Snowflake and Anodot announced a strategic partnership that brings predictive analytics to Snowflake’s enterprise data warehouse. With this integration, customers can discover anomalies in data streams and raise alerts in real-time.
Snowflake Computing, a San Mateo-based startup, is one of the first few companies to realize the potential of building a native, cloud-based data warehouse. Founded in 2012, Snowflake exploited the capabilities of the cloud to design a SQL data warehouse from the ground up. Snowflake’s data platform turned all the knobs available in the cloud to extract the best performance. Within a short span, it started to pose a threat to traditional data warehousing companies.
Snowflake has emerged as a cloud-native business intelligence platform. It is designed as a highly-scalable, low ops, interoperable and SQL-compliant data warehouse. Snowflake is tightly integrated with AWS and Azure offering choice to enterprise customers.
Based in Israel, Anodot was founded in 2014 by David Drai, Ira Cohen, and Shay Lang. The company is an early mover in bringing ML to log management. Anodot claims that it analyzed over 5.2 billion data points per day within six months of the launch. Delivered as a SaaS platform, Anodot can ingest data from a variety of sources including clickstreams, sensors, CRM, server logs and application logs.
Anodot is one of the first AIOps platforms that could find outliers and anomalies in data streams. Unlike other platforms, Anodot is not strongly tied up to IT or OT datasets. Its secret sauce lies in the way it can learn from the inbound data streams in real-time to find anomalies. Customers from the retail vertical use Anodot for sales forecast and fraud detection. For the financial industry, Anodot can help tackle customer churn, detect potential security issues and even the cause of declined transaction rate.
Snowflake and Anodot partnership brings the best of business intelligence and predictive analytics to customers. Anodot can learn from the massive amount of data stored in Snowflake to find outliers and anomalies in real-time. It can notify users when it detects anomalies in Snowflake data, via email, push notification, Slack, PagerDuty or even Webhook.
This partnership aligns with the recent trend of integrated predictive analytics and data warehouse. Google is adding native machine learning capabilities to BigQuery enabling customers to perform predictive analytics within the same environment. Microsoft is moving towards native ML support for Microsoft SQL Server, SQL Data Warehouse and Azure Data Lake.
The combination of Snowflake and Anodot is a viable alternative to proprietary data warehouses in the public cloud. Customers would benefit from the portability and choice offered by these two platforms.