Seven Category-Defining Features – from Time Travel Analysis to BYOC – Strengthen the Breadth, Depth, and Total Data Observability Time to Value
Big Data London – Telmai, the AI-driven data observability platform built for open architecture, today unveiled its latest release featuring seven category-defining features designed to simplify and accelerate data observability adoption for the enterprise.
With the growth of the data ecosystem over the past few years, 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 empowers 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.
“We are excited to bring Telmai’s most significant release to the market to date,” said Mona Rakibe, co-founder and CEO of Telmai. “We’ve drawn inspiration from our enterprise customers to build a product that redefines the future of data reliability.”
Telmai is launching and demonstrating its new release at the Big Data London conference at Stand #760. Mona Rakibe will also take the stage to present “Data Observability Use Cases: A Look Beyond Data Quality and Incidence Response” at noon at the Modern Data Stack Theater.
“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. 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.
Multi-Attribute Rules/Expectations
In addition to broad observability coverage across systems, Telmai’s strength lies in identifying attribute-level data issues in depth and at scale. 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.
Telmai Is Available In The Google Marketplace
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.
“As a data-driven enterprise, we’ve found Telmai to be an invaluable solution for us,” said Raghu Nadiger, Data and Analytics Leader at DataStax. “Telmai’s data observability platform empowers us to monitor and detect data quality issues in real-time, ensuring the integrity of our downstream systems and the accuracy of our decision-making analytics. We are looking forward to Telmai’s upcoming product release and its promising range of features, and we anticipate the positive impact it will have on our data operations.”
This product release builds on the company’s momentum in recent months, including:
- $5.5 million in seed funding in June 2023
- Three significant partnerships with Google Cloud, Snowflake, and Databricks
- Leader in the 2023 GigaOm Radar Report for Data Observability
- 5-star customer ratings in G2 Data Quality Grid report
To learn more about Telmai, please visit https://www.telm.ai/ or request a product demo.
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