Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
Traditional Data Lakes frequently simplify their role to merely serving as inexpensive raw data repositories, overlooking crucial elements such as data transformation, quality assurance, and security protocols. Consequently, data scientists often find themselves dedicating as much as 80% of their time to the processes of data acquisition, comprehension, and cleansing, which delays their ability to leverage their primary skills effectively. Furthermore, the establishment of traditional Data Lakes tends to occur in isolation by various departments, each utilizing different standards and tools, complicating the implementation of cohesive analytical initiatives. In contrast, Smart Data Lakes address these challenges by offering both architectural and methodological frameworks, alongside a robust toolset designed to create a high-quality data infrastructure. Essential to any contemporary analytics platform, Smart Data Lakes facilitate seamless integration with popular Data Science tools and open-source technologies, including those used for artificial intelligence and machine learning applications. Their cost-effective and scalable storage solutions accommodate a wide range of data types, including unstructured data and intricate data models, thereby enhancing overall analytical capabilities. This adaptability not only streamlines operations but also fosters collaboration across different departments, ultimately leading to more informed decision-making.
API Access
Has API
No
API Access
Has API
No
Integrations
Hadoop
Yes
Amazon S3
No
Amazon Web Services (AWS)
Yes
Apache Kafka
No
Apache Spark
No
Google Cloud Platform
Yes
IBM Netezza Performance Server
Yes
Java
No
Microsoft Azure
Yes
Python
No
Integrations
Hadoop
Yes
Amazon S3
Yes
Amazon Web Services (AWS)
No
Apache Kafka
Yes
Apache Spark
Yes
Google Cloud Platform
No
IBM Netezza Performance Server
No
Java
Yes
Microsoft Azure
No
Python
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
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
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
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
Datametica
Founded
2013
Country
India
Website
www.datametica.com
Vendor Details
Company Name
ELCA Group
Founded
1968
Country
Switzerland
Website
www.elca.ch/en/smart-data-lake-builder-efficient-way-lay-foundations-smarter-data-lake
Product Features
Data Discovery
Contextual Search
No
Data Classification
No
Data Matching
No
False Positives Reduction
No
Self Service Data Preparation
No
Sensitive Data Identification
No
Visual Analytics
No
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
ETL
Data Analysis
No
Data Filtering
No
Data Quality Control
No
Job Scheduling
No
Match & Merge
No
Metadata Management
No
Non-Relational Transformations
No
Version Control
No