Average Ratings 6 Ratings
Average Ratings 1 Rating
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.
Description
iceDQ, a DataOps platform that allows monitoring and testing, is a DataOps platform. iceDQ is an agile rules engine that automates ETL Testing, Data Migration Testing and Big Data Testing. It increases productivity and reduces project timelines for testing data warehouses and ETL projects. Identify data problems in your Data Warehouse, Big Data, and Data Migration Projects. The iceDQ platform can transform your ETL or Data Warehouse Testing landscape. It automates it from end to end, allowing the user to focus on analyzing the issues and fixing them. The first edition of iceDQ was designed to validate and test any volume of data with our in-memory engine. It can perform complex validation using SQL and Groovy. It is optimized for Data Warehouse Testing. It scales based upon the number of cores on a server and is 5X faster that the standard edition.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
Cloudera
Yes
AWS Glue
Yes
Amazon S3
Yes
Amazon Web Services (AWS)
Yes
Apache Airflow
Yes
Azure Cosmos DB
Yes
Azure SQL Database
Yes
Databricks
Yes
Google Cloud BigQuery
Yes
Google Cloud Dataflow
Yes
Integrations
Cloudera
Yes
AWS Glue
No
Amazon S3
No
Amazon Web Services (AWS)
No
Apache Airflow
No
Azure Cosmos DB
No
Azure SQL Database
No
Databricks
No
Google Cloud BigQuery
No
Google Cloud Dataflow
No
Pricing Details
Consumption-based and annual fixed licensing fee are both available.
Free Trial
No
Free Version
No
Pricing Details
$1000
Free Trial
Yes
Free Version
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
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
No
Linux
Yes
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
Yes
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
FirstEigen
Founded
2015
Country
United States
Website
firsteigen.com/databuck/
Vendor Details
Company Name
iceDQ
Founded
2005
Country
United States
Website
icedq.com
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
Product Features
Automated Testing
Hierarchical View
No
Move & Copy
Yes
Parameterized Testing
Yes
Requirements-Based Testing
Yes
Security Testing
No
Supports Parallel Execution
Yes
Test Script Reviews
No
Unicode Compliance
No
Big Data
Collaboration
No
Data Blends
No
Data Cleansing
No
Data Mining
No
Data Visualization
No
Data Warehousing
No
High Volume Processing
No
No-Code Sandbox
No
Predictive Analytics
No
Templates
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
Data Warehouse
Ad hoc Query
No
Analytics
No
Data Integration
No
Data Migration
No
Data Quality Control
No
ETL - Extract / Transfer / Load
No
In-Memory Processing
No
Match & Merge
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
