In today's livestream, Saurin Shah and Isaac Kunen talk about unstructured data support in Snowpark. Hosted by Felipe Hoffa and Dash Desai.

Securonix brings exciting new capabilities to the field of threat detection and response thanks to its integration with Snowflake. Together, the two companies provide a split-architecture solution that solves the problem of data silos and enables organizations to make better, more timely decisions about potential threats to their organization. It’s a next-gen SIEM solution already finding widespread use in such organizations as healthcare institutions, airlines, and telecommunication companies.

In today’s world, retail customers expect things fast. They want their products on time and they want their orders not to be canceled. And when things go wrong, they want answers. To deliver that experience, retailers need to be able to understand at a granular level how their customers’ orders are moving through their supply chains. In this episode of “Powered by Snowflake,” Daniel Myers chats with Elevate Co-founder and CTO James Sutton about his company’s recently introduced retail operations platform that provides the analytics retailers need to evaluate and manage supply chain performance.

What if there was a way to easily manage, process, and analyze any data type in a single platform? Snowflake is here to help. Simplify your architecture with a single platform for all data types and workloads, unlocking new use cases for your data. With Snowpark, your data scientists and engineers can securely build scalable, optimized pipelines, and quickly and efficiently execute machine learning workflows while working in Python, Java, or Scala.

By taking a data-driven approach to cloud security, Lacework allows clients to expand their use of the cloud safely in a way that doesn't require them to grow their security team linearly. The key, says Ryan Henrich, Director of Technology Alliances at Lacework, is to treat security as a data problem.

How do you implement a decay function in SQL? You can use window functions, which scale better than joins, or better yet, you can try what Felipe Hoffa did: use tabular UDFs. In this video, Felipe shows you how you can use a tabular UDF to write custom code that can analyze a table row by row while preserving state. Felipe wrote a table UDF in JavaScript that uses a low amount of memory to keep track of the decaying values. He was able to run it in 36 seconds, instead of the 46 seconds that the SQL with windows solution took; and then he optimized the JavaScript even further and ran it in just 9 seconds.

Snowsight is the single best place to analyze your data, share insights, and administer your Snowflake account.

The Observability Platform from Observe is a tool that helps engineers and DevOps teams quickly analyze the performance of and troubleshoot problems with an organization’s distributed applications. Its power comes from its ability to ingest any kind of telemetry and machine data–trace data, log data, metric data, billing data, and so on–into a single source using Snowflake, and then map the relationships between data sets. These relationships can be displayed graphically as an interactive visualization, making it easy for end users to trace connections between datasets in order to diagnose the cause of error notifications or gain insight into an application's performance.

Unstructured data stores a tremendous amount of information, but it is not easy to search, analyze, or query, especially on the fly— presenting more challenges to data architectures that are already complex. In this session, you will learn about Snowflake’s new support for unstructured data and how unstructured data can be used to analyze proprietary replay files for the Dota 2 video game. Dota 2 has a substantial esports presence and its developer hosts the world's largest endowed esports tournament.

Did you know that Google Analytics 4 now exports event data at the individual user level for free? In this episode of SnowFlake Bytes, Felipe Hoffa, Snowflake’s Data Cloud Advocate, shows you how to move this GA4 data into Snowflake and translate the sample queries into Snowflake SQL. His favorite part of the process? Creating a UDF that transforms the original schema nested data into a variant object that is easier to query.