Systems | Development | Analytics | API | Testing

Snowpark for Python: Large-Scale Feature Engineering, Machine Learning Model Training, and More

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.

Winning the race: data as the ultimate competitive edge, with Susie Wolff

Susie Wolff, former Formula 1 driver and founder of Dare to be Different, knows a lot about using data to thrive under pressure. In racing, data is the difference between being a champion and falling behind. How can your business become data driven the way Formula 1 has? How can you get the insights you need to thrive — not tomorrow, not next week, but right now? Industry analyst and digital transformation expert Maribel Lopez interviews Wolff, extracting takeaways that every business can apply.

Using Snowpark For Python And XGBoost To Run 200 Forecasts In 10 Minutes

Snowpark for Python, now generally available, empowers the growing Python community of data scientists, data engineers, and developers to build secure and scalable data pipelines and machine learning (ML) workflows directly within Snowflake—taking advantage of Snowflake’s performance, elasticity, and security benefits, which are critical for production workloads. Using user-defined table functions (UDTFs) and the new Snowpark-optimized warehouse with higher memory, users can run large-scale model training workloads using popular open-source libraries available through Anaconda integration.

Become a Financial Storyteller

Financial statements tell an important story, but they rarely tell the entire story. It often requires a sharp eye and a healthy measure of experience to elicit meaningful information from the numbers. Even people with keen financial acumen will have questions, and they can easily overlook important realities that lay buried somewhere within the details. For those with less experience reading financial reports, this task is far more difficult.

Are These the 6 Best Reverse ETL Vendors?

The amount of big data that enterprises churn out is simply staggering. All this information is worthless unless organizations unlock its true value for analytics. This is where ETL proves useful. Traditional ETL (extract, transform, and load) remains the most popular method for moving data from point A to point Z. It takes disparate data sets from multiple sources, transforming that data to the correct format and loading it into a final destination like a data warehouse.

Episode 3 & 4 | Data Destination & Data Governance | Data Journey

What are data destinations? In a very abstract sense, data destination is another input along the series of process elements in a data pipeline. However, when calling out an element as the destination, it is really seen as the final destination such as a database, data lake or data warehouse. And yet, any element within the data pipeline has aspects of a final destination (and scaling challenges).