Telmai, the AI-driven data observability platform built for open architecture, announced its latest release featuring seven category-defining features designed to simplify and accelerate data observability adoption for the enterprise.

“Through automation, ML and AI, and intuitive self-service capabilities, we are creating a future where the complexities of ensuring data quality in a heterogeneous environment will become a thing of the past,” said Max Lukichev, co-founder and CTO of Telmai.

Telmai’s new release is based on its core product pillars:

• End-to-end observability – from ingestion to consumption
• Deep and granular record-level data quality checks and anomaly detection
• Faster time to value

Time Travel Analysis: Telmai extends its time-to-value accelerators with retrospective analysis of historical data, enabling Telmai’s anomaly detection ML models to train instantly, eliminating the need for a long learning period for the system to observe the data’s behavior to build baseline thresholds. The time travel feature also helps develop and test rules and analyze their impact on past data, helping business and technical teams build preventative data quality metrics they can trust.

BYOC (Bring Your Own Cloud) Option For AWS, GCP And Azure: To enable enterprises that cannot move their data outside of their cloud account or even VPC due to privacy concerns or the volume of data itself, Telmai has built its private cloud offerings across all three major cloud providers. This release allows customers to deploy Telmai in their GCP, AWS, or Azure cloud accounts. With Telmai’s control planes fully managing the upgrades and scaling optimization, customers get all the benefits of public SaaS in their accounts.

End-to-End Observability For Heterogeneous Data Pipelines: Telmai’s platform is built for open architecture, enabling users to monitor complex heterogeneous data in SQL and NoSQL databases, files, and event streams.

Multi-Attribute Rules/Expectations: Telmai customers can now interactively define complex expressions over multiple attributes, set expectations, or monitor the outputs for anomalies or violations. Telmai’s Spark-based architecture enables processing hundreds of expressions over billions of records at a low cost instead of the costly processing of individual queries inside the database.

Customers can ensure faster procurement, quicker deployments, and greater control over costs by leveraging their GCP credits and consolidated billing to purchase Telmai directly from the Google Cloud Marketplace.