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Fivetran

Episode 1: Why everything doesn't need to be generative AI | Rocket Software

Generative AI has everyone talking, but has that buzz overshadowed the potential of predictive AI? We talked with Parag Shah, Senior Director of Data and Analytics at Rocket Software, to explore the hype and hope around both generative and predictive AI.

Sorenson Plugs & Plays Data Integration at Scale with Fivetran

Sorenson Communications is a leading provider of captioning and interpretation services for the hard-of-hearing and deaf, with the mission to make communication accessible and clear regardless of signed or spoken language. Automated, real-time translation of all kinds depends heavily on natural language processing and the data used to train it.

Automate Building ML Experiments with Databricks, AutoML, and Fivetran: Predicting Wine Quality

Learn how Fivetran’s automated data movement platform allows you to quickly set up a relational database connector to the Databricks Lakehouse to move a wine quality dataset over to the lakehouse and ensure that it’s ML-ready. Then you’ll see how to use Databricks and AutoML to run classification experiments on the dataset to generate models for wine quality predictions based on a variety of parameters, including citric acid, ph, residual sugar, and sulphates. An extra bonus is that you don’t have to be a data engineer, ML engineer, or a wine expert to deliver quick value with this tech stack and approach.

Accelerate Building Data Apps with Snowflake, Streamlit, and Fivetran: Workday Talent Management

Learn how Fivetran accelerates and automates data movement for Workday HCM data to the Snowflake Data Cloud. Using Fivetran’s fully automated and fully managed data movement service, you can achieve self-service data integration for all Snowflake data workloads quickly and securely and always be 100% automated. Kelly Kohlleffel steps you through creating a Workday to Snowflake connector with Fivetran for both the initial sync and ongoing incremental change data capture while creating a data app-ready dataset in Snowflake that is immediately useable, trusted, organized, and understandable by Streamlit.