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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.
What is QueryPie?
QueryPie functions as an all-encompassing platform that integrates a variety of data sources and security measures into one seamless interface. Propel your organization’s progress towards success without needing to modify your existing data framework. In a landscape increasingly influenced by data, robust data governance has risen to paramount importance. Ensure compliance with data governance standards while simultaneously providing numerous users with access to a growing array of critical information. Develop data access protocols that take into account essential elements such as IP addresses and access times. You are able to establish different levels of access based on SQL commands divided into categories such as DML, DCL, and DDL, which facilitates secure data analysis and alterations. Effortlessly keep track of SQL event specifics and scrutinize user activities along with possible security risks by reviewing logs filtered through permissions. Additionally, all past data can be exported for detailed reporting, guaranteeing a comprehensive understanding of your data utilization and security measures. This integrated strategy not only improves oversight but also fosters informed decision-making for upcoming initiatives, thereby paving the way for a more secure and efficient data environment. Ultimately, QueryPie empowers organizations to harness their data responsibly while driving innovation and growth.
What is Immuta?
Immuta's Data Access Platform is designed to provide data teams with both secure and efficient access to their data. Organizations are increasingly facing intricate data policies due to the ever-evolving landscape of regulations surrounding data management.
Immuta enhances the capabilities of data teams by automating the identification and categorization of both new and existing datasets, which accelerates the realization of value; it also orchestrates the application of data policies through Policy-as-Code (PaC), data masking, and Privacy Enhancing Technologies (PETs) so that both technical and business stakeholders can manage and protect data effectively; additionally, it enables the automated monitoring and auditing of user actions and policy compliance to ensure verifiable adherence to regulations. The platform seamlessly integrates with leading cloud data solutions like Snowflake, Databricks, Starburst, Trino, Amazon Redshift, Google BigQuery, and Azure Synapse.
Our platform ensures that data access is secured transparently without compromising performance levels. With Immuta, data teams can significantly enhance their data access speed by up to 100 times, reduce the number of necessary policies by 75 times, and meet compliance objectives reliably, all while fostering a culture of data stewardship and security within their organizations.
Integrations Supported
Amazon Redshift
Apache Hive
Azure SQL Database
Google Cloud BigQuery
MariaDB
MongoDB
MySQL
SAP HANA
SQL Server
Snowflake
Integrations Supported
Amazon Redshift
Apache Hive
Azure SQL Database
Google Cloud BigQuery
MariaDB
MongoDB
MySQL
SAP HANA
SQL Server
Snowflake
API Availability
API Availability
Has API
Pricing Information
Pricing not provided
Pricing Information
Consumption based or enterprise subscription.
Free Trial Offered?
Supported Platforms
SaaS
Supported Platforms
SaaS
Customer Service / Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
QueryPie
Date Founded
2019
Company Location
South Korea
Company Website
www.querypie.com
Company Facts
Organization Name
Immuta
Date Founded
2015
Company Location
United States
Company Website
immuta.com
Categories and Features
Data Governance
Not specified
Sensitive Data Discovery
Not specified
Categories and Features
Big Data
Collaboration
High Volume Processing
No-Code Sandbox
Predictive Analytics
Cloud Security
Encryption
Cloud Workload Protection
Data Security
Governance
Logging & Reporting
Data De-Identification
Not specified
Data Governance
Access Control
Data Discovery
Data Mapping
Data Profiling
Policy Management
Data Lineage
Not specified
Data Management
Not specified
Data Masking
Not specified
Data Privacy Management
Access Control
CCPA Compliance
Consent Management
Data Mapping
GDPR Compliance
Policy Management
Sensitive Data Identification
Data Quality
Data Discovery
Data Profililng
Metadata Management
Data Security
Alerts / Notifications
Data Classification
Data Discovery
Data Masking
Database Security
Encryption
Logging / Reporting
Policy Management
Sensitive Data Compliance
Data Security Posture Management (DSPM)
Not specified
Data Visualization
Analytics
Dashboard Creation
Visual Discovery
Database Security
Not specified
Encryption
Central Policy Enforcement
Drag & Drop UI
Tokenization / Data Masking
GDPR Compliance
Access Control
Consent Management
Data Mapping
Policy Management
Risk Management
Sensitive Data Identification
HIPAA Compliance
Access Control / Permissions
Audit Management
Compliance Reporting
Data Security
Machine Learning
Deep Learning
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Visualization
Sensitive Data Discovery
Not specified