Efficiency Recommendations for Infrastructure, Configuration, and Code
Unravel Data, the first AI-enabled data observability and FinOps platform built to address the speed and scale of modern data platforms, announced the release of Unravel for Snowflake. By employing AI that is purpose-built for managing the Snowflake technology stack, cloud data cost management is put into the hands of Snowflake customers by providing them with granular insights into specific cost drivers, as well as AI-driven cost and performance recommendations for optimizing SQL queries and data applications. Unravel for Snowflake is the latest data observability and FinOps product from Unravel Data, adding to the portfolio of purpose-built AI solutions that include Databricks, EMR, Cloudera, and BigQuery.
Today, companies are looking to AI to provide them with a competitive advantage, which is driving an exponential increase in data usage and workloads, use cases, pipelines, and generative AI/LLM models. In turn, companies are facing even greater problems with broken pipelines and inefficient data processing, slowing time-to-business value and adding to exploding cloud data bills. Unfortunately, most companies lack visibility into their data cloud spend or ways to optimize data pipelines/workloads to lower spend, speed innovation, and mitigate problems.
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Unravel’s purpose-built AI for Snowflake delivers insights based on Unravel’s deep observability at granular levels to deliver AI-driven cost optimization recommendations for warehouses and SQL that include: warehouse provisioning, run-time, auto-scaling efficiencies, and more. With Unravel, Snowflake users can see real-time cost usage by query, user, department, and warehouse, and set customized dashboards, alerts, and guardrails to enable accurate, granular cost allocation, trend visualization, and forecasting.
“As companies double down on AI efforts, we can expect to see more wasted data cloud spend. Costs are incurred not only with infrastructure but with consumption, as most AI pipelines are created in ways that drive up unnecessary cloud data costs,” said Kunal Agarwal, CEO and co-founder, Unravel Data. “Data engineering and architecture teams need an early warning system to alert them to out-of-control spending, an automated way to pinpoint the source of performance issues and cost overruns, and AI-driven recommendations to optimize code in ways that mitigate unnecessary costs, speed new development, and eliminate data pipeline problems.”
At the core of Unravel Data’s platform is its AI-powered Insights Engine, which has been trained to understand all the intricacies and complexities of modern data platforms and the supporting infrastructure. The Insights Engine has been built to ingest and interpret the continuous millions of ongoing data streams to provide real-time insights into application and system performance, and recommendations to optimize costs, including right-sizing instances and applying code recommendations for performance and financial efficiencies. When combined with Unravel’s automated guardrails and alerts, the Insights Engine enables organizations to achieve data cloud efficiency at scale.
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“Our latest research shows that the adopters of cloud data warehouses struggle with data pipeline complexity, lack of staff/expertise, and an inability to predict workloads,” says Kevin Petrie, VP of Research at The Eckerson Group. “FinOps platforms for cloud data analytics, such as Unravel, provide the granular visibility that stakeholders need to predict and monitor spending. This makes it easier for companies to optimize workloads, change user behavior, and get a handle on governing cloud costs.”
Unravel for Snowflake includes additional features such as:
- Visibility for cost allocation with chargeback/showback reports
- Warehouse-level insights and recommendations relating to warehouse consolidation and underutilization efficiencies
- Compute + storage unit cost reporting with average cost per project, query, and user over time
- SQL-related insights and recommendations for optimizing queries by filters, joins, projection inefficiencies, anti-patterns, and more to improve query efficiency and increase capacity so that more users and requests can be served at the same spend
- Dashboard customization for at-a-glance summaries and drill-down insights for spend, performance, and unit costs
- Alert customization using OpenSearch-based alerts beyond Snowflake’s out-of-the-box alerts to enable early warnings of resource usage spikes before they hit the cloud bill.
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