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OORT DataHubOur innovative decentralized platform enhances the process of AI data collection and labeling by utilizing a vast network of global contributors. By merging the capabilities of crowdsourcing with the security of blockchain technology, we provide high-quality datasets that are easily traceable. Key Features of the Platform: Global Contributor Access: Leverage a diverse pool of contributors for extensive data collection. Blockchain Integrity: Each input is meticulously monitored and confirmed on the blockchain. Commitment to Excellence: Professional validation guarantees top-notch data quality. Advantages of Using Our Platform: Accelerated data collection processes. Thorough provenance tracking for all datasets. Datasets that are validated and ready for immediate AI applications. Economically efficient operations on a global scale. Adaptable network of contributors to meet varied needs. Operational Process: Identify Your Requirements: Outline the specifics of your data collection project. Engagement of Contributors: Global contributors are alerted and begin the data gathering process. Quality Assurance: A human verification layer is implemented to authenticate all contributions. Sample Assessment: Review a sample of the dataset for your approval. Final Submission: Once approved, the complete dataset is delivered to you, ensuring it meets your expectations. This thorough approach guarantees that you receive the highest quality data tailored to your needs.
What is Precog?
Precog is a sophisticated platform designed for the integration and transformation of data, allowing businesses to seamlessly access, prepare, and analyze information from diverse sources. With its intuitive no-code interface and powerful automation features, Precog simplifies the connection to various data sources, transforming raw data into valuable insights without requiring any technical expertise. Additionally, the platform integrates effortlessly with popular analytics tools, which helps users to expedite their data-driven decision-making processes. By minimizing complexity and offering remarkable flexibility, Precog enables organizations to fully exploit their data's potential, ultimately improving workflow efficiency and fostering innovation across multiple teams and industries. Its user-centric design guarantees that even individuals without a technical background can effectively utilize data, making it an accessible solution for all. In this way, Precog not only enhances operational capabilities but also promotes a culture of data literacy within organizations.
What is Datagaps ETL Validator?
DataOps ETL Validator is a comprehensive solution designed for automating the processes of data validation and ETL testing. It provides an effective means for validating ETL/ELT processes, simplifying the testing phases associated with data migration and warehouse projects, and includes a user-friendly interface that supports both low-code and no-code options for creating tests through a convenient drag-and-drop system. The ETL process involves extracting data from various sources, transforming it to align with operational requirements, and ultimately loading it into a specific database or data warehouse. Effective testing within this framework necessitates a meticulous approach to verifying the accuracy, integrity, and completeness of data as it moves through the different stages of the ETL pipeline, ensuring alignment with established business rules and specifications. By utilizing automation tools for ETL testing, companies can streamline data comparison, validation, and transformation processes, which not only speeds up testing but also reduces the reliance on manual efforts. The ETL Validator takes this automation a step further by facilitating the seamless creation of test cases through its intuitive interfaces, enabling teams to concentrate more on strategic planning and analytical tasks rather than getting bogged down by technical details. Consequently, it empowers organizations to enhance their data quality and improve operational efficiency significantly, fostering a culture of data-driven decision-making. Additionally, the tool's capabilities allow for easier collaboration among team members, promoting a more cohesive approach to data management.
Integrations Supported
Microsoft Power BI
Snowflake
Tableau
Amazon Redshift
Azure Databricks
Azure Synapse Analytics
Datagaps DataOps Suite
Eloqua
Emarsys
Google Cloud BigQuery
Integrations Supported
Microsoft Power BI
Snowflake
Tableau
Amazon Redshift
Azure Databricks
Azure Synapse Analytics
Datagaps DataOps Suite
Eloqua
Emarsys
Google Cloud BigQuery
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
Precog
Date Founded
2020
Company Location
United States
Company Website
precog.com
Company Facts
Organization Name
Datagaps
Company Location
United States
Company Website
www.datagaps.com/etl-validator/
Categories and Features
Artificial Intelligence
Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)
Data Replication
Asynchronous Data Replication
Automated Data Retention
Continuous Replication
Cross-Platform Replication
Dashboard
Instant Failover
Orchestration
Remote Database Replication
Reporting / Analytics
Simulation / Testing
Synchronous Data Replication
ETL
Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control
Categories and Features
ETL
Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control