Exposed: Custom column types
Exposed is a handy SQL library for Kotlin, but what happens when standard SQL types just don't cut it? You might need to support specific database features, like PostgreSQL's enum or ltree, or perhaps you want to map a column directly to a domain-specific type that truly fits your business logic.
This is exactly where custom column types shine. By implementing your own, you gain precise control over how data is stored and retrieved, all while maintaining that crucial type safety. It’s a powerful way to make the database align perfectly with your code, not the other way around.
Let's dive into the implementation for a PostgreSQL enum.
Exposed is a handy SQL library for Kotlin, but what happens when standard SQL types just don't cut it? You might need to support specific database features, like PostgreSQL's enum or ltree, or perhaps you want to map a column directly to a domain-specific type that truly fits your business logic.
This is exactly where custom column types shine. By implementing your own, you gain precise control over how data is stored and retrieved, all while maintaining that crucial type safety. It’s a powerful way to make the database align perfectly with your code, not the other way around.
Let's dive into the implementation for a PostgreSQL enum.
Comparison: StarRocks vs Apache Druid
Apache Druid has long been a staple for real-time analytics, but let's be honest: today's analytical demands are becoming incredibly sophisticated. As data performance needs evolve, even established solutions are facing new challenges. This is where StarRocks makes its entrance—a high-performance, open-source analytical database designed specifically to meet these advanced enterprise needs.
It's not just about replacing an incumbent; it's about a shift in capabilities. StarRocks promises robust performance for contemporary workloads, but how does it really stack up against a well-known veteran like Druid? We're looking beyond the hype at core functionalities, strengths, and benchmark results.
Let's dig into the practical examples and see which database best fits your needs.
Apache Druid has long been a staple for real-time analytics, but let's be honest: today's analytical demands are becoming incredibly sophisticated. As data performance needs evolve, even established solutions are facing new challenges. This is where StarRocks makes its entrance—a high-performance, open-source analytical database designed specifically to meet these advanced enterprise needs.
It's not just about replacing an incumbent; it's about a shift in capabilities. StarRocks promises robust performance for contemporary workloads, but how does it really stack up against a well-known veteran like Druid? We're looking beyond the hype at core functionalities, strengths, and benchmark results.
Let's dig into the practical examples and see which database best fits your needs.
AWS SageMaker: Choosing the Right Inference Type for ML Models
Deploying a model in AWS SageMaker seems simple until you hit that one critical question: which inference type should you choose? You're faced with four options—Real-Time, Serverless, Batch Transform, and Asynchronous. At first glance, the differences aren't obvious, yet picking the wrong one can be a costly mistake, leaving you paying for 24/7 idle instances or forcing users to endure a painful 30-second cold start.
The right choice isn't about which is "best," but which is right for your specific task. It all hinges on four key factors: payload size, expected latency, traffic patterns, and whether you're willing to pay for idle time. Understanding these trade-offs is the key to optimizing both performance and your AWS bill.
Let's dig into the specs, practical examples, and pricing models for each.
Deploying a model in AWS SageMaker seems simple until you hit that one critical question: which inference type should you choose? You're faced with four options—Real-Time, Serverless, Batch Transform, and Asynchronous. At first glance, the differences aren't obvious, yet picking the wrong one can be a costly mistake, leaving you paying for 24/7 idle instances or forcing users to endure a painful 30-second cold start.
The right choice isn't about which is "best," but which is right for your specific task. It all hinges on four key factors: payload size, expected latency, traffic patterns, and whether you're willing to pay for idle time. Understanding these trade-offs is the key to optimizing both performance and your AWS bill.
Let's dig into the specs, practical examples, and pricing models for each.
AI-Powered Social Engineering
Those clumsy phishing emails with bad grammar and spelling mistakes? They're quickly becoming a thing of the past. The new threat is AI-generated: perfectly crafted, hyper-personalized, and deployed at a massive scale. Attackers are now using AI not just for convincing social engineering, but to discover zero-day vulnerabilities and generate polymorphic malware that evades traditional detection.
But this is a full-blown arms race. Defenders are firing back with the same technology, leveraging AI for real-time behavioral analysis to spot anomalies, enhance threat intelligence to predict attacks, and automate incident response to contain threats in milliseconds. It’s AI versus AI, and the most dangerous position to take is believing it's someone else's problem.
Let's dive into the stats, strategies, and code behind this new digital battlefield.
Those clumsy phishing emails with bad grammar and spelling mistakes? They're quickly becoming a thing of the past. The new threat is AI-generated: perfectly crafted, hyper-personalized, and deployed at a massive scale. Attackers are now using AI not just for convincing social engineering, but to discover zero-day vulnerabilities and generate polymorphic malware that evades traditional detection.
But this is a full-blown arms race. Defenders are firing back with the same technology, leveraging AI for real-time behavioral analysis to spot anomalies, enhance threat intelligence to predict attacks, and automate incident response to contain threats in milliseconds. It’s AI versus AI, and the most dangerous position to take is believing it's someone else's problem.
Let's dive into the stats, strategies, and code behind this new digital battlefield.