From rice genomes to historical hurricane data, Google Cloud Public Datasets offer a world of exploration and insight. The more than 20 PB across 200+ datasets in our Public Dataset Program helps you explore big data and data analytics without a lot of cost, setup, or overhead. You can explore up to 1 TB per month at no cost, and you don’t even need a billing account to start using BigQuery sandbox.
When it comes to machine learning (ML) in the enterprise, there are many misconceptions about what it actually takes to effectively employ machine learning models and scale AI use cases. When many businesses start their journey into ML and AI, it’s common to place a lot of energy and focus on the coding and data science algorithms themselves.
With any transformation in industry or marketplace, there are leaders and losers. The winners know the fundamental pillars that are hidden to some and evident to others that drive and enable success.
Apache Spark is a fast and general-purpose engine for large-scale data processing. It’s most widely used to replace MapReduce for fast processing of data stored in Hadoop. Designed specifically for data science, Spark has evolved to support more use cases, including real-time stream event processing. Spark is also widely used in AI and machine learning applications.
One of the big trends we’ve seen this year is organizations going direct to consumer. Manufacturers who sold through retail outlets are moving online, and as a result a huge amount of digital transformation is occurring. A customer of ours has done exactly that. Kyowa is a Japanese cosmetics and health food company and they’ve moved from retail to online and digital, and Yellowfin has been a significant part of that journey. In particular, they’ve used Signals.
Snowflake is delighted to share the findings of a new Forrester Consulting Total Economic Impact™ (TEI) study that examines the potential return on investment for organizations that procure Snowflake through Amazon Web Services (AWS) Marketplace and then use Snowflake as a core part of your application’s architecture. We commissioned the study in partnership with AWS.
This is the 3rd blog in our series on Snowflake Resource Optimization. In parts 1 and 2 of this blog series, we showed you how Snowflake’s unique architecture allows for a virtually unlimited number of compute resources to be accessed near-instantaneously. We also provided best practices for administering these compute resources to optimize performance and reduce credit consumption.
So, you’ve made a cool People Analytics App, filled it with data from your HCM, Finance, Talent Review System, and Recognition System (I could go on). You’ve also governed access to it to specific people and can use it to tell compelling stories.
So, you’ve made a cool People Analytics App, filled it with data from your HCM, Finance, Talent Review System, and Recognition System (I could go on). You’ve also governed access to it to specific people and can use it to tell compelling stories.
