Average Ratings 0 Ratings
Average Ratings 6 Ratings
Description
Anomalo helps you get ahead of data issues by automatically detecting them as soon as they appear and before anyone else is impacted.
-Depth of Checks: Provides both foundational observability (automated checks for data freshness, volume, schema changes) and deep data quality monitoring (automated checks for data consistency and correctness).
-Automation: Use unsupervised machine learning to automatically identify missing and anomalous data.
-Easy for everyone, no-code UI: A user can generate a no-code check that calculates a metric, plots it over time, generates a time series model, sends intuitive alerts to tools like Slack, and returns a root cause analysis.
-Intelligent Alerting: Incredibly powerful unsupervised machine learning intelligently readjusts time series models and uses automatic secondary checks to weed out false positives.
-Time to Resolution: Automatically generates a root cause analysis that saves users time determining why an anomaly is occurring. Our triage feature orchestrates a resolution workflow and can integrate with many remediation steps, like ticketing systems.
-In-VPC Development: Data never leaves the customer’s environment. Anomalo can be run entirely in-VPC for the utmost in privacy & security
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
Databricks
Yes
Google Cloud BigQuery
Yes
SQL Server
Yes
Snowflake
Yes
AWS Glue
No
Alation
Yes
Amazon Athena
Yes
Amazon Redshift
Yes
Amazon S3
No
Amazon Web Services (AWS)
No
Integrations
Databricks
Yes
Google Cloud BigQuery
Yes
SQL Server
Yes
Snowflake
Yes
AWS Glue
Yes
Alation
No
Amazon Athena
No
Amazon Redshift
No
Amazon S3
Yes
Amazon Web Services (AWS)
Yes
Pricing Details
Free pilot. Secure In-VPC deployment. Contact us to get started.
- Automated data monitoring
- Simple installation
- Visualizations for reporting and explainability
- Automated data monitoring
- Simple installation
- Visualizations for reporting and explainability
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
Yes
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
No
Live Rep (24/7)
Yes
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
Anomalo
Founded
2018
Country
United States
Website
www.anomalo.com
Vendor Details
Company Name
FirstEigen
Founded
2015
Country
United States
Website
firsteigen.com/databuck/
Product Features
Data Quality
Address Validation
No
Data Deduplication
No
Data Discovery
No
Data Profililng
No
Master Data Management
No
Match & Merge
No
Metadata Management
Yes
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
