A tale of three data platforms
The convergent evolution of BigQuery, Snowflake and Databricks demonstrates a broader tendency in the development of cloud platforms.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Automated fraud detection with Fivetran and BigQuery
Exploring fraud detection with automated data integration in a cloud data warehouse: part one of two.
Choosing the Right API: REST vs. RESTful for Integration
Choosing between REST API vs RESTful API is pivotal for efficient and scalable business solutions in data integration. Application Programming Interfaces (APIs) are critical in data integration, enabling diverse systems to communicate and data exchange seamlessly. In this landscape, REST (Representational State Transfer) APIs have emerged as a standard, known for their simplicity and effectiveness in handling network requests.
How Fan 360 redefines the sports landscape
Your opportunity to create a loyal fanbase is locked behind disparate data silos. It’s time to call an audible.
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.
How generative AI will change the nature of work
Generative AI is a powerful tool for rapidly creating and iterating on content of all kinds.
Top REST API Best Practices for Data Integration
In an era where data integration forms the backbone of modern digital ecosystems, effectively mastering REST APIs is crucial for any data engineer to ensure efficiency and reliable data transfers. Our 5 key takeaways from the article on REST API best practices for data integration are.
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.
Sorenson plugs and plays data integration at scale
Learn how the world's leading language services provider leverages Fivetran to offer its customers real-time, data-dependent services.
Why you should focus on data centralization in 2024
As data leaders embark on their data strategy in the new year, data centralization is integral to demonstrate value.