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How Snowflake's Snowpark Manages Data

Having just gone public in September 2020, it's already been a turbulent time for the team at Snowflake. Not only did they raise nearly $4 billion during their IPO, making it the largest software IPO in history to date, but they even secured backing from the likes of Warren Buffet and Salesforce. With such a strong start, it begs the question: where is all of this energy being focused?

For now, at least, the brunt of Snowflake's efforts are being invested into Snowpark; a new development tool that facilitates custom data management workflows on their new data warehouse and the Snowflake Data Cloud.

Benoit Dageville, co-founder and president of products with Snowflake, spoke about their recent service enhancements in a recent press release, stating: "Data is central to how we run our lives, businesses, and institutions. Many of today’s organizations still struggle to mobilize all of their data in service of their enterprise. The Data Cloud contains a massive amount of data from Snowflake customers and commercial data providers, creating a powerful global data network effect for mobilizing data to drive innovation and create new revenue streams."

What is Snowpark?

Designed specifically for data engineers, scientists and developers, Snowpark lets IT professionals write code – in their preferred coding language – to streamline and optimize data management on their end. As such, it accommodates a number of popular data management strategies, including:

Data Preparation: In most cases, data needs to be conditioned prior to processing and analysis. If this weren't the case, you'd be stuck with troves of useless and redundant information that clog your system and make it difficult to find the most important data.

ETL / ELT: Termed as Extract, Transform, Load (ETL) or Extract, Load, Transform (ETL), these are two separate data management practices that are critical when working with large-scale data warehouses. While ETL uses an independent staging area for data prior to being transferred to the warehouse, ELT skips this step entirely. Instead, it uses the data warehouse itself for all transformational and processing needs.

Feature Engineering: A form of advanced machine learning, feature engineering uses highly specialized and targeted datasets to improve the performance of their own machine learning capabilities. It basically makes it possible for the machine to teach itself over the course of time, and becomes easier and easier as more time passes by.

These three key features, alongside the near-zero maintenance benefits, make it easier for data scientists and engineers to ply their trade in the most effective way possible. In turn, this gives businesses the ability to focus on their bottom line and the data that is most beneficial to their interests.

Christian Kleinerman, Snowflake's senior vice president of product, stated: "Snowflake’s platform enables organizations to leverage the power of the Data Cloud regardless of which supported public cloud they use, or where an organization’s data or users are located. The new features announced today are another example of Snowflake’s commitment to delivering the technology customers need to fully mobilize their data and achieve meaningful business value."


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