Telmai Named a Leader in GigaOm 2023 Radar Report for Data Observability

Achieve End-to-End Data Quality

Quickstart with 40+ pre-defined metrics

Telmai uses machine learning to scan all your datasets and generate a set of health metrics for each including uniqueness, volume, distribution, pattern drifts, schema changes, and more. These metrics are automatically classified into Data Quality Key Indicators (KPIs) like accuracy, validity, freshness, completeness, and more.

ML and metadata driven metrics

Telmai uses both the data values itself and the metadata to identify and predict your data quality issues. Telmai uses metadata to provide insights on volume, schema changes, and recent table updates while using machine learning to detect trends, outliers and drifts in actual data values. Together, these two techniques give you a full picture of your data quality.

Monitor complex data types

Gain an end-to-end visibility into your entire data pipeline regardless of the type, form, shape, frequency and volume of data in the pipeline. Telmai monitors data of all types - from data warehouses and analytic databases, to data lakes, semi-structured sources such as JSON, streaming data such as Kafka queues, pub-sub messages, and even data extracted from APIs.

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Trusted by

The Leader in Data Quality

Telmai has been named a High Performer in the G2 Data Quality Report. Checkout our 5 star reviews.

Integration

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Quick time to value

Telmai's out of the box data quality metrics, ML-based automation, and a low-code, no-code user interface provides you and your team engineering productivity and faster time to value.

70%

Faster detection and resolution of data quality issues by using ML

10X

Cost reduction vs. maintaining data quality rules over time

The Leader in Data Quality

Telmai has been named a "High Performer" in the G2 Data Quality Grid Report of Spring 2023.

Checkout our 5 star reviews.

Customer testimonials

“Our customers deserve the best possible data to make their business grow. Telmai's Data Observability enables us to show the quality of the data that we are delivering to our customers. With Telmai we are able to build data quality KPIs that reflect our data's freshness, completeness, and accuracy to keep and extend the trust that our customers have put in us.”

Harlow Ward

CTO, Clearbit

"When dealing with data sets comprising million of records, Telmai quickly becomes an incredibly useful tool, saving a lot of time and ultimately increasing data quality."
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Maria Grineva

SVP Engineering, Dun & Bradstreet

“We chose Telmai because of its flexibility and architecture. Telmai will remain our data observability platform as our data landscape changes. Telmai has lots of adapters in place to quickly integrate with various data sources across cloud providers. In addition, it supports real time API integration for automated decisions in complex workflows.”

Kishore Damodara

AVP of Cloud and Data Engineering, Merkle

"Data anomaly investigations that used to take hours now take minutes and give us more time to focus on the business needs."

Darius Kemeklis

EVP, Myers-Holum

“In searching for data quality and observability, we looked at a few solutions. They were limited to tracking just the data volumes, row counts, completeness, and uniqueness. That was not enough for us. We needed to look inside each attribute and each record to see if our data is drifting or its patterns are changing. Telmai was the only solution that empowered us to do so.”

Alejandra Cabrera

Data Product Manager, Clearbit

“To prepare data for product usage analysis we needed data quality metrics beyond monitoring operational data pipelines and job status checks. While we continue to monitor the quality of the pipeline, we chose Telmai to detect the quality of the data that moves through the pipeline.”