Today, Snowflake announced an investment in Metaplanea Boston-based startup that helps firms discover and resolve data quality issues with an end-to-end AI-powered platform.
While the quantity invested just isn’t yet disclosed, Snowflake says the support will end in closer integration between Metaplane's data observability offering and the Snowflake data cloud, allowing users of the latter to access the knowledge their downstream projects, including AI applications support, can keep a better eye on.
Metaplane, which takes on heavily funded players like Monte Carlo and Acceldata, can even launch a native app for data platform Snowflake confirmed. Notably, this move represents Snowflake's fifth investment this 12 months and second within the observability space. Back in March, the corporate supported Observe, which analyzes telemetry data from enterprise applications and provides users with relevant context to quickly discover and remediate incidents.
Today, data is the driving force of contemporary business applications, including RAG-based AI chatbots, but most firms struggle to maintain data quality so as. There is just so much information spread across siled systems, databases and applications that it’s difficult for teams to maintain track of all the things to discover problems and anomalies. Because of complex pipelines, teams sometimes should take care of lots of and even 1000’s of sources.
Founded by MIT graduate Kevin Hu, former HubSpot engineer Peter Casinelli, and former Appcues developer Guru Mahendran, Metaplane solves this problem by applying AI at different levels of the info stack, from ingestion to consumption.
The platform integrates with tools across the info stack – similar to Fivetran, Snowflake BigQuery, dbt, Airflow and Tableau – and uses a machine learning (ML) model to coach the complete data profile, covering historical metadata, lineage and logs. Once training is complete, it routinely begins flagging data anomalies (even schema changes) in keeping with user-setup monitors.
Hu previously told VentureBeat that these monitors might be arrange in only quarter-hour to keep watch over data quality metrics similar to freshness, row count, uniqueness and nullity. In the meantime, notifications shall be routed on to affected data teams via their preferred channels.
With Snowflake's investment, Metaplane will now deepen its integration with the Snowflake data cloud and canopy much more telemetry and metadata on the platform. According to Snowflake, this can include complete data pipelines in addition to app features similar to Snowpark, Snowpark Container Services, Snowflake Native Apps and Streamlit.
The work will ultimately enable Snowflake customers to closely monitor the standard of their data assets moving through various stages of the pipeline to power downstream applications. If an error occurs with applications and data, Metaplane notifies users of the issue at hand, in addition to the foundation reason behind the issue and essentially the most appropriate solution.
While it stays to be seen exactly when this deeper integration will go live, Snowflake says the startup can even launch a native app of its platform in the info cloud as a part of its partnership with Metaplane. This allows users to deploy and manage Metaplane directly inside their Snowflake instance – without having to attach Snowflake as is the case with other data tools.
“This opens the door to much more immersive experiences and allows customers to take full advantage of Metaplane without having to maneuver or copy their data outside of the secure, managed environment of their Snowflake account,” said Ashwin Kamath and Harsha Kapre, each Management at Snowflake is chargeable for the product, wrote in a joint blog post.
Snowflake goals to overcome the age of AI
Since Sridhar Ramaswamy took over as CEO, Snowflake has taken an aggressive approach to deploy AI on the intersection of knowledge and higher compete with Databricks, which has been focused on AI since its inception.
Last 12 months, at its Snowday event, the corporate introduced Cortex, a completely managed service for constructing Gen AI apps using information stored in the info cloud. In the months that followed, the corporate hired several open source AI providers, including Mistral and Reka, to supply their models on Cortex and help teams develop apps for various use cases. The company even trained Arctic, its own Large Language Model (LLM) optimized for complex enterprise workloads like SQL generation, code generation, and command tracing, and launched a Copilot experience to assist users understand and explore their data facilitate.
Prior to Metaplane, the corporate invested in 4 other firms to strengthen its data and AI efforts. These were Coda, Coalesce, Observe and Landing AI.
