News Aggregator

Augmenting the Client With Alpine.js

Aggregated on: 2024-10-04 14:06:23

This post is part of a series comparing different ways to implement asynchronous requests on the client, which is colloquially known as AJAX. I dedicated the previous post to Vue.js. I'll dedicate this one to Alpine.js — not to be confused with Alpine Linux. I'll follow the same structure as previously.

Understanding the Dependency Injection Lifecycle: Singleton, Scoped, and Transient With Detailed Examples

Aggregated on: 2024-10-03 22:06:23

Developers may be aware of the lifecycle of service instances when using dependency injection, but many don’t fully grasp how it works. You can find numerous articles online that clarify these concepts, but they often just reiterate definitions that you might already know. Let me illustrate with a detailed example that simplifies the explanation. When implementing dependency injection, developers have three options that determine the life cycle of the instances:

How to Create a Search Engine and Algorithm With ClickHouse and Snowflake

Aggregated on: 2024-10-03 20:06:23

ClickHouse is an open-source data warehousing solution that is architected as a columnar database management system. This makes it extremely powerful to work with massive datasets, especially ones that are long as they can be aggregated, ordered, or computed with low latency. When working with the same data type, it's very efficient for fast scanning and filtering of the data. This makes it a great use case for implementing a search engine. A lot of applications use Elasticsearch as their search engine solution. However, such an implementation can be expensive both in terms of cost and time. Copying the data over to Elasticsearch can also cause lags because data is being migrated to another data store. Also, setting up the Elasticsearch cluster, configuring the nodes and defining and fine-tuning indexes can take more programmatic work, which may not be justified for all projects.

Implementing Polling With RxJS

Aggregated on: 2024-10-03 18:06:23

In most front-end applications, fetching data from the back-end is a common requirement to provide up-to-date information to the user. Typically, this process is straightforward: know the API endpoint, create a function, call it, process the response, and display it on the page. However, there are scenarios — often rare or specific to certain business needs — where client-server communication must be more sophisticated. In this article, we’ll explore one such case: implementing repetitive API calls (polling) using RxJS. RxJS Refresher Before diving into polling, let’s take a moment to refresh our understanding of RxJS. For those new to it, RxJS (Reactive Extensions for JavaScript) is a library that brings reactive programming concepts into the JavaScript ecosystem. It’s particularly useful for managing asynchronous operations, offering powerful tools like Observables, Observers, Subjects, and Operators.

Why Doesn't Postgres Use My Index?

Aggregated on: 2024-10-03 16:06:23

It may happen that we created an index and yet PostgreSQL doesn’t use it. What could be the reason and how to fix it? We identified 11 distinct scenarios. Read on to find out. Important Things Indexes may be tricky. We already covered how they work in another article. Let’s quickly recap important parts about how they work.

Supercharge Your Search With GenAI: From Simple Queries to Smarter Results

Aggregated on: 2024-10-03 14:21:23

In specialized fields like law, medicine, or even fashion, search engines are critical tools that professionals use to find accurate, relevant information quickly. However, traditional search engines often struggle to interpret complex, domain-specific queries. That’s where Generative AI (GenAI) can revolutionize the process by transforming simple queries into powerful search instructions through query expansion and reformulation. By integrating Chain-of-Thought (CoT) prompting with Large Language Models (LLMs), you can significantly enhance the precision and relevance of search results. This tutorial will show you how to implement Flan-T5 (or similar models) for advanced query expansion and reformulation.

A Comprehensive Guide to Database Sharding: Building Scalable Systems

Aggregated on: 2024-10-02 23:06:23

In this article, we’ll dive deep into the concept of database sharding, a critical technique for scaling databases to handle large volumes of data and high levels of traffic. Here’s what you can expect to learn: What is Sharding?: We’ll start by defining what sharding is and why it’s essential for modern, high-performance databases. You’ll understand how sharding can help overcome the limitations of traditional database scaling methods. Types of Sharding: Next, we’ll explore the different types of sharding, including horizontal and vertical sharding. We’ll discuss the benefits and challenges of each approach, helping you decide which might be best for your use case. Selecting a Shard Key: Choosing the right shard key is crucial for the success of a sharded database. In this section, we’ll walk through the factors to consider when selecting a shard key, common mistakes to avoid, and how to balance performance with even data distribution. Routing Requests to Shards: Finally, we’ll cover the methods for routing queries to the correct shard. This section will provide insights into the architecture and strategies to ensure efficient query processing in a sharded environment. By the end of this guide, you’ll have a comprehensive understanding of database sharding, enabling you to implement it effectively in your systems.

Hyperscale Data Centers Ignite Construction Surge

Aggregated on: 2024-10-02 22:06:22

Despite the lack of a universal definition for hyperscale data centers, many people view them as massive facilities that typically handle mission-critical workloads distributed across numerous servers. These buildings have collectively contributed to a data center construction boom, emphasizing the world’s substantial and growing dependence on technologies and the infrastructure supporting them. Emerging Technologies Spurring Data Center Construction When a February 2024 study examined the state of the global data center industry, the results revealed some of the primary drivers of the current construction boom. One finding was many of the largest hyperscale providers increasingly accommodate clients’ artificial intelligence applications and other computing-intensive requirements.

Key Considerations for Effective AI/ML Deployments in Kubernetes

Aggregated on: 2024-10-02 21:06:22

Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, Kubernetes in the Enterprise: Once Decade-Defining, Now Forging a Future in the SDLC. Kubernetes has become a cornerstone in modern infrastructure, particularly for deploying, scaling, and managing artificial intelligence and machine learning (AI/ML) workloads. As organizations increasingly rely on machine learning models for critical tasks like data processing, model training, and inference, Kubernetes offers the flexibility and scalability needed to manage these complex workloads efficiently. By leveraging Kubernetes' robust ecosystem, AI/ML workloads can be dynamically orchestrated, ensuring optimal resource utilization and high availability across cloud environments. This synergy between Kubernetes and AI/ML empowers organizations to deploy and scale their ML workloads with greater agility and reliability.

Transforming Manufacturing: AI-Driven Quality Control and Predictive Maintenance Using Cloud and IoT

Aggregated on: 2024-10-02 19:51:23

In modern manufacturing, the key to sustaining product reliability, compliance with regulatory standards, and maintaining operational efficiency lies in robust quality control and advanced maintenance management. This study delves into the integration of AI technologies to interpret sensor data from production equipment. By employing predictive modeling techniques such as Stacked LSTM networks, RNNs, Random Forests, Gradient Boosting, SVM, ARIMA, and SARIMA, the goal is to forecast potential equipment failures, implement proactive maintenance solutions, and uphold rigorous quality checks through automated systems. This research showcases how AI-driven insights can be displayed through interactive dashboards and comprehensive reports, aiding in meeting regulatory standards while substantially improving operational dependability in manufacturing. Beyond the immediate benefits of reduced downtime and increased productivity, anomaly detection, especially in rotating machinery, plays a pivotal role in AI-driven predictive maintenance (PdM). The findings highlight how predictive modeling transforms equipment management, fostering innovation and supporting sustainability across various industries by utilizing sensors, cameras, and laser beam sensing technology.

Top 6 Cybersecurity Threat Detection Use Cases: How AI/ML Can Help Detect Advanced and Emerging Threats

Aggregated on: 2024-10-02 18:51:22

AI/ML tools and technologies heavily influence the modern digital landscape by introducing numerous use cases involving AI-based malware detection, preventing social engineering attacks, and threat identification and remediation. Many organizations have acknowledged AI/ML's prominence in the cybersecurity threat landscape and have started to leverage AI/ML in their business and security requirements. AI/ML-based solutions assist employees to complete their day-to-day tasks more efficiently and in no time. However, at the same time, we have also witnessed an exponential increase in the cybersecurity threat landscape. Hence, cybersecurity professionals and software developers have also started using AI/ML to build more innovative, adaptive, and robust systems and applications to protect an organization's IT and network infrastructure. Statistics: AI/ML in Cybersecurity In recent times, we have witnessed the widespread adoption and exponential growth of AI and ML technologies. Below are the statistics taken after thorough research highlighting the impact of AI/ML tools and techniques on cybersecurity threat detection and increasing overall cybersecurity performance.

AWS LetsEncrypt Lambda or Why I Wrote a Custom TLS Provider for AWS Using OpenTofu and Go

Aggregated on: 2024-10-02 17:36:22

These days, it's challenging to imagine systems that have public API endpoints without TLS certificate protection. There are several ways to issue certificates: Paid wildcard certificates that can be bought from any big TLS provider Paid root certificates that sign all downstream certificates that are issued by corporate PKI systems Free certificates issued by TLS providers like LetsEncrypt or AWS Certificate Manager Self-signed certificates, issued by OpenSSL or another tool Within the context of this post, I will mainly discuss free certificates that can be used inside of AWS, but not only by AWS services. Clearly, using anything other than AWS Certificate Manager makes no sense if you exclusively use managed AWS services and don't have strict security requirements. AWS Certificate Manager offers a very convenient and speedy method of issuing certificates via DNS or HTTP challenges; however, you face basic AWS limitations if you need to use these certificates outside of AWS services (API Gateway, ALB, NLB, etc.), such as an EC2 instance running Nginx that needs a physical certificate file. Additionally, even if you request it, AWS Certificate Manager does not display the certificate content.

Java 23: What Developers Need To Know

Aggregated on: 2024-10-02 16:21:22

As Java implements its 23rd major release on September 17, 2024, developers are in for a treat with many new features and improvements. Oracle's latest release promises to make development more efficient and enjoyable while enhancing the platform's performance, stability, and security. Let's dive into the key aspects developers should be excited about in Java 23. The Six-Month Release Cadence Continues Oracle is sticking to its six-month release cycle for Java, now in its seventh year. This approach has successfully delivered regular updates and innovations to the Java ecosystem. A Long-Term Support (LTS) version is released every two years, providing stability for enterprises that prefer less frequent updates. Java 23 marks the 14th release in this six-month cadence.

Misconfiguration Madness: Thwarting Common Vulnerabilities in the Financial Sector

Aggregated on: 2024-10-02 15:21:22

Ever since people started putting their money into banks and financial institutions, other people have sought to steal those deposits or otherwise fraudulently obtain those protected assets. When someone asked infamous 1920s-era bank robber Willie Sutton why he robbed banks, he simply replied, “Because that’s where the money is.” Today, much of the money held by banks and other financial institutions is in digital form, and many of the sensitive records held by those firms can be just as valuable as the digital currency itself. But the reasons behind the targeting of financial institutions by threat actors remain much the same as they were in Sutton’s time over 100 years ago: that’s where the money — and at least some valuable personal data — is kept.

How JavaScript DataTable Libraries Address Challenging Requirements in Web Data Management

Aggregated on: 2024-10-02 14:21:22

Web data management is becoming increasingly important as applications move towards being more complex and data-intensive. The necessity to manage large and complex datasets causes numerous challenges that demand effective solutions from development teams. Fortunately, web developers can rely on ready-made tools such as a JavaScript data table library (or simply a grid) that make it easier to deal with such issues effectively. In this article, we’ll consider the most challenging data management requirements encountered in web apps and highlight how popular JavaScript grid components help address these pain points.

Unit Integration Testing With Testcontainers Docker Compose

Aggregated on: 2024-10-02 13:21:22

Is your test dependent on multiple other applications and do you want to create an integration test using Testcontainers? Then the Testcontainers Docker Compose Module is the solution. In this blog, you will learn how convenient it is to create an integration test using multiple Testcontainers. Enjoy! Introduction Using Testcontainers is a very convenient way to write a unit integration test. Most of the time, you will use it in order to test the integration with a database, a message bus, etc. But what if your application interacts with an application that consists of multiple containers? How can you add these containers to your unit integration test? The answer is quite simple: you should use the Docker Compose Module.

Kubernetes Observability: Lessons Learned From Running Kubernetes in Production

Aggregated on: 2024-10-02 00:21:22

Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, Kubernetes in the Enterprise: Once Decade-Defining, Now Forging a Future in the SDLC. In recent years, observability has re-emerged as a critical aspect of DevOps and software engineering in general, driven by the growing complexity and scale of modern, cloud-native applications. The transition toward microservices architecture as well as complex cloud deployments — ranging from multi-region to multi-cloud, or even hybrid-cloud, environments — has highlighted the shortcomings of traditional methods of monitoring.

Leveraging Artificial Intelligence for Software Testing To Test Healthcare Applications

Aggregated on: 2024-10-01 22:21:23

The healthcare domain is complex, and Medicare often undervalues the potential of patients to adapt to modern technology. Despite this, technology can significantly enhance healthcare software products. However, the acquisition of these advancements is not easy for patients, particularly those on Medicare or with chronic conditions, due to unique obstacles. To offer top-notch software products, it's necessary to stay up-to-date with technology. AI-driven solutions are on the verge of rapid evolution, offering transformative benefits. For instance, predictive analytics that utilizes artificial intelligence can predict health risks based on patient data, which can lead to timely interventions. Furthermore, administrative tasks are streamlined through automated systems, resulting in improved efficiency and cost reduction.

Balance Trade-Offs in Microservices Architecture

Aggregated on: 2024-10-01 21:21:22

When architecting microservices, we deal with different non-functional requirements and their trade-offs. It's important to keep the right balance among different architectural aspects. Every architectural decision has its advantages and disadvantages, the challenge is finding a balance among them as much as possible according to what each system needs. We must balance the trade-offs in microservices architecture between various functional and non-functional elements to maximize the system efficiently. Following are the key trade-offs you can deal with in implementing the microservices, which are discussed with different scenario-based strategies:

How To Improve the Developer vs. AI Relationship

Aggregated on: 2024-10-01 20:21:22

Developers are often skeptical of artificial intelligence. Count me as one of them. But the potential of AI tools — in the right context, of course — should outweigh our skepticism, regardless of how justified it may be. Like it or not, the path to faster releases and better products runs through AI. It’s on the development community to adapt.

Workarounds for Oracle Restrictions on the Size of Expression Lists

Aggregated on: 2024-10-01 19:21:22

When developing an enterprise system — whether it is a completely new system or simply the addition of a new feature — it is not uncommon to need to retrieve a significant volume of records (a few hundred or even thousands) from an underlying relational database. A usual scenario is having a list of identifiers (user IDs, for example) and needing to retrieve a set of data from each of them. Disregarding all the ease provided by Object-Relational Mappers, among which Hibernate can be underlined as a very reliable option, a straightforward way to address such a situation is to build a SQL SELECT query and bind all identifiers as a comma-delimited list of expressions in combination with a SQL IN operator. The snippet below shows the query structure.

Best Practices for Market Data Replay

Aggregated on: 2024-10-01 18:21:22

This tutorial introduces the practical implementation of the DolphinDB’s replay feature. Based on DolphinDB’s streaming replay, distributed database architecture, and APIs, you can create a powerful tool for historical insight and model back-testing, which allows you to review situations of interest and improve future performance. By following the instructions in this tutorial, you will learn: The workflow of constructing a market replay solution The optimal data storage plan for data sources The methods to process and analyze replayed data via APIs The performance of the replay functionality Note: For detailed instructions on how to replay market data, refer to Tutorial: Market Data Replay.

Using Lombok Library With JDK 23

Aggregated on: 2024-10-01 17:21:22

Java 23 is finally out, and we can start migrating our project to it. The very first pitfall comes quickly when switching to the latest JDK 23 with compilation issues when using the Lombok library in your project. Let's begin with the symptom description first. Description The Lombok library heavily relies on annotations. It's used for removing a lot of boilerplate code; e.g., getters, setters, toString, loggers, etc.

Using AWS WAF Efficiently To Secure Your CDN, Load Balancers, and API Servers

Aggregated on: 2024-10-01 16:21:22

The introduction of software has made remarkable changes to how business is conducted. "Back then," people would meet in person, and most companies used manual methods, which were not scalable. Software has changed the game, and web applications are essential for a business's success. Software is how customers interact with businesses, share their data, and receive goods and services. Software-as-a-service (SaaS) has become a giant industry, taking care of hosting services used by customers by upgrading, scaling, and securing customer data. With the massive proliferation of SaaS services, many are using AWS, and security is a big concern. Malicious actors seek to steal customer data or DDoS-ing the service to prevent legitimate customers from accessing the website.

Guarding Kubernetes From the Threat Landscape: Effective Practices for Container Security

Aggregated on: 2024-10-01 15:21:22

Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, Kubernetes in the Enterprise: Once Decade-Defining, Now Forging a Future in the SDLC. Kubernetes is driving the future of cloud computing, but its security challenges require us to adopt a full-scale approach to ensure the safety of our environments. Security is not a one-size-fits-all solution; security is a spectrum, influenced by the specific context in which it is applied. Security professionals in the field rarely declare anything as entirely secure, but always as more or less secure than alternatives. In this article, we are going to present various methods to brace the security of your containers.

Azure Deployment Using FileZilla

Aggregated on: 2024-10-01 14:21:22

In today's digital landscape, deploying web applications to the cloud is a common practice. Azure provides various deployment options, including GitHub, Azure DevOps, Bitbucket, FTP, or a local Git repository. In this step-by-step guide, we will focus on the FileZilla FTP client as a means to publish your Angular UI application to Azure. Follow these steps to make your Angular app accessible to the world.

A Hands-On Guide to OpenTelemetry: Linking Metrics to Traces With Exemplars, Part 2

Aggregated on: 2024-10-01 13:21:22

Are you ready to start your journey on the road to collecting telemetry data from your applications? Great observability begins with great instrumentation! In this series, you'll explore how to adopt OpenTelemetry (OTel) and how to instrument an application to collect tracing telemetry. You'll learn how to leverage out-of-the-box automatic instrumentation tools and understand when it's necessary to explore more advanced manual instrumentation for your applications. By the end of this series, you'll have an understanding of how telemetry travels from your applications to the OpenTelemetry Collector, and be ready to bring OpenTelemetry to your future projects. Everything discussed here is supported by a hands-on, self-paced workshop authored by Paige Cruz.

Building a CI/CD Pipeline With Kubernetes: A Development Guide With Deployment Considerations for Practitioners

Aggregated on: 2024-09-30 23:51:21

Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, Kubernetes in the Enterprise: Once Decade-Defining, Now Forging a Future in the SDLC. In the past, before CI/CD and Kubernetes came along, deploying software to Kubernetes was a real headache. Developers would build stuff on their own machines, then package it and pass it to the operations team to deploy it on production. This approach would frequently lead to delays, miscommunications, and inconsistencies between environments. Operations teams had to set up the deployments themselves, which increased the risk of human errors and configuration issues. When things went wrong, rollbacks were time consuming and disruptive. Also, without automated feedback and central monitoring, it was tough to keep an eye on how builds and deployments were progressing or to identify production issues.

Building a Food Inventory Management App With Next.js, Material-UI, Firebase, Flask, and Hugging Face

Aggregated on: 2024-09-30 23:21:21

These days, restaurants, food banks, home kitchens, and any other business that deals with products and foods that go bad quickly need to have good food inventory management. Kitchens stay organized and waste is kept to a minimum by keeping track of stock, checking expiration dates, and managing usage well. I will show you how to make a Food Inventory Management App in this guide. With this app, users can:

Network Sniffing: A Critical Concept in Network Security

Aggregated on: 2024-09-30 22:21:21

What Is Network Sniffing? Sniffing includes the passive interception of data packets crossing a network with further analysis. Initially, sniffing was developed to help network administrators troubleshoot connectivity problems, and since then, it has evolved into an important technique of network management and security testing. How Does Network Sniffing Work? Basically, network sniffing is based on the fact that a network interface card is set in "promiscuous mode:" all network traffic crossing its path is captured, not just packets addressed to the device. Then sniffing software analyzes the packets captured, allowing a user to read and interpret the data crossing the network.

A Decade of Excellence: The Journey, Impact, and Future of Kubernetes

Aggregated on: 2024-09-30 20:51:21

Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, Kubernetes in the Enterprise: Once Decade-Defining, Now Forging a Future in the SDLC. A decade ago, Google introduced Kubernetes to simplify the management of containerized applications. Since then, it has fundamentally transformed the software development and operations landscape. Today, Kubernetes has seen numerous enhancements and integrations, becoming the de facto standard for container orchestration.

Using Spring AI With LLMs To Generate Java Tests

Aggregated on: 2024-09-30 20:21:21

The AIDocumentLibraryChat project has been extended to generate test code (Java code has been tested). The project can generate test code for publicly available GitHub projects. The URL of the class to test can be provided then the class is loaded, the imports are analyzed and the dependent classes in the project are also loaded. That gives the LLM the opportunity to consider the imported source classes while generating mocks for tests. The testUrl can be provided to give an example to the LLM to base the generated test. The granite-code and deepseek-coder-v2 models have been tested with Ollama. The goal is to test how well the LLMs can help developers create tests.

Geo-Location Redirects With AWS CloudFront

Aggregated on: 2024-09-30 19:06:21

In the age of global digital services, geo-location-based content is critical for improving user experience and engagement, especially if you implement any shops or subscription services that should be adopted by the local market. AWS CloudFront is one of the most widely used content delivery network (CDN) systems. It includes certain necessary features to implement geo-location-based redirection out of the box, but not in a single click.

How To Improve RAG Quality by Storing Knowledge Graph in Vector Database

Aggregated on: 2024-09-30 18:06:21

Retrieval-Augmented Generation (RAG) techniques, by integrating external knowledge bases, provide additional contextual information for LLMs, effectively alleviating issues such as hallucination and insufficient domain knowledge of LLM. However, relying solely on general knowledge bases has its limitations, especially when dealing with complex entity relationships and multi-hop questions, where the model often struggles to provide accurate answers. Introducing Knowledge Graphs (KGs) into the RAG system provides a new solution to this problem. KGs present entities and their relationships in a structured manner, offering more refined contextual information during retrieval. By leveraging the abundant relational data of KGs, RAG can not only pinpoint relevant knowledge more accurately but also better handle complex question-answering scenarios, such as comparing entity relationships or answering multi-hop questions.

Why Haven’t You Upgraded to HTTP/2?

Aggregated on: 2024-09-30 17:06:21

From a Java perspective, I’ve been the beneficiary of some pretty amazing features over the years: Generics (Java 5) Streams and Lambda Expressions (Java 8) Enhanced Collection Functionality (Java 9) Sealed Classes (Java 17) As key features become available, I’ve been able to reduce development time as I implement features, while also seeing benefits in performance and supportability.

Transformers: Age of Attention

Aggregated on: 2024-09-30 16:06:21

In 2017, a group of researchers (from Google and the University of Toronto) introduced a new way to handle natural language processing (NLP) tasks. Their revolutionary paper “Attention is All You Need” presented the Transformer model, an architecture that has since become the basis of many advanced AI systems today. The model's performance, scalability, and versatility have led to its widespread adoption, forming the backbone of state-of-the-art models like BERT (Bidirectional Encoder Representations) and GPT (Generative Pre-trained Transformers). Before the Transformer model, most AI models that processed language relied heavily on a type of neural network called a Recurrent Neural Network (RNN) or its improved version, the Long Short-Term Memory Network (LSTM). In particular, problems like language modeling and machine translation (also called sequence transduction). These models processed words in a sequence, one by one, from left to right (or vice versa). While this approach made sense because words in a sentence often depend on the previous words, it had some significant drawbacks:

Agile Primitives

Aggregated on: 2024-09-30 15:06:21

TL;DR: Agile Primitives Are we losing sight of what truly matters in Agile? The future isn’t about rigidly adhering to or outright dismissing frameworks like Scrum or SAFe. Instead, it’s about returning to the fundamental principles — the Agile Primitives — that genuinely empower teams to adapt, innovate, and deliver real value. By refocusing on these core elements, organizations can move beyond mere methodological compliance and embrace authentic agility. Dive in to explore how the Agile community is coming full circle, rediscovering the essence that made Agile transformative in the first place.

Stream Processing in the Serverless World

Aggregated on: 2024-09-30 14:06:21

It’s a very dynamic world today. Information moves fast. Businesses generate data constantly. Real-time analysis is now essential. Stream processing in the serverless cloud solves this. Gartner predicts that by 2025, over 75% of enterprise data will be processed outside traditional data centers. Confluent states that stream processing lets companies act on data as it's created. This gives them an edge. Real-time processing reduces delays. It scales easily and adapts to changing needs. With a serverless cloud, businesses can focus on data insights without worrying about managing infrastructure.

Maximizing Cloud Network Security With Next-Generation Firewalls (NGFWs): Key Strategies for Performance and Protection

Aggregated on: 2024-09-30 13:06:21

As cloud networks continue to expand, security concerns become increasingly complex, making it critical to ensure robust protection without sacrificing performance. One key solution organizations use to achieve this balance is the deployment of Next-Generation Firewalls (NGFWs), which play an essential role in securing cloud environments. These advanced firewalls are integral to cloud security strategies, combining multiple layers of defense with optimized performance to tackle evolving threats. Understanding NGFWs in Cloud Networks To fully appreciate the scope of cloud network security, it's essential to understand both the capabilities of NGFWs and how they integrate into broader cloud security approaches. NGFWs go beyond traditional firewalls, offering advanced features such as deep packet inspection, application-level filtering, intrusion prevention systems (IPS), SSL/TLS inspection, and threat intelligence integration. Together, these tools help NGFWs safeguard dynamic cloud environments from sophisticated attacks, ensuring a higher level of protection.

Using AI in Your IDE To Work With Open-Source Code Bases

Aggregated on: 2024-09-28 13:06:20

Thanks to langchaingo, it's possible to build composable generative AI applications using Go. I will walk you through how I used the code generation (and software development in general) capabilities in Amazon Q Developer using VS Code to enhance langchaingo. Let's get right to it!

Leveraging IBM WatsonX Data With Milvus To Build an Intelligent Slack Bot for Knowledge Retrieval

Aggregated on: 2024-09-27 22:21:20

In today's fast-paced work environment, quick and easy access to information is crucial for maintaining productivity and efficiency. Whether it's finding specific instructions in a runbook or accessing key knowledge transfer (KT) documents, the ability to retrieve relevant information swiftly can make a significant difference. This tutorial will guide you through building an intelligent Slack bot that leverages IBM WatsonX.data and Milvus for efficient knowledge retrieval. By integrating these tools, you'll create a bot that can search and provide answers to queries based on your organization's knowledge sources. We will use IBM WatsonX.data to populate and query relevant documents and IBM WatsonX.ai to answer questions from the fetched documents.

Indexes Under The Hood

Aggregated on: 2024-09-27 20:21:20

Many things can improve database performance. One of the most obvious solutions is to use indexes. This blog post will examine them in detail to understand how they work in PostgreSQL and what makes them useful. Data Storage Basics Before getting to indexes, we need to understand the basics of data storage in PostgreSQL. We will explain how things are represented internally, but we won’t cover all the aspects of the data storage like shared memory and buffer pools.

Creating MVPs on a Budget: A Guide for Tech Startups

Aggregated on: 2024-09-27 18:51:20

You must have used Snapchat and Uber, but do you know how they started? These widely used applications began as minimum viable products (MVPs). Uber was initially launched as “UberCab,” a basic, on-demand ride-hailing app with one main feature: allowing users in San Francisco to book black car services through their smartphones. Similarly, Snapchat started as a single-feature application called “Picaboo,” enabling users to send photos that would disappear after a few seconds.

Hands-On: Assigning Pods to Nodes Using Affinity Rules

Aggregated on: 2024-09-27 17:06:20

This article describes how to configure your Pods to run in specific nodes based on affinity and anti-affinity rules. Affinity and anti-affinity allow you to inform the Kubernetes Scheduler whether to assign or not assign your Pods, which can help optimize performance, reliability, and compliance. There are two types of affinity and anti-affinity, as per the Kubernetes documentation:

Is Spring AI Strong Enough for AI?

Aggregated on: 2024-09-27 15:51:20

In recent years, there has been a significant surge in the adoption of artificial intelligence (AI) and machine learning (ML) technologies across a wide range of industries. Frameworks such as TensorFlow, PyTorch, and Scikit-learn have emerged as popular choices for AI development due to their versatility and robustness. However, the seamless integration of AI into enterprise-grade, production-ready applications poses unique challenges that need to be addressed. Spring, a widely recognized enterprise-level framework, is celebrated for its exceptional robustness, scalability, and flexibility in crafting sophisticated applications. Nevertheless, the question arises: can Spring effectively cater to the intricate demands of AI/ML-based applications? This article aims to explore the depths of Spring's capabilities within the AI domain, its potential integration with AI libraries, and its ability to effectively manage AI workflows within production environments.

Low-Code AI Agent for Classifying User Support Tickets With OpenAI and Kumologica

Aggregated on: 2024-09-27 13:06:20

This article will demonstrate how Kumologica and OpenAI can assist in developing an AI agent API that efficiently classifies cases within an enterprise using user data, without the need for customer support agent intervention. An ideal case management solution should be able to automatically categorize and prioritize cases from various channels, identify high-priority cases, apply labels, and assign them to the appropriate groups without requiring agents to manually perform these tasks. There are several case management products currently available on the market, such as ServiceNow, JIRA, and Salesforce Case Management. While some of these products include built-in solutions for ticket classification, others offer no such functionality. Even for those with built-in solutions, there are limitations in their ability to integrate with third-party systems.

Advanced Strategies for Building Modern Data Pipelines

Aggregated on: 2024-09-26 23:51:19

In today's data-driven world, organizations increasingly rely on sophisticated data pipelines to manage vast volumes of data generated daily. A data pipeline is more than just a conduit for data — it is a complex system that involves the extraction, transformation, and loading (ETL) of data from various sources to ensure that it is clean, consistent, and ready for analysis. This article explores advanced strategies, the process of building data pipelines, and the pillars of a successful modern data strategy, with a focus on both real-time and batch data processing. Steps for Building Data Pipelines Building modern data pipelines that are efficient, scalable, and resilient requires advanced strategies and a deep understanding of the underlying technologies. Let’s dive into the key steps to building out your data pipelines.

Integrating Data Engineering Into Artificial Intelligence

Aggregated on: 2024-09-26 23:06:19

Data is often referred to as the lifeblood of artificial intelligence and machine learning (ML). We know the value of data: it fuels insights, provides input for predictions, and is foundational for data engineers' and data scientists' decisions. The journey from raw data to meaningful information is lengthy which is where data engineering fits into play. Incorporating data engineering and dealing with the messiness of the dataset is crucial to having a smooth flow of data through the ML pipeline, leading us to better models and consistent results. In this article, I’ll take a deep dive into how data engineering and Machine Learning intersect and why it is important in this era of modern data-driven applications. Importance of Data Engineering in Artificial Intelligence Machine learning relies on data and its accuracy. Raw data transformation involves landing data, cleaning, transformation as per requirements, and loading to the target to make it meaningful. These activities take from extracting the source systems up to a point where you analyze using some tool/configuration. The ML lifecycle has these stages, and data engineering is the focus of each step.

Maturing an Engineering Organization From DevOps to Platform Team

Aggregated on: 2024-09-26 22:06:19

The DevOps model broke down the wall between development and production by assigning deployment and production management responsibilities to the application engineers and providing them with infrastructure management tools. This approach expanded engineers' competencies beyond their initial skill sets. This model helped companies gain velocity as applications weren't passed around from team to team, and owners became responsible from ideation to production. It shortened the development lifecycle and time to deployment, making companies more agile and responsive.

Java for AI

Aggregated on: 2024-09-26 21:06:19

Artificial Intelligence (AI) is modernizing industries by enabling machines to perform tasks that typically require no human intervention; tasks such as problem-solving, natural language understanding, and image processing. For AI-related software development, Python is often used. However, Java is also a powerful option, as many organizations are using it in enterprise applications due to its robustness and scalability. In this article, we explore how the Java programming language can be used for AI development, along with supporting libraries and tools. Java Programming Language for AI Java offers several features that make Java suitable for AI-related task development: