![Valerie S.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Valerie S.") VS Valerie S. Senior Data Reliability Engineer Enterprise (\> 1000 emp.) 1/23/2026 "Telmai is essential for maintaining trust and reliability in production data pipelines" 5/5 What do you like best about Telmai? Telmai has fundamentally changed how we approach data reliability at scale. The ML-driven anomaly detection catches issues in our pipelines before they impact downstream consumers—things like schema drift, data freshness violations, and statistical outliers that would be nearly impossible to monitor manually across our data estate. What sets it apart is the balance between automation and control: we get intelligent, out-of-the-box checks that adapt to our data patterns, but we can also define custom validation rules for business-critical datasets. This has shifted our team from firefighting data incidents to proactively maintaining SLAs, and the automated alerting means stakeholders get notified immediately when thresholds are breached. The integration with our existing stack of cloud data storage providers like Amazon S3, Google, Snowflake, and Starburst was straightforward, and it scales without adding significant compute overhead. Telmai has become a non-negotiable part of our data reliability infrastructure. I am using the tool on a daily basis. Additionally, the support from the Telmai team has been fantastic. They are always willing to help with a tricky SQL-based data quality rule implementation or to help troubleshoot any issues we're having with the platform. The team is very receptive to our product feedback as well. Review collected by and hosted on G2.com. What do you dislike about Telmai? The system’s basic error handling works well, but initially we needed to build a more robust alerting workflow outside of Telmai. We connected Telmai's email data alerts to trigger a Zapier workflow, pushing tickets to Jira and alerts to Slack. This workflow took some extra time to set up. The team has since implemented new features like a direct Slack integration, but since we already have a setup that works we haven't tested it out yet. With the improvements the team has already made it's likely that this outside workflow would not be needed today. Review collected by and hosted on G2.com. What problems is Telmai solving and how is that benefiting you? Prior to implementing Telmai, our data quality management was almost entirely reactive. We typically discovered issues only after they had already impacted end users—whether through customer reports, escalations from our GTM teams, or ad-hoc queries from analysts and engineers who happened to encounter anomalies in their workflows. This created significant lag time between when issues were introduced and when they were detected, often resulting in downstream impacts to business decisions and customer trust. Telmai's comprehensive suite of out-of-the-box monitors has fundamentally shifted us from reactive to proactive data quality management. We now catch data anomalies—including freshness delays, schema changes, null rate spikes, and statistical outliers—within minutes of occurrence, often before any downstream consumer is affected. More importantly, we're identifying entire categories of data quality issues that would have gone completely undetected under our previous manual approach, such as subtle distribution shifts or gradual data degradation over time. One of the most valuable workflow improvements has been incorporating Telmai monitoring into our incident resolution process. For every data quality issue we remediate, we now establish Telmai observability as part of our "definition of done." This means we're building a comprehensive safety net of monitors that guard against regression—if a pipeline breaks in the same way again, or if we ingest data from a problematic source exhibiting similar quality issues, our team is immediately alerted and can triage and resolve the problem before it propagates. This has dramatically reduced our mean time to detection (MTTD) and mean time to resolution (MTTR). Telmai hasn't just improved our data quality—it's transformed our entire operational model around data reliability and observability. Review collected by and hosted on G2.com. Show More Current UserValidated ReviewerSource: Organic See what 22 reviewers think of Telmai 4.9 out of 5 · Verified reviews from real users [ Read all reviews ](https://www.g2.com/products/telmai/reviews)