Telmai defines Freshness by considering both the data and the metadata about the table. For example, a table that was updated in the last hour, and therefore carrying an updated timestamp (i.e. metadata,) may not have fresh, accurate, and up-to-date data for critical attributes or entities within the content of the table. Telmai does not solely rely on metadata. Instead, it also looks at the actual data values to determine Freshness as a metric. For more information, please refer to this article.

Telmai defines Freshness by considering both the data and the metadata about the table. For example, a table that was updated in the last hour, and therefore carrying an updated timestamp (i.e. metadata,) may have fresh, accurate and up to date data. Telmai does not solely rely on medata. Instead it also looks at the actual data values to determine Freshness as a metric.

Data lineage in its true form should provide end-to-end visibility of your entire data . This functionality is often best fit for a data catalog where data dependencies from ingest to downstream systems are captured along with proper metadata, ownership, change logs, and other important information. Through integrations, Telmai is able to detect and flag any data issues in your data catalog of choice and its data lineage features. 
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The data lineage offered in some Data Observability tools is designed to detect problems after the fact, where the data is already in its destinations (e.g. data warehouse). In this scenario, because data has already deteriorated, lineage helps detect the root cause of the problem. Telmai on the other hand is the Data Observability platform that gets integrated across all stages of the pipeline, not just the destination. Therefore, it is used to detect data issues earlier in the data life cycle and all throughout its different stages. As a result, a separate data lineage feature is not quite needed in Telmai.

Telmai is designed to decouple metrics calculations from the underlying databases and data pipelines. With this architecture, you will not be using the computing resources of your data warehouse and other cloud infrastructures. This will help you protect the performance of these operational systems, and at the same time give you a scalable data observability platform that detects anomalies across your entire data, without being bound to query runtime issues, data samples, or high infrastructure costs.

Telmai integrates with channels like Slack, Email, or to help you communicate and alert the right team at the right time. Telmai notifications include outliers, drifts, and other inconsistencies in your data. Using APIs you can also auto generate a ticket and assign it to the right source owner and integrate the information Telmai provides with your ticking systems. This helps the team immediately triage by fully understanding the issue and reducing its time to resolution.

Telmai also helps you set up auto-remediation through APIs. With our REST integration, you can build logic to automatically invoke a remediation flow as your data quality drifts. Telmai can be integrated into orchestration tools such as Airflow and DBT.

Telmai offers both a SaaS and a Private Cloud option. Depending on your security requirements you can choose the right option for you. We encrypt data both in transit and at rest. Telmai honors GDPR and CCPA as reflected in our Privacy Policy. Telmai is also SOC 2 compliant. For more information see our security page and our SOC 2 type 2 announcement.