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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

Description

At Datametica, our innovative solutions significantly reduce risks and alleviate costs, time, frustration, and anxiety throughout the data warehouse migration process to the cloud. We facilitate the transition of your current data warehouse, data lake, ETL, and enterprise business intelligence systems to your preferred cloud environment through our automated product suite. Our approach involves crafting a comprehensive migration strategy that includes workload discovery, assessment, planning, and cloud optimization. With our Eagle tool, we provide insights from the initial discovery and assessment phases of your existing data warehouse to the development of a tailored migration strategy, detailing what data needs to be moved, the optimal sequence for migration, and the anticipated timelines and expenses. This thorough overview of workloads and planning not only minimizes migration risks but also ensures that business operations remain unaffected during the transition. Furthermore, our commitment to a seamless migration process helps organizations embrace cloud technologies with confidence and clarity.

Description

Our cutting-edge data migration solution is meticulously crafted to seamlessly transfer and convert data from outdated legacy systems to modern frameworks, featuring robust data validation processes and instant reporting capabilities. Frequently, during the data migration journey, critical information may be lost or compromised, leading to significant challenges. The transition from older systems to newer ones entails a complicated and lengthy procedure. While it might be tempting to take shortcuts or to merge data without the necessary tools, such approaches often lead to expensive and prolonged frustrations. For institutions like State Agencies, the stakes are too high to risk errors during the initial transfer. This phase is notoriously difficult, and many organizations struggle to execute it successfully. A successful data migration initiative relies heavily on a solid initial design, which serves as the blueprint for the entire project. This stage involves carefully crafting and coding the rules needed to process various data types according to your unique requirements, ensuring a smoother migration experience. Ultimately, investing time and resources at this stage can significantly enhance the overall efficiency and accuracy of the migration process.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS)
Google Cloud Platform
Hadoop
IBM Netezza Performance Server
Microsoft Azure
Snowflake

Integrations

Amazon Web Services (AWS)
Google Cloud Platform
Hadoop
IBM Netezza Performance Server
Microsoft Azure
Snowflake

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

Datametica

Founded

2013

Country

India

Website

www.datametica.com

Vendor Details

Company Name

Pritna

Founded

2015

Country

United States

Website

pritna.com/products/data-migration-tool/

Product Features

Data Discovery

Contextual Search
Data Classification
Data Matching
False Positives Reduction
Self Service Data Preparation
Sensitive Data Identification
Visual 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

ETL

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

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