption keys, defining and monitoring transformation jobs, reorganizing data into a columnar structure, removing duplicate records, and linking related entries. After data is successfully loaded into the data lake, it is essential to implement precise access controls for datasets and continuously monitor access across a broad spectrum of analytics and machine learning tools and services. The comprehensive management of these tasks can significantly enhance the overall efficiency and security of data handling within an organization.

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

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

AWS App Mesh
Amazon Athena
Amazon DataZone
Amazon EMR
Amazon Redshift
Amazon S3
Amazon SageMaker Feature Store
Collate
NLSQL
Velotix

Integrations

AWS App Mesh
Amazon Athena
Amazon DataZone
Amazon EMR
Amazon Redshift
Amazon S3
Amazon SageMaker Feature Store
Collate
NLSQL
Velotix

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/lake-formation/

Vendor Details

Company Name

Oracle

Founded

1977

Country

United States

Website

www.oracle.com/data-lakehouse/

Product Features

Product Features

Data Management

Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge

Data Warehouse

Ad hoc Query
Analytics
Data Integration
Data Migration
Data Quality Control
ETL - Extract / Transfer / Load
In-Memory Processing
Match & Merge

Alternatives

Alternatives

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