Announcing Data Observability for Firebolt
Overview Firebolt is the cloud data warehouse designed for next-gen analytics experiences. The platform’s ease of use of modern architecture with a sub-second performance at a terabyte-scale has enabled many companies to adopt it in the recent past. So when a months back, a channel partner company said: You got to build an integration with Firebolt! I […]
Firebolt is the cloud data warehouse designed for next-gen analytics experiences. The platform’s ease of use of modern architecture with a sub-second performance at a terabyte-scale has enabled many companies to adopt it in the recent past.
So when a months back, a channel partner company said: You got to build an integration with Firebolt! I almost felt like Firebolt was the coolest kid on the block with whom we had to make friends 🙂
After reading their white papers, the data geek in me felt even more strongly that we had to make friends with this cool kid, and Max couldn’t agree more.
So here we go. As a complete data pipeline Observability tool, Telmai now supports Firebolt along with many other DataWarehouse and datalake integrations like Snowflake, GCS, S3, AzureBlog, and BigQuery.
Telmai + Firebolt
As a low-code, no-code data observability product Telmai helps data teams proactively detect and investigate data issues within Firebolt before having a downstream impact.
Data teams struggle to detect data quality issues as soon as they occur — these issues are often first experienced by consumers of ML and Analytics and cause long and tedious investigation cycles. Telmai helps reduce the mean time to detect (MTTD) and mean time to resolve(MTTR) data quality issues.
Benefits for Firebolt users :
- Automatic monitoring on 40+ predefined data metrics like schema change, row count, completeness, uniqueness, patterns, distribution change, freshness, accuracy, etc
- Simple UI for expectations/rules – no more hand-coded rules using Great Expectation or DBT expectations.
- Support for structured & semi-structured data, i.e., nested and multi-valued attributes.
- Change data capture so users can independently monitor the entire dataset and changes alone.
- Support multiple formats like JSON, Parquet, CSV, and Avro
- Intuitive human-in-loop model for fine-tuning thresholds, policies, or writing expectations
- Spark processing to enable infinite scale without performance impact on Firebolt queries.
How does this work
How to get started ?
- Create a free Telmai account
- Within minutes you will get your account with onboarding instructions
- Connect to Firebolt data source
- You are all set. You automatically get monitored.
Firebolt users can now continuously monitor their data in Firebolt for Key Data quality metrics without impacting performance. This no-code integration ensures that the data inside Firebolt is always trustworthy for Analytics.
Feel free to get your trial account or schedule a demo @ www.telm.ai
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