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Ratings and Reviews 0 Ratings
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DataBuckEnsuring the integrity of Big Data Quality is crucial for maintaining data that is secure, precise, and comprehensive. As data transitions across various IT infrastructures or is housed within Data Lakes, it faces significant challenges in reliability. The primary Big Data issues include: (i) Unidentified inaccuracies in the incoming data, (ii) the desynchronization of multiple data sources over time, (iii) unanticipated structural changes to data in downstream operations, and (iv) the complications arising from diverse IT platforms like Hadoop, Data Warehouses, and Cloud systems. When data shifts between these systems, such as moving from a Data Warehouse to a Hadoop ecosystem, NoSQL database, or Cloud services, it can encounter unforeseen problems. Additionally, data may fluctuate unexpectedly due to ineffective processes, haphazard data governance, poor storage solutions, and a lack of oversight regarding certain data sources, particularly those from external vendors. To address these challenges, DataBuck serves as an autonomous, self-learning validation and data matching tool specifically designed for Big Data Quality. By utilizing advanced algorithms, DataBuck enhances the verification process, ensuring a higher level of data trustworthiness and reliability throughout its lifecycle.
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What is PingDataGovernance?
The swift increase in digital transactions and data has resulted in a scenario where authorization logic is scattered across various parts of organizations. This disorganization can complicate the processes of updating, auditing, and managing such logic, often rendering it a tedious task. PingDataGovernance provides a solution to this challenge by delivering a unified system for authorization policies that can evaluate a range of factors, such as identity attributes, entitlements, and contextual details, to streamline the authorization of critical actions and access to important data. With this system, organizations can act quickly without sacrificing security or compliance with relevant regulations. Featuring a user-friendly drag-and-drop interface, it allows individuals to adjust policies in mere minutes. Moreover, organizations have the autonomy to choose which teams are permitted to manage these policies, enabling customized governance. Unlike traditional role-based access control (RBAC), dynamic authorization utilizes significant contextual data attributes to assess access requests in real-time, ensuring centralized policy enforcement that adheres to regulatory requirements. This forward-thinking method not only simplifies access control management but also boosts the overall agility of organizations in a fast-evolving digital environment, paving the way for more innovative workflows. As a result, businesses can better adapt to new challenges while maintaining the integrity of their security frameworks.
What is Google Cloud Dataplex?
Google Cloud's Dataplex acts as a sophisticated data fabric that enables businesses to efficiently discover, oversee, monitor, and govern their data across multiple platforms such as data lakes, warehouses, and marts, all while ensuring consistent controls that guarantee access to trustworthy data and support extensive analytics and AI projects. By providing a unified interface for managing data, Dataplex simplifies tasks such as data discovery, classification, and metadata enhancement for a range of data types, including structured, semi-structured, and unstructured data located both within Google Cloud and in external settings. It logically organizes data into business-relevant domains via lakes and data zones, thus facilitating easier data curation, tiering, and archiving processes. The platform's centralized security and governance capabilities allow for effective management of policies, comprehensive monitoring, and detailed auditing across disparate data silos, fostering a sense of distributed data ownership while ensuring overarching control. In addition, Dataplex features automated assessments of data quality and lineage tracking, which bolster the trustworthiness and traceability of data, assuring organizations of the reliability of their data-driven choices. By merging these features, Dataplex not only simplifies the intricacies of data management but also fosters improved collaboration among teams dedicated to analytics and AI, ultimately driving innovation and efficiency. This comprehensive approach equips organizations to harness their data assets more effectively in a rapidly evolving digital landscape.
Integrations Supported
Gemini
Google Cloud BigQuery
Google Cloud Dataflow
Google Cloud Dataproc
Google Cloud Platform
RadiantOne
Vertex AI
Integrations Supported
Gemini
Google Cloud BigQuery
Google Cloud Dataflow
Google Cloud Dataproc
Google Cloud Platform
RadiantOne
Vertex AI
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
$0.060 per hour
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
Ping Identity
Date Founded
2002
Company Location
United States
Company Website
www.pingidentity.com/en/software/pingdatagovernance.html
Company Facts
Organization Name
Date Founded
1998
Company Location
United States
Company Website
cloud.google.com/dataplex
Categories and Features
Data Governance
Access Control
Data Discovery
Data Mapping
Data Profiling
Deletion Management
Email Management
Policy Management
Process Management
Roles Management
Storage Management
Categories and Features
Data Governance
Access Control
Data Discovery
Data Mapping
Data Profiling
Deletion Management
Email Management
Policy Management
Process Management
Roles Management
Storage Management
Data Lineage
Database Change Impact Analysis
Filter Lineage Links
Implicit Connection Discovery
Lineage Object Filtering
Object Lineage Tracing
Point-in-Time Visibility
User/Client/Target Connection Visibility
Visual & Text Lineage View
Data Management
Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge