This is a guest community post from Haejoon Lee, a software engineer at Mobigen in South Korea and a Koalas contributor.

Try this notebook to reproduce the steps outlined below

We recently hosted a live webinar — How Starbucks Forecasts Demand at Scale with Facebook Prophet and Databricks — During this webinar we learnt why Demand Forecasting is critical to Retail/ CPG firms and how it enables 22 other use cases. Brendan O’Shaughnessy, Data Science Manager at Starbucks walked us through how Starbucks does demand forecasting at scale. We also did a step by step demo on how to perform fine-grained demand forecasts on a day/store/SKU level with Databricks and Facebook’s Prophet

With technological advancements in imaging and the availability of new efficient computational tools, digital pathology has taken center stage in both research and diagnostic settings. Whole Slide Imaging (WSI) has been at the center of this transformation, enabling us to rapidly digitize pathology slides into high resolution images. By making slides instantly shareable and analyzable, WSI has already improved reproducibility and enabled enhanced education and remote pathology services.

We are excited to announce the release of Delta Lake 0.5.0, which introduces Presto/Athena support and improved concurrency.

We recently hosted a live webinar — Geospatial Analytics and AI in Public Sector — during which we covered top geospatial analysis use cases in the Public Sector along with live demos showcasing how to build scalable analytics and machine learning pipelines on geospatial data at sale.

Advances in time series forecasting are enabling retailers to generate more reliable demand forecasts. The challenge now is to produce these forecasts in a timely manner and at a level of granularity that allows the business to make precise adjustments to product inventories. Leveraging Apache Spark™ and Facebook Prophet, more and more enterprises facing these challenges are finding they can overcome the scalability and accuracy limits of past solutions.

We started Databricks in 2013 in a tiny little office in Berkeley with the belief that data has the potential to solve the world’s toughest problems. We entered 2020 as a global organization with over 1000 employees and a customer base spanning from two-person startups to Fortune 10s.

Machine learning models can seem like magical savants. They can distinguish hot dogs from not-hot-dogs, but that’s long since an easy trick. My aunt’s parrot can do that too. But machine-learned models power voice-activated assistants that effortlessly understand noisy human speech, and cars that drive themselves more or less safely. It’s no wonder we assume these are at some level artificially ‘intelligent’.

The evolution and convergence of technology has fueled a vibrant marketplace for timely and accurate geospatial data. Every day billions of handheld and IoT devices along with thousands of airborne and satellite remote sensing platforms generate hundreds of exabytes of location-aware data. This boom of geospatial big data combined with advancements in machine learning is enabling organizations across industry to build new products and capabilities.