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Data is the lifeblood of organizations, but handling it is no easy task. Companies have to build data pipelines, maintain them and then bring everything together in a central data warehouse for analysis and data science.
Now, while there are plenty of data warehouses to work with, Snowflake continues to be the star performer. The company’s data cloud, according to the company, has more than 7,000 enterprise customers (and is still growing) and has seen particular demand for its ability to support various workloads, as well as its usage-based pay-as-you-go pricing model.
However, the thing with cloud data warehouses like Snowflake is that they all have different cost structures. Teams cannot easily compare which product is more affordable. Secondly, not all of these products offer real-time alerts or even optimization features to bring costs down. Teams have to manually scour resource usage reports from cloud providers to identify which services are driving up costs to make adjustments. This is time-consuming, error-prone and far from scalable.
Enter Chaos Genius for Snowflake optimization
To address these challenges, California-based Chaos Genius offers an automated dataops observability and optimization platform. The company recently raised $3.3 million in a seed round led by Elevation Capital with the participation of Y Combinator and multiple angels.
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Chaos Genius’s platform uses machine learning and artificial intelligence to analyze workloads – queries, databases and resource usage – in a data warehouse and provides enhanced metrics and cost monitoring capabilities. This allows teams to dive into credit consumption data, as well as detect anomalies, create smart alerts and automatically get recommendations to optimize performance and reduce costs.
For example, the platform can analyze query patterns (often on the scale of millions) to identify inefficient queries and make intelligent recommendations to cut them down, thereby improving performance.
“Given the economic downturn, companies like never before are pushing to make cutbacks, especially when it concerns imprudent expenses,” Preeti Shrimal, cofounder and CEO of Chaos Genius, said.
“With a unique set of features, like analyzing query patterns, finding unused data and [making] intelligent recommendations, our product makes a massive impact on how data teams use their warehouses,” she added.
The company says its product, which is currently available for Snowflake, can cut down spending on data cloud deployments by 10% to 30%. It is being used by some of the largest Snowflake customers with over $1 million in annual spending and workloads of up to 100 million queries per month. It will use the fresh funding to build on this offering and expand to other data platforms, including Databricks, Amazon Redshift and Google BigQuery.
Need for cost optimization
The funding for Chaos Genius comes as more and more enterprises face the need to cut down costs during the current economic climate. According to a McKinsey report, organizations have a savings potential of 15-35% in data spend through optimization of data sourcing, infra, governance and consumption. A recent Gartner survey also found that the primary complaint from buyers of cloud data management solutions is the lack of predictable costs and pricing transparency.
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