Since Snowflake’s inception, we’ve had the needs of our customers as our North Star, with a clear focus on security and governance of data. Early on we also committed to continuous innovations to improve performance and reduce latencies, and by virtue of our business model continuously improve the economics for our customers.

As data science and machine learning adoption has grown over the last few years, Python is catching up to SQL in popularity within the world of data processing. SQL and Python are both powerful on their own, but their value in modern analytics is highest when they work together.

Snowflake is committed to helping developers focus on building their apps and businesses rather than on infrastructure management. At this year’s Snowday, Snowflake announced a series of advancements that empower developers to do more with their data, enhancing productivity and unlocking new ways to develop applications, pipelines, and machine learning (ML) models with Snowflake’s unified data platform.

There’s a lot of talk in the market these days about data clean rooms, along with some confusion about what exactly a data clean room is and how it differs from data sharing methods. In this blog post, I’d like to shed some light on this topic.

Every organization wants to identify the right sales leads at the right time to optimize conversions. Lead scoring is a popular method for ranking prospects through an assessment of perceived value and sales-readiness. Scores are used to determine the order in which high-value leads are contacted, thus ensuring the best use of a salesperson’s time. Of course, lead scoring is only as good as the information supplied.

Are you efficiently unifying, modeling, analyzing, and activating all the data you need to drive impactful marketing campaigns and customer experiences? For years, marketing teams have struggled to operate from a single view of the customer and their business, essential to powering personalized experiences and measuring impact on key KPIs such as sales, growth, and profitability. Today, only half of all marketers have a unified view of the customer.

The catalyst of innovation and transformation is data. The companies that recognize the power of data and wield it to drive business transformation are seeing positive impacts on their business outcomes, as indicated in our report, How to Win in the Data Economy. We surveyed 1,000 senior business and technology executives to gauge the impact the data industry is having on their businesses, and to what extent companies are embracing the opportunity to become data leaders.

Snowflake removes the need to perform maintenance tasks on your data platform and provides you with the freedom to choose your data model methodology for the cloud. When attempting to keep the cost of data processing low, both data volume and velocity can make things challenging.

What’s harder than finding the right data architecture? Finding the right dedicated partner. Adverity gets both with Snowflake. Learn how the two organizations are moving into new markets and supplying even more reliable marketing data to Adverity customers. When a fast-growing SaaS business looks to expand its client base, it normally encounters two major challenges: In many cases, an external data solution provider can only help solve the scalability challenge.

In part 1 of this blog series, we looked at how Snowflake supports the GEOGRAPHY geospatial data type, which works with the earth as an ellipsoid, measuring distances over a curvature and plotting objects using the latest World Geodetic System, WGS84.