One of the major challenges in detecting phishing is the limitations at the Internet Service Provider (ISP) level. Traditional tools often lack the visibility and ability to recognize phishing sites in real time, as they mostly rely on network-level information. Phishing schemes frequently use complex, changing tactics, like rotating domains or imitating legitimate sites, which can go unnoticed without a detailed analysis of the site’s content.

Snowflake leaders offer insight on AI, open source and cybersecurity development — and the fundamental leadership skills required — in the years ahead. As we come to the end of a calendar year, it’s natural to contemplate what the new year will hold for us. It’s an understatement to say that the future is very hard to predict, but it’s possible to both prepare for the likeliest outcomes and stay ready to adapt to the unexpected.

Efficient handling of asynchronous data streams is an important tool of modern application development. Kotlin Flows, part of the Kotlin Coroutines library, provide a flexible and elegant solution for working with such data streams. Kotlin Flows are part of Kotlin Coroutines – unlike traditional callbacks or RxJava handling, which can be clunkier and may not directly integrate with your existing code structure easily.

$190 trillion+ market catapults Testlio above 100 payment testing engagements.

Platform engineering is a software development approach that combines DevOps principles to elevate the developer experience. It focuses on designing and constructing toolchains and workflows that promote self-service capabilities for software engineering teams. By addressing security, compliance, and operational efficiency, platform engineering enables organizations to build robust internal systems tailored to the needs of both developers and operations teams.

For organizations looking to accelerate their Azure adoption while maintaining control, security and reliability, Fivetran provides a best-in-class solution that consistently outperforms ADF.

For more than a decade, Cloudera has been an ardent supporter and committee member of Apache NiFi, long recognizing its power and versatility for data ingestion, transformation, and delivery. Our customers rely on NiFi as well as the associated sub-projects (Apache MiNiFi and Registry) to connect to structured, unstructured, and multi-modal data from a variety of data sources – from edge devices to SaaS tools to server logs and change data capture streams.

Welcome to the first installment of a series of posts discussing the recently announced Cloudera AI Inference service. Today, Artificial Intelligence (AI) and Machine Learning (ML) are more crucial than ever for organizations to turn data into a competitive advantage. To unlock the full potential of AI, however, businesses need to deploy models and AI applications at scale, in real-time, and with low latency and high throughput. This is where the Cloudera AI Inference service comes in.