Telmai Update Simplifies, Accelerates Enterprise Adoption of Data Observability
Seven features -- from time travel analysis to BYOC -- strengthen the breadth, depth, and total data observability time to value.
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Telmai, an AI-driven data observability platform built for open architecture, has released its latest version featuring seven features designed to simplify and accelerate data observability adoption for the enterprise.
With the growth of the data ecosystem, enterprises are seeing an accelerated need for continuous, reliable data flowing through their pipelines. High-quality, consistent, and reliable data is the foundation for AI/ML, including generative AI and analytics-based data products.
Telmai’s release allows data engineers, architects, and product owners to harness powerful time travel features, build multi-attribute data contracts, conduct in-depth root cause analysis of data failures, and gain greater control over data privacy and residency via its new private cloud offerings.
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 more quickly, 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 preventive 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 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.
End-to-End Observability for Heterogeneous Data Pipelines
Telmai's platform is built for open architectures, enabling users to monitor complex heterogeneous data in SQL and NoSQL databases, files, and event streams. Telmai has expanded its capabilities to include:
- Metadata monitoring: Telmai customers now have the flexibility to leverage lightweight and cost-efficient metadata-only monitoring for large amounts of less-important tables while preserving Telmai's capabilities of deep record value anomaly detection for critical tables. Users control monitoring data and metadata or metadata only at the table level.
- Cross-system data lineage: Users can capture and visualize cross-system lineage to monitor inconsistencies in the data across the pipeline. Automatic data consistency checks across volume, uniqueness, and completeness help enterprises detect data loss between pipeline stages and perform root cause analysis based on metric drifts.
- Data binning: An extension of Telmai’s circuit-breaker capabilities, users can now automate splitting good data from bad in the flow and use the outcome of observability. Good data can continue flowing through the pipeline and anomalous data can be stored for further analysis or remediation, enabling users to optimize the cost of transferring, storing, and processing invalid data.
Multiattribute 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.
To learn more about Telmai, please visit https://www.telm.ai/.