Support actions and outcomes beyond alerting
Automate and orchestrate the response to data issues ensuring a continuous flow of reliable data, preventing disruptions before they impact downstream
Integrate into the ticketing system
- Notify data consumers of issues, ensuring data quality issues are managed before impacting operations
- Automatically generate detailed descriptions for JIRA, ServiceNow, and other ticket systems
Stop pipeline flow
- Stop the next step of the pipeline based on data quality SLA
- Reduce cloud cost of processing and remediation of bad jobs
- Control data pipeline flow based on ML-based anomalies or manual validations SLAs
Notify issues to the right team on the right channel
- Define granular alerting policies across rules and ML-anomalies
- Notify at the right channels – slack, pager duty, email
- No-over alerting, ensure that only the assigned team gets alerted
Exclude bad data from the AI workload
- Control the quality of data for analytical reports and AI workloads,park suspicious data in your raw storage or bronze layer
- Run your ML models on pre-processed data while controlling the noise level in the data
- Optimize cost, i.e., transfer, transform, and process data that has a high degree of data reliability
Turn alerts into action
Integrations with ticketing solutions like Jira allow your team to take action faster
More features
Connect Datasources
Connect your datasource, or send data via REST, or load a local file
Analyze Data Health
Quickly identify and pinpoint data anomalies, errors, or inconsistencies
Alert
Telmai will learn your data and its trends and automatically alert on unexpected drifts
Recommendations
Telmai will finally advice you on next best actions for your data sets
Open Architecture
No-code connection to Data lake and Lakehouse –natively support raw formats like Iceberg, Hudi and Delta
Data Quality
Validate every value before ingesting into AI-model, automate and orchestrate DQ workflows in AI-workloads
Anomaly Detection
No sampling, ML-driven anomaly detection on column-values and business metrics
Data Layer Consistency
Improve quality across bronze, silver and gold layers embedded design patterns to stop bad data at bronze
Data Health Analysis
No code analysis and reporting of your data lake and lake house
Incident Management
Alerting, ticketing, investigation and remediation workflows