Average Ratings 0 Ratings
Average Ratings 6 Ratings
Description
Information flows in from various sources, increasing in both volume and intricacy. Within this information lies valuable knowledge and insights brimming with potential. This potential can only be fully harnessed when it influences every decision and action taken by the organization in real-time. As the landscape of business evolves, the data itself transforms, yielding fresh knowledge and insights. This establishes a continuous cycle of learning and adaptation. Sectors as diverse as finance, healthcare, telecommunications, manufacturing, transportation, and entertainment have acknowledged the opportunities this presents. The journey to capitalize on these opportunities is both formidable and exhilarating. Achieving success requires unprecedented levels of speed and agility in comprehending, managing, and processing vast quantities of ever-evolving data. For complex organizations to thrive, they need a high-performance data platform designed for automation and self-service, capable of flourishing amidst change and adjusting to new circumstances, while also addressing the most challenging data processing and management issues. In this rapidly evolving environment, organizations must commit to investing in innovative solutions that empower them to navigate the complexities of their data landscapes effectively.
Description
Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
AWS Glue
No
Amazon S3
No
Amazon Web Services (AWS)
No
Apache Airflow
No
Azure Cosmos DB
No
Azure SQL Database
No
Cloudera
No
DataHawk
Yes
Databricks
No
Google Cloud BigQuery
No
Integrations
AWS Glue
Yes
Amazon S3
Yes
Amazon Web Services (AWS)
Yes
Apache Airflow
Yes
Azure Cosmos DB
Yes
Azure SQL Database
Yes
Cloudera
Yes
DataHawk
No
Databricks
Yes
Google Cloud BigQuery
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
Consumption-based and annual fixed licensing fee are both available.
Free Trial
No
Free Version
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
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
Yes
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
No
Live Training (Online)
No
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
Ab Initio
Founded
1995
Country
United States
Website
www.abinitio.com/en/
Vendor Details
Company Name
FirstEigen
Founded
2015
Country
United States
Website
firsteigen.com/databuck/
Product Features
Data Governance
Access Control
No
Data Discovery
No
Data Mapping
No
Data Profiling
No
Deletion Management
No
Email Management
No
Policy Management
No
Process Management
No
Roles Management
No
Storage Management
No
Data Quality
Address Validation
No
Data Deduplication
No
Data Discovery
No
Data Profililng
No
Master Data Management
No
Match & Merge
No
Metadata Management
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
Product Features
Big Data
Collaboration
No
Data Blends
No
Data Cleansing
No
Data Mining
No
Data Visualization
No
Data Warehousing
No
High Volume Processing
Yes
No-Code Sandbox
No
Predictive Analytics
No
Templates
No
Data Governance
Access Control
No
Data Discovery
No
Data Mapping
No
Data Profiling
No
Deletion Management
No
Email Management
No
Policy Management
No
Process Management
No
Roles Management
No
Storage Management
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
Data Quality
Address Validation
No
Data Deduplication
No
Data Discovery
No
Data Profililng
Yes
Master Data Management
No
Match & Merge
No
Metadata Management
No
