Telmai Data Observability

What do you like best about the product?

Telmai has transformed our approach to data quality by identifying and automatically resolving data inconsistencies before they cause disruptions. This proactive monitoring minimizes reactive manual work and frees our engineering team to focus on strategic initiatives. The platform's intuitive interface empowers both business and technical users to define data policies, ensuring compliance while reducing risk. Telmai integrates seamlessly into our complex data environment, scaling to our needs without adding operational overhead or cloud costs.

What do you dislike about the product?

While the system offers basic error handling, built-in advanced error handling and support would be a great addition. The Telmai team has assured us that this feature is on their roadmap for later this year.

What problems is the product solving and how is that benefiting you?

With Telmai, defining data policies is simple for technical and business users, helping us meet compliance standards and reduce risks effectively. Its ability to scale within our existing data environment without increasing operational overhead or cloud expenses ensures sustainability.

What do you like best about the product?

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.

What do you dislike about the product?

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.

What problems is the product 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.

What do you like best about the product?

I like Telmai because it is simple to track data quality and it quickly spot the issues without needing complicated setup. It saves time by automated checks and clear the alerts.

What do you dislike about the product?

Some deeper insights of Telmai application feels limited and understanding the detailed metrics takes extra attention. Few advanced options of Telmai feel less flexible than expected.

What problems is the product solving and how is that benefiting you?

Telmai helps me to catch the data issues early as compared to other applications. It reduces manual efforts and keeps the data quality consistent. Telmai gives me more confidence in reporting as it identifies data gaps easily.

What do you like best about the product?

The ease of implementation, the quality of customer support, and how often I use the product are all important factors for me.

What do you dislike about the product?

To be honest, there isn't really anything noteworthy in my opinion.

What problems is the product solving and how is that benefiting you?

to keep my data safe and secure

What do you like best about the product?

he most helpful thing about Telmai is that it provides complete, end-to-end data observability without relying on sampling. It continuously monitors data across the entire pipeline — from ingestion to consumption, detecting anomalies, drifts, and quality issues in real time. Its support for multiple data formats and open architectures makes it easy to integrate with modern data stacks, while the no/low-code interface helps both technical and business users identify and fix problems quickly. Overall, Telmai is most helpful in ensuring trust, accuracy, and reliability of enterprise data with minimal operational effort.

The main upsides of using Telmai are its strong data reliability, visibility, and ease of use. It ensures full data observability across pipelines without sampling, so every record is checked for accuracy. Telmai’s real-time anomaly detection helps catch issues early, preventing bad data from affecting analytics or AI models. It’s highly integrative, supporting diverse data formats and cloud or on-prem systems, making it flexible for complex enterprise environments. The low-code interface and automated insights save time for data engineers and make it accessible to business teams too. Overall, Telmai improves data quality, trust, and operational efficiency while reducing manual monitoring effort.

What do you dislike about the product?

he main downsides of Telmai are its initial learning curve and the effort needed to train and configure it effectively. Managing a large number of data assets can be a bit cumbersome, and some users feel the UI could be more intuitive for large-scale operations. Additionally, because Telmai offers full data validation and advanced monitoring, it can be costlier than simpler data-quality tools. Overall, while powerful, it requires some setup time and planning to get the most value from it.

What problems is the product solving and how is that benefiting you?

Telmai solves the problem of poor data visibility and quality across complex data pipelines. It detects issues like missing values, schema drifts, and data anomalies in real time, ensuring every record is validated instead of relying on samples. This helps catch problems early, preventing bad data from affecting analytics or AI models. For users, it brings greater trust, faster issue resolution, and reduced manual monitoring, ultimately improving data reliability and decision-making across the organization.