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What is dataFactory?

Remove all manual processes, potential sources of error, and inefficiencies to streamline operations. Eliminate the necessity to frequently reconfigure your data warehouse with each change in business needs. Introduce automated quality checks both among and within data sources and ensure prompt responses to any arising issues, as this is crucial for many data users. It is vital to have complete confidence in your data today. Establish a "gold record" reference point that guarantees business teams can always access the latest information available. Create a single, unified version of the truth that is accessible anytime and from anywhere. Develop an intermediate model that organizes, stores, and maintains your data independently of its intended use. Be quick to adapt to changing data sources and business questions. Integrate all data sources—from data lakes and operational systems to spreadsheets and legacy applications—just as you did with the initial one. Ensure that data is stored, preserved, and improved in quality to facilitate the automation of data warehouse processes. Organize, enrich, and meticulously document your data so that it is readily available in well-structured datasets, often referred to as information marts. By doing this, you lay the foundation for more effective decision-making throughout the organization, ultimately fostering a culture of data-driven insights and innovation.

What is Data Lakes on AWS?

A multitude of Amazon Web Services (AWS) users are in search of a data storage and analytics option that outperforms traditional data management systems in terms of flexibility and agility. The emergence of data lakes has proven to be a groundbreaking and increasingly popular approach for data storage and analysis, allowing businesses to manage a wide array of data types from multiple sources within a single repository that supports both structured and unstructured data. AWS Cloud offers vital elements that empower customers to develop a secure, versatile, and cost-effective data lake. These elements include AWS managed services that facilitate the ingestion, storage, discovery, processing, and analysis of diverse data formats. To support clients in building their data lakes, AWS presents a thorough data lake solution that acts as an automated reference implementation, laying out a highly available and economical data lake architecture on the AWS Cloud, complemented by a user-friendly console for searching and accessing datasets. Additionally, this solution not only improves the accessibility of data but also simplifies the entire data management process, ultimately leading to enhanced operational efficiency for organizations. As a result, companies can leverage these advantages to make more informed decisions based on their data insights.

Media

Media

Integrations Supported

Google Sheets
Microsoft Excel
Microsoft Power BI
Qlik Data Integration
Tableau
Toucan

Integrations Supported

Google Sheets
Microsoft Excel
Microsoft Power BI
Qlik Data Integration
Tableau
Toucan

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

dFakto

Date Founded

2000

Company Location

Belgium

Company Website

www.dfakto.com/datafaktory-data-warehouse-automation/

Company Facts

Organization Name

Amazon

Date Founded

1994

Company Location

United States

Company Website

aws.amazon.com/solutions/implementations/data-lake-solution/

Categories and 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

Categories and Features

Data Analysis

Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics

Data Management

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

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