Systems | Development | Analytics | API | Testing

As generative AI grows in popularity and enterprises scramble to embrace new technology in a scalable, compliant way, it can be difficult to know the right path forward. Your enterprise has already invested in automating processes to free up resources and improve organizational efficiency, but is it enough? This is where intelligent process automation comes into play.

This guest blog highlights how our strategic partnership with General Catalyst has fueled Fivetran's growth, showcasing our journey towards scaling innovation and efficiency.

You open your Uber app and book a ride. Upon successful booking, a map pops up and shows you the whereabouts of your driver. While in the Uber, you open Zillow, a real estate app, to check out the apartment you want to rent that is located a few kilometers away from your location. The weather app, knowing your location, then sends you a notification to expect rain tomorrow. All of these are examples of the power of geolocation-based app features.

To execute locally, remote, or on-cloud? That is the question. But test execution is more than just “hitting Enter and seeing if it passed or not.” If you are doing it manually, test execution is about performing every single test step as outlined in the test case. For automation testing, test execution takes on another layer of complexity: In which environment should you run the test? Which test cases should you prioritize for execution?

A solid understanding of internal technical and business terms is essential to manage and use data. Business glossaries are pivotal in this aspect, facilitating improved understanding and collaboration among teams. A business glossary breaks down complex terms into easy-to-understand definitions, ensuring that everyone in the organization, from the newest recruit to the CEO, is on the same page regarding business language.

When a data breach occurs, your response is critical. What do you do first? Do you have a plan for communicating with business units, regulators and other concerned parties? The integrity and security of data infrastructure stand as paramount concerns for business leaders across all sectors. As technology evolves and threats become more sophisticated, the pursuit of an unbreakable data infrastructure remains an ongoing challenge.

In the last couple of years, AI has launched itself to the forefront of technology initiatives across industries. In fact, Gartner predicts the AI software market will grow from $124 billion in 2022 to $297 billion in 2027. As a data platform company, Cloudera has two very clear priorities. First, we need to help customers get AI models based on trusted data into production faster than ever.

Learn how to use BigQuery’s new CDC capability in Dataflow and its at-least-once streaming mode to simplify your CDC pipeline and reduce costs.

For enterprises, commercial use of AI is still in its early stages, and it’s a case of risk and reward, weighing up both and investigating the best way forward. Of course, there’s much to gain from the use of AI. Already, companies are providing better customer service, parsing complex information through natural language inputs, and generally making workflows faster.