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.

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?

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

Download a sample report that includes insights into the health of a Snowflake data estate.

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.

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.

Software testing can feel like you're walking a tightrope—striving to uphold impeccable quality standards while the clock ticks towards your next release. It’s a daunting challenge to make sure every test is planned and executed with speed, all while hoping there are no blind spots in your process. Enter test management. It’s a great way to ensure your tests remain organized, efficient, and consistent. And of course, so you can report on this hard work to leadership.

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.

A data pipeline is a series of processes that move raw data from one or more sources to one or more destinations, often transforming and processing the data along the way. Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems.

Data migration refers to the process of moving data from one location to another. In the context of Oracle Cloud, data is transferred from on-premises systems or other cloud platforms to Oracle’s cloud infrastructure. Whether you’re migrating databases, applications, or entire systems, a complete approach is critical to a successful migration.