VuData enables organizations to gain a comprehensive understanding of the health of their data, system health, encompassing continuous monitoring and assessment of performance, reliability, and operational state. It uses Azure's Generative AI services for root cause analysis and Azure API Management for seamless integration and monitoring. This provides real-time insights into key metrics, anomalies, and potential issues, ensuring optimal functioning and rapid response to any disruptions. Additionally, VuData features AI-enabled, proactive monitoring and self-healing capabilities, allowing the system to automatically detect, diagnose, and remediate issues or anomalies without human intervention. This ensures continuous uptime and stability by addressing problems as soon as they arise, minimizing downtime and improving overall system reliability.
Full Description:
- Increasing data use cases
- Adaption of Cloud Infrastructure & Modern ecosystem
- Growing complexity of the data stack
- Sheer velocity, variety (I.e., structure, semi-structure, unstructured), observability data (I.e., Metrics, Logs, Event, Traces and Data)
- More complex issues such as schema changes, unexpected drifts, poor data quality, data downtimes, duplicate data
- Advanced analytics dashboards that heavily cater to data teams (data engineers, data scientists, data analysts)
- Multiple on-prem and cloud data source adaptors (I.e., For metadata and execution logs)
Our Solutions:
TechM’s solution (i.e., VuData) come with various features, which are broadly classified into the following categories. These features allow for near real-time monitoring and analysis of data, providing insights into systems, pipeline performance and identifying issues as they occur. This helps with the management of tickets, events, and data to provide analysis and help identifying trends and areas for continuous improvement.
Monitoring Features:
- Automated Live Monitoring
- Jobs Failure Monitoring
- Jobs Success Monitoring
- SLA Deviation
- Pipeline Run Summary
- Pipeline Trend Analysis
- Data Health Insights
- Event/Alert/Notification Analysis
Observer Features
- Failure Job Observation
- Long Running Jobs Observation
- Data Anomaly Observation
- Preventive Anomaly detection
- New/Missing Job Identification
Ticket Management Features
- Auto Ticket Creation
- Auto Ticket Closure
- Ticket Analysis
Data Management Features
- Job scheduling
- Job re-run
- Data lineage
Data Analysis Features
- Root Cause Analysis (RCA)
- Impact Analysis
- View Trendline
- Recommendation
- Ticket Classification
- Risk Identification
Business challenges
The below challenges that organizations face when managing data environments can lead to the implementation of a Data Observability initiative.
Data Complexity
- Data Stack Complexity
- Exploding Data Volumes
- Diverse Data Sources
Lack of Visibility
- Multiple Technologies and Platforms
- Inefficient Data Pipelines
- Incomplete or Inaccurate Metadata
- Lack of Real-Time Monitoring
Data Silos
- Disparate Data Sources
- Limited Data Access and Sharing
- Inconsistent Data Formats and Definitions
- Incomplete or Inaccurate Data Lineage
Data Governance
- Lack of Data Governance Policies and Procedures
- Manual or Inefficient Data Management Processes
- Incomplete or Inaccurate Data Cataloging and Profiling
- Inadequate Data Security and Privacy Controls
Organizational Culture
- Resistance to Change
- Lack of Data-Driven Decision Making
Skills and Expertise
- Lack of Skilled Data Professionals
- Insufficient Data Training and Education
Rapidly Expanding Business Requirements
- Inability to Scale Data Management and Analytics Capabilities
- Difficulty in Meeting Evolving Business Needs
Benefits
The following benefits can be achievable by Data Observability Solution.
- Improved Data Quality
- Reduced Downtime
- Increased Efficiency
- Better Data Governance
- Faster Time-to-Insight
- Improved Collaboration
- Predictive Analytics
The price listed is an indicative number. For finalizing the commercials, please contact us.