Elementary Data Features
What are the features of Elementary Data?
Functionality
- Real-time Analytics
- Data quality monitoring
- Automation
- End to End visiblity
Management
- Anomaly identification
- Single pane view
- Real-time alerts
- Data lineage
- Integrations
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Elementary Data Categories on G2
Filter for Features
Functionality
Monitoring | Monitors database functionality to verify baselines are maintained or exceeded. | Not enough data | |
Alerting | Sends alerts via email, text, phone, and more when an incident or issue occcurs. | Not enough data | |
Logging | Captures logs for all database functions to garner greater information around issues or failures. | Not enough data | |
Response Time | Monitors database query time for unusual execution times | Not enough data | |
Reporting | Manually and/or automatically generates reports covering database performance | Not enough data | |
Data Visualization | Follows database monitoring live information through graphical dashboards | Not enough data | |
Identification | Correctly identify inaccurate, incomplete, or duplicated data from a data source. | Not enough data | |
Correction | Utilize deletion, modification, appending, merging, or other methods to correct bad data. | Not enough data | |
Normalization | Standardize data formatting for uniformity and easier data usage. | Not enough data | |
Preventative Cleaning | Clean data as it enters the data source to prevent mixing bad data with cleaned data. | Not enough data | |
Data Matching | Finds duplicates using the fuzzy logic technology or an advance search feature. | Not enough data | |
Real-time Analytics | Generate real-time depth analytics utilizing event metrics, logging and metadata. 12 reviewers of Elementary Data have provided feedback on this feature. | 51% (Based on 12 reviews) | |
Data quality monitoring | Use custom or pre-built tests for buisness rules. to ensure data quality. This feature was mentioned in 12 Elementary Data reviews. | 90% (Based on 12 reviews) | |
Automation | As reported in 13 Elementary Data reviews. Involves automation capabilities to identify and track issues, failed operations by looking at historical trends. | 86% (Based on 13 reviews) | |
End to End visiblity | As reported in 12 Elementary Data reviews. Complete visibility of the data pipeline, and immediately notifies the data team if any issues. Ensures cross stack visbility. | 79% (Based on 12 reviews) |
Management
Reporting | Provide follow-up information after data cleanings through a visual dashboard or reports. | Not enough data | |
Automation | Automatically run data identification, correction, and normalization on data sources. | Not enough data | |
Quality Audits | Schedule automated audits to identify data anomalies over time based on set business rules. | Not enough data | |
Dashboard | Gives a view of the entire data quality management ecosystem. | Not enough data | |
Governance | Allows user role-based access and actions to authorization for specific tasks. | Not enough data | |
Anomaly identification | Identify the different type of anomalies and receive alerts. This feature was mentioned in 12 Elementary Data reviews. | 89% (Based on 12 reviews) | |
Single pane view | The data observability environment can be viewed from a single dashboard. This feature was mentioned in 12 Elementary Data reviews. | 86% (Based on 12 reviews) | |
Real-time alerts | Based on 13 Elementary Data reviews. Provides immediate alerts for any anomalies or expected events. | 78% (Based on 13 reviews) | |
Data lineage | Establishes lineage for the data pipeline - from data warehouse to the data user. 12 reviewers of Elementary Data have provided feedback on this feature. | 76% (Based on 12 reviews) | |
Integrations | Support integrations with various business applications which support different data processes. Also, integrate with apps to provide alerts. This feature was mentioned in 12 Elementary Data reviews. | 78% (Based on 12 reviews) |
Data Management
Data Integration | Integrates data and data-related technologies into a single environment. | Not enough data | |
Metadata | Provides metadata management capabilities. | Not enough data | |
Self-service | Empowers the user via a self-service capability to manage data workflows. | Not enough data | |
Automated workflows | Completely automates end-to-end data workflows across the data integration lifecycle. | Not enough data |
Analytics
Analytics capabilities | Provides a high performance, flexibile analytics platform to support data management and embrace data driven decision making. | Not enough data | |
Dasboard visualizations | Collect and displays metrics across the data integration via a dashboard. | Not enough data |
Monitoring and Management
Data Observability | Involved solely in monitoring data pipelines, sending alerts and troubleshooting data. | Not enough data | |
Testing capabilities | Deploys testing capabilities such as report testing, big data testing, cloud data migration testing, ETL and data warehouse testing. | Not enough data |
Cloud Deployment
Hybrid cloud support | Supports analytical platforms and data pipelines across complex hybrid environments. | Not enough data | |
Cloud migration capabilities | Supports migration of component or pipeline to different cloud environments. | Not enough data |
Generative AI
AI Text Generation | Allows users to generate text based on a text prompt. | Not enough data | |
AI Text Summarization | Condenses long documents or text into a brief summary. | Not enough data | |
AI Text Generation | Allows users to generate text based on a text prompt. | Not enough data | |
AI Text Summarization | Condenses long documents or text into a brief summary. | Not enough data |
