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What is Datametica?

At Datametica, our cutting-edge solutions play a pivotal role in minimizing risks and lowering costs, time, frustration, and anxiety associated with migrating data warehouses to the cloud. We streamline the transition of your existing data warehouse, data lake, ETL, and enterprise business intelligence systems to your chosen cloud platform through our suite of automated products. Our methodology encompasses the development of a robust migration strategy that incorporates workload discovery, assessment, planning, and cloud optimization. Utilizing our Eagle tool, we deliver valuable insights from the initial discovery and assessment stages of your current data warehouse to the creation of a customized migration strategy, which outlines the data to be transferred, the ideal sequence for migration, and projected timelines and costs. This detailed analysis of workloads and meticulous planning not only mitigates migration risks but also ensures that business operations experience no disruptions during the process. Moreover, our dedication to facilitating a smooth migration empowers organizations to adopt cloud technologies with both confidence and clarity, ultimately positioning them for future growth and innovation. By prioritizing a tailored approach, we ensure that each client's unique needs are met throughout the entire migration journey.

What is DataOps DataFlow?

Apache Spark offers a comprehensive component-driven platform that streamlines the automation of Data Reconciliation testing for contemporary Data Lake and Cloud Data Migration initiatives. DataOps DataFlow serves as an innovative web-based tool designed to facilitate the automation of testing for ETL projects, Data Warehouses, and Data Migrations. You can utilize DataFlow to efficiently load data from diverse sources, perform comparisons, and transfer discrepancies either into S3 or a Database. This enables users to create and execute data flows with remarkable ease. It stands out as a premier testing solution specifically tailored for Big Data Testing. Moreover, DataOps DataFlow seamlessly integrates with a wide array of both traditional and cutting-edge data sources, encompassing RDBMS, NoSQL databases, as well as cloud-based and file-based systems, ensuring versatility in data handling.

Media

Media

Integrations Supported

Snowflake
Amazon Redshift
Amazon Web Services (AWS)
Azure Synapse Analytics
Datagaps DataOps Suite
Google Cloud Platform
Hadoop
IBM Netezza Performance Server
Microsoft Azure
Microsoft Power BI
Tableau

Integrations Supported

Snowflake
Amazon Redshift
Amazon Web Services (AWS)
Azure Synapse Analytics
Datagaps DataOps Suite
Google Cloud Platform
Hadoop
IBM Netezza Performance Server
Microsoft Azure
Microsoft Power BI
Tableau

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Contact us
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

Datametica

Date Founded

2013

Company Location

India

Company Website

www.datametica.com

Company Facts

Organization Name

Datagaps

Date Founded

2010

Company Location

United States

Company Website

www.datagaps.com/dataops-dataflow/

Categories and 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

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

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