Querying databases comes with costs—wall clock time, CPU usage, memory consumption, and potentially actual dollars. As your application scales, optimizing these costs becomes crucial. Materialized views offer a powerful solution by creating a pre-computed, optimized data representation. Imagine a retail scenario with separate customer and product tables. Typically, retrieving product details for a customer's purchase requires cross-referencing both tables.

At NodeSource, we pride ourselves on delivering the best tools and open source support for Node.js users. Staying aligned with the latest releases is a key part of that commitment. With the latest Long-Term Support (LTS) release, Node.js 22 (codenamed "Jod"), we continue to ensure our users have access to reliable, secure, and up-to-date **Node.js binaries **through our NodeSource Binary Distributions.

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

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.

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.

Today enterprises can leverage the combination of Cloudera and Snowflake—two best-of-breed tools for ingestion, processing and consumption of data—for a single source of truth across all data, analytics, and AI workloads. But now AWS customers will gain more flexibility, data utility, and complexity, supporting the modern data architecture.

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.

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.

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.

Ever commit code and wonder how it’s faring in the pipeline? Let's make every status update as clear as day. Every time you push new code, a series of automated CI builds and checks spring into action, each telling a part of your project's story. This is where git commit statuses come in—they transform the complex results of these processes into clear, actionable insights. Think of them as real-time signals that indicate whether your code is ready to go live or needs a little more attention.