In today’s regulatory environment, organizations are compelled to adopt stringent security measures to mitigate the risk of PII (Personally Identifiable Information) data breaches in their production environments. However, the pressure to provision more test data in non-production environments, often sourced from production data, has introduced new vulnerabilities.

Idempotent operations produce the same result even when the operation is repeated many times. The result of the 2nd, 3rd, and 1,000th repeat of the operation will return exactly the same result as the 1st time. In this post, we will demystify the concept of idempotency—a fundamental property that ensures consistency, predictability, and reliability in APIs and distributed systems.

Manufacturing has been one of the most prominent industries worldwide since the Industrial Revolution. Since then, from producing textiles to computer chips, manufacturers have always looked for ways to innovate. In the age of AI and APIs (or application programming interfaces), tech companies are looking for technological breakthroughs to gain an edge over the competition.

History is full of dynamic duos like peanut butter and jelly or Batman and Robin—but is it time to add APIs and ERPs to the mix? According to a 2019 study by Cloud Elements, APIs and ERPs are essential to modern business strategy. 55 percent of respondents believe that API integrations are “critical” to their operations. What’s more, the survey’s participants say that ERPs are one of their top three business use cases for API integrations.

Internal app sharing is a crucial step in ensuring the quality and functionality of applications before their public release. With the upcoming discontinuation of Microsoft's App Center, developers are looking for robust alternatives that streamline the distribution process and address common challenges faced during internal testing phases. This post explores the intricacies of internal app sharing, highlighting common obstacles and reviewing platforms designed to simplify the process.

Cloudera’s data-in-motion architecture is a comprehensive set of scalable, modular, re-composable capabilities that help organizations deliver smart automation and real-time data products with maximum efficiency while remaining agile to meet changing business needs. In this blog, we will examine the “why” behind streaming data and review some high-level guidelines for how organizations should build their data-in-motion architecture of the future.

Modern data platforms enable enterprises to extract valuable business insights from data, sourced from various origins. Data engineers, data scientists, and other data practitioners utilize both data streaming and batch processing frameworks as a means to provide these insights. While batch processes work on historical data, stream processing extracts insights in real time, enabling businesses to react faster with respect to changing events.

I’m happy to share that Snowflake has signed the Cybersecurity and Infrastructure Security Agency (CISA) Secure By Design Pledge as we champion the advancement of industry standards for security in technology design. The CISA pledge to foster tech-ecosystem security deeply aligns with Snowflake’s own product design ethos, where security is built in from the start.