News Aggregator

Sanity Testing vs. Regression Testing: Key Differences

Aggregated on: 2024-06-10 18:21:05

As part of the Software Development Life Cycle (SDLC), different types of testing are performed, each targeting specific elements and aspects of the software application. Among those, sanity and regression testing are two of the most important types of software testing. Comparing sanity testing and regression testing, sanity testing is the process that involves a quick test of the software application when it goes through any minor changes to make sure that the software is stable for further testing. When it comes to regression testing, it validates the changes made to the software (fixing bugs, adding features) haven't broken anything and that everything built previously works as expected.

dovpanda: Unlock Pandas Efficiency With Automated Insights

Aggregated on: 2024-06-10 18:21:05

Writing concise and effective Pandas code can be challenging, especially for beginners. That's where dovpanda comes in. dovpanda is an overlay for working with Pandas in an analysis environment. dovpanda tries to understand what you are trying to do with your data and helps you find easier ways to write your code and helps in identifying potential issues, exploring new Pandas tricks, and ultimately, writing better code – faster. This guide will walk you through the basics of dovpanda with practical examples. Introduction to dovpanda dovpanda is your coding companion for Pandas, providing insightful hints and tips to help you write more concise and efficient Pandas code. It integrates seamlessly with your Pandas workflow. This offers real-time suggestions for improving your code.

JavaScript, Node.js, and Apache Kafka for Full-Stack Data Streaming

Aggregated on: 2024-06-10 17:06:05

JavaScript is a pivotal technology for web applications. With the emergence of Node.js, JavaScript became relevant for both client-side and server-side development, enabling a full-stack development approach with a single programming language. Both Node.js and Apache Kafka are built around event-driven architectures, making them naturally compatible for real-time data streaming. This blog post explores open-source JavaScript clients for Apache Kafka and discusses the trade-offs and limitations of JavaScript Kafka producers and consumers compared to stream processing technologies such as Kafka Streams or Apache Flink. JavaScript: A Pivotal Technology for Web Applications JavaScript is a pivotal technology for web applications, serving as the backbone of interactive and dynamic web experiences. Here are several reasons JavaScript is essential for web applications:

Twilio Empowers Developers With Enhanced Data Warehouse Interoperability

Aggregated on: 2024-06-10 15:51:05

In the era of data-driven decision-making, businesses are constantly seeking ways to harness the power of their customer data to deliver personalized experiences. Twilio, the leading customer engagement platform, has taken a significant step forward in this direction with the announcement of Linked Audiences and the debut of the Segment Data Graph at Snowflake Summit 2024. These new product features enhance Twilio Segment's interoperability with data platforms and warehouses, empowering developers, engineers, and architects to unlock the full potential of customer data. Unlocking the Power of Customer Data Twilio's Linked Audiences and Segment Data Graph are game-changers for businesses that rely on data platforms and warehouses for managing customer data. By enhancing interoperability between Segment's CDP and data warehouses, Twilio enables businesses to transform static data repositories into dynamic, actionable engines that drive real-time, personalized customer experiences.

Understanding MVP: Striking the Balance Between Minimum and Viable

Aggregated on: 2024-06-10 14:36:06

"Is this feature needed for MVP? Why do we need more budget for our MVP? Why didn't users mention this requirement during MVP definition? Why can't we deliver the MVP faster?" If any of these questions sound familiar, keep reading. If you've ever been part of an Agile team or involved in technology development, you've likely encountered the term "MVP," or Minimum Viable Product. Despite its seemingly straightforward definition, the concept of MVP often leads to confusion and misapplication. Misunderstanding MVP can cause product failures, as teams may incorrectly prioritize "minimum" over "viable." This article aims to demystify MVP and provide clarity on its true meaning and application in product development.

Exploring WebAssembly for Cloud-Native Development

Aggregated on: 2024-06-10 13:21:07

I’m a senior solution architect and polyglot programmer interested in the evolution of programming languages and their impact on application development. Around three years ago, I encountered WebAssembly (Wasm) through the .NET Blazor project. This technology caught my attention because it can execute applications at near-native speed across different programming languages. This was especially exciting to me as a polyglot programmer since my programming expertise ranges across multiple programming languages including .NET, PHP, Node.js, Rust, and Go. Most of the work I do is building cloud-native enterprise applications, so I have been particularly interested in advancements that broaden Wasm’s applicability in cloud-native development. WebAssembly 2.0 was a significant leap forward, improving performance and flexibility while streamlining integration with web and cloud infrastructures to make Wasm an even more powerful tool for developers to build versatile and dynamic cloud-native applications. I aim to share the knowledge and understanding I've gained, providing an overview of Wasm’s capabilities and its potential impact on the cloud-native development landscape.

Snowflake Empowers Developers to Easily Build Data-Driven Apps and Chatbots

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

In today's fast-paced, data-driven world, developers, engineers, and architects are constantly seeking ways to create intelligent applications and chatbots that harness the power of data. Snowflake, the Data Cloud company, has been at the forefront of empowering these professionals with its unified platform, cutting-edge AI capabilities, and powerful tools like Snowpark and Streamlit. This article explores how Snowflake is making it easier for developers to build data-driven apps and chatbots, drawing insights from the Snowflake Builder's Keynote at the Snowflake Data Cloud Summit and an interview with Amanda Kelly, Director of Streamlit and Product Experiences at Snowflake. The Snowflake Advantage: A Unified Platform One of the key advantages of Snowflake is its single, unified platform. As Jeff Hollan, Director of Product at Snowflake, stated during the Builder's Keynote, "We don't make you do the hard work of figuring out all these different pieces put together. It all just comes together in a single, unified platform." This unified approach allows developers to focus on building applications rather than worrying about integrating disparate systems.

Harnessing the Power of Gen AI: Real-World Applications and Best Practices

Aggregated on: 2024-06-08 15:06:02

The advent of Generative AI (Gen AI) has sparked a revolution across industries, promising to transform the way we approach problem-solving and innovation. As the hype surrounding this technology reaches fever pitch, it is crucial for developers, engineers, and architects to navigate between the excitement and the reality. At the Snowflake Data Cloud Summit, I had the opportunity to attend a couple of sessions on AI – AI for Good and The GenAI Wave: Navigating Between Hype and Reality. In this article, we will explore real-world applications of Gen AI, best practices for implementation, and strategies for leveraging this technology to drive tangible business value.

Using AWS Data Lake and S3 With SQL Server: A Detailed Guide With Research Paper Dataset Example

Aggregated on: 2024-06-07 23:35:59

The integration of AWS Data Lake and Amazon S3 with SQL Server provides the ability to store data at any scale and leverage advanced analytics capabilities. This comprehensive guide will walk you through the process of setting up this integration, using a research paper dataset as a practical example. What Is a Data Lake? A data lake serves as a centralized repository for storing both structured and unstructured data, regardless of its size. It empowers users to perform a wide range of analytics, including visualizations, big data processing, real-time analytics, and machine learning.

When Should You Use Distributed PostgreSQL for Gen AI Apps?

Aggregated on: 2024-06-07 22:50:59

Postgres continues to evolve the database landscape beyond traditional relational database use cases. Its rich ecosystem of extensions and derived solutions has made Postgres a formidable force, especially in areas such as time-series and geospatial, and most recently, gen(erative) AI workloads. Pgvector has become a foundational extension for gen AI apps that want to use Postgres as a vector database. In brief, pgvector adds a new data type, operators, and index types to work with vectorized data (embeddings) in Postgres. This allows you to use the database for similarity searches over embeddings.

Comprehensive Guide to Data Analysis and Visualization With Pandas and Matplotlib

Aggregated on: 2024-06-07 21:50:59

Data analysis and visualization are fundamental skills in the realm of data science. Python, a versatile programming language, offers robust libraries like Pandas and Matplotlib to facilitate these tasks. Pandas provides powerful data manipulation capabilities, while Matplotlib is excellent for creating a wide range of visualizations. This tutorial will walk you through the process of analyzing a dataset and creating insightful visualizations using these libraries. By the end of this tutorial, you will be equipped with the knowledge to handle data more effectively and present your findings visually. Data Preparation The first step in any data analysis project is to prepare the data. Data preparation involves collecting, cleaning, and organizing data into a structured format. We'll start by importing the necessary libraries and creating a sample dataset that includes information on various products, their sales, and profit figures.

The Role of Data Engineering in Building Scalable AI Systems

Aggregated on: 2024-06-07 20:50:59

Data is the new oil. It’s valuable, but if unrefined it cannot be used. It has to be changed into gas, plastic, chemicals, etc. to create a valuable entity that drives profitable activity; so data must be broken down and analyzed for it to have value. — Clive Humby, 2006 One of the most clichéd yet true phrases is that technology changes every day and impacts everything. Artificial intelligence is one of the most prominent disciplines bringing disruptions to every industry around the world. With computing technology being upgraded and improved yearly, artificial intelligence (AI) technology is ushering in a new era of innovation across various industries. From healthcare and wellness to finance and manufacturing, AI-powered solutions are transforming the norm of how businesses operate, offer insights, and make well-informed decisions like never before. However, to be effective on a global scale, AI requires a massive amount of data to learn from and be trained on. Therefore, a robust foundation of data engineering is essential for every successful AI application.

Handling Alerts and Popups in Appium

Aggregated on: 2024-06-07 19:50:59

It is common for any mobile application to show alerts and popups. These alerts and popups prompt users to grant various permissions, like contacts, notifications, etc. However, they need to be handled when performing mobile application testing. Though there are many approaches to handling these interactions, choosing the right tool and technique is important to ensure a seamless mobile app testing experience. Handling alerts and popups in Appium is essential for achieving efficient test execution, enhanced test coverage, and more. In this Appium tutorial, let’s understand the different types of alerts and popups in Appium and how to handle them in our mobile automated tests.

Even More OpenTelemetry!

Aggregated on: 2024-06-07 19:05:59

I continue to work on my OpeneTelemetry demo. Its main idea is to showcase traces across various technology stacks, including asynchronous communication via an MQTT queue. This week, I added a couple of components and changed the architecture. Here are some noteworthy learnings; note that some of them might not be entirely connected to OpenTelemetry. Here's an updated diagram. New components appear in violet, and updated components appear in green.

How To Build a Simple GitHub Action To Deploy a Django Application to the Cloud

Aggregated on: 2024-06-07 17:05:59

Continuous integration and continuous delivery (CI/CD) capabilities are basic expectations for modern development teams who want fast feedback on their changes and rapid deployment to the cloud. In recent years, we’ve seen the growing adoption of GitHub Actions, a feature-rich CI/CD system that dovetails nicely with cloud hosting platforms such as Heroku. In this article, we’ll demonstrate the power of these tools used in combination — specifically how GitHub Actions can be used to quickly deploy a Django application to the cloud. A Quick Introduction to Django Django is a Python web application framework that’s been around since the early 2000s. It follows a model-view-controller (MVC) architecture and is known as the “batteries-included” web framework for Python. That’s because it has lots of capabilities, including a strong object-relational mapping (ORM) for abstracting database operations and models. It also has a rich templating system with many object-oriented design features.

Getting Started With AI Functions

Aggregated on: 2024-06-07 17:05:59

This past week we went "all-in" on AI functions. An AI function is the ability to create AI assistant logic, allowing the chatbot to "do things," instead of just passively generating text. To understand the power of such functions you can read some of our previous articles about the subject.

Embracing Responsible AI: Principles and Practices

Aggregated on: 2024-06-07 15:50:59

Today we use AI to offer simpler solutions to intricate problems in various sectors such as education, transportation, finance, and healthcare. Due to this reason, it is very important to adhere to best practices and standards. Adhering to AI principles is not a matter of ethics but is essential for establishing trust promoting fairness and ensuring the advancement of AI technologies. These guiding principles are crafted to instill confidence, mitigate risks, and ensure that AI contributes positively to society while respecting rights and ethical values. Microsoft Responsible AI Guidelines Ethical artificial intelligence encompasses creating, implementing, and overseeing AI systems in a responsible way. The goal is to optimize the benefits of intelligence while mitigating risks and negative impacts. Here are key reasons why embracing AI practices is paramount and Microsoft Identified the six principles below.

ChatGPT: Your Guide to SQL Query Translation Between Databases

Aggregated on: 2024-06-07 14:50:59

Everyone knows that ChatGPT is perfect for translating between many human languages. But did you know that this language model can also convert SQL queries between various database dialects? Whether you are transitioning from MySQL to PostgreSQL, SQL Server to Oracle, or any other combination, ChatGPT can assist in accurately translating your SQL queries. This capability extends beyond simple syntax changes, providing insights into how database systems handle data types, functions, and constraints. By leveraging ChatGPT for SQL translation, you can ensure a smoother and more efficient transition between database systems, maintaining data integrity and query performance.

Takeaways From Day 2 of the Snowflake Summit 2024

Aggregated on: 2024-06-07 13:05:59

Snowflake Data Cloud Summit 2024 has taken the data world by storm, showcasing groundbreaking innovations that empower enterprises to harness the power of AI and drive unparalleled value from their data. From advancements in Snowflake Cortex AI and Snowflake ML to enhanced data collaboration and developer tools, the event has set the stage for a new era of enterprise AI. Strengthening the Data Foundation During the Product Keynote, Christian Kleinerman, SVP of Product at Snowflake, emphasized the importance of strengthening the data foundation. "Our single goal with the Snowflake AI Data Cloud is to help you get more value out of your data and help you achieve the goals for your company faster, better, and, of course, leverage trends like artificial intelligence," Kleinerman stated.

Why Is Kubernetes Debugging So Problematic?

Aggregated on: 2024-06-06 23:50:57

Debugging application issues in a Kubernetes cluster can often feel like navigating a labyrinth. Containers are ephemeral by design and intended to be immutable once deployed. This presents a unique challenge when something goes wrong and we need to dig into the issue. Before diving into the debugging tools and techniques, it's essential to grasp the core problem: why modifying container instances directly is a bad idea. This blog post will walk you through the intricacies of Kubernetes debugging, offering insights and practical tips to effectively troubleshoot your Kubernetes environment. The Problem With Kubernetes Video

GenAI-Powered Automation and Angular

Aggregated on: 2024-06-06 22:35:57

Building a full-featured Angular browser/mobile user interface requires a very specific skill set. There is a vast array of tools, frameworks, and platforms, and each requires a steep learning curve. An open-source project, ApiLogicServer, has released a technical preview that combines GenAI-powered microservice automation with Ontimize, an Angular UI framework. While the promise of a complete running application is the goal of GenAI-enabled microservice, the developer will still need to interact with the generated components to create a finished web application.

Techniques for Chaos Testing Your Redis Cluster

Aggregated on: 2024-06-06 21:35:57

For large-scale, distributed systems, chaos testing becomes an essential tool. It helps uncover potential failure points and strengthen overall system resilience. This article delves into practical and straightforward methods for injecting chaos into your Redis cluster, enabling you to proactively identify and address weaknesses before they cause real-world disruptions. Set Up Create a Redis cluster

Machine Learning With Python: Data Preprocessing Techniques

Aggregated on: 2024-06-06 20:35:57

Machine learning continues to be one of the most rapidly advancing and in-demand fields of technology. Machine learning, a branch of artificial intelligence, enables computer systems to learn and adopt human-like qualities, ultimately leading to the development of artificially intelligent machines. Eight key human-like qualities that can be imparted to a computer using machine learning as part of the field of artificial intelligence are presented in the table below. Human Quality

Building a Tool To Generate Text With OpenAI’s GPT-4 Model

Aggregated on: 2024-06-06 19:35:57

In this tutorial, we will guide you through the process of building a tool that utilizes OpenAI's GPT-4 model to produce text based on user prompts. We will cover setting up your environment, making API calls to OpenAI's model, and integrating the tool into a basic application. By the end of this tutorial, you will have a functional tool that can generate text by interacting with OpenAI's GPT-4 model. Prerequisites Basic understanding of Python programming An OpenAI API key (sign up at OpenAI's website if you don't have one) A working Python environment (Python 3.7+) Step 1: Setting up Your Environment First, you need to install the OpenAI Python client library. Open your terminal and run:

Mocking Dependencies and AI Is the Next Frontier in Vue.js Testing

Aggregated on: 2024-06-06 18:50:57

Vue.js is a popular JavaScript framework, and as such, it is crucial to ensure that its components work as they are supposed to: effectively, and more importantly, reliably. Mocking dependencies is one of the most efficient methods of testing, as we will discover in this article. The Need for Mocking Dependencies Mocking dependencies is a way of exerting control over tests by providing the capacity to isolate components under test from their dependencies. As all frameworks work with multiple components, which can range from APIs to services and even interactions such as clicks or hovers, it is important to be able to isolate these components to test for their durability, behavior, and reliability. Mocking dependencies allow users to create a controlled testing environment to verify the component's behavior in isolation.

Heroku for ChatOps: Start and Monitor Deployments From Slack

Aggregated on: 2024-06-06 17:35:57

In our last two articles, we explored how to configure CI/CD for Heroku using Heroku pipelines. When viewing a pipeline within the Heroku dashboard, you can easily start a deployment or promote your code from one environment to the next with the click of a button. From the dashboard, you can monitor the deployment and view its progress. This all works really well, assuming that you have Heroku open in your browser. But, what if you wanted to do it all from Slack?

How to Effortlessly Host Your Angular Website on GitHub Pages

Aggregated on: 2024-06-06 16:35:57

Angular is a leading framework for building web applications and a large number of companies use it in their enterprise software building. This makes the ability to showcase your Angular application quite lucrative. It is not always straightforward though to showcase your Angular skills to others. Maybe you work for an application that is internal to your company only, or it is behind a pay subscription or you are an aspiring student who has not yet worked on a real-world application. It can be very costly to host your sample web application on online cloud hosting platforms or can become hard to maintain if you decide to host it on your local machine. GitHub pages offer a very cost-effective way to host, and with Angular’s GitHub page integration, it becomes a breeze to host your app.

The Era of Enterprise AI Has Arrived: Insights From Snowflake Summit 2024 Day 1

Aggregated on: 2024-06-06 15:50:57

This week, thousands of data and AI professionals gathered in San Francisco for Snowflake Summit 2024, the largest event focused on eliminating complexity and making AI accessible to everyone in the organization. The overarching message was clear — welcome to the age of enterprise AI. Snowflake CEO Sridhar Ramaswamy kicked off the event by highlighting the company's continued growth and innovation. Snowflake now has 9,800 customers, processes 5 billion queries daily, and its largest customer table contains 200 trillion rows. Over 50% of customers are using Snowpark to bring more extensibility to the platform.

.NET 9 and C# 13: New Features and Improvements

Aggregated on: 2024-06-06 14:50:57

The development of the .NET platform and C# language moves forward with the launch of .NET 9 and C# 13, introducing a range of enhancements and advancements to boost developer efficiency, speed, and safety. This article delves into upgrades and new features in these releases giving developers a detailed look. Figure courtesy of Microsoft

Streamlining Data Integration

Aggregated on: 2024-06-06 13:50:57

Integrating data from multiple sources like Salesforce and Oracle into Amazon Redshift is crucial for organizations looking to centralize their analytics. This article demonstrates how to connect to Salesforce and Oracle, extract data using SOQL and SQL queries, load it into Redshift staging tables, and perform transformations using Redshift stored procedures, all orchestrated through Python scripts. Prerequisites Salesforce: Access to Salesforce with the necessary API permissions. Oracle: Access to an Oracle database with the necessary query permissions. Amazon Redshift: An existing Redshift cluster. Python: Installed with the necessary libraries (simple_salesforce, cx_Oracle, boto3, psycopg2). Connecting to Salesforce and Extracting Data First, let's connect to Salesforce and extract data using SOQL.

Software Design Patterns and Principles

Aggregated on: 2024-06-05 23:50:57

Design patterns are a set of templates formulated over a period of time by software architects. They are best practices that software programmers can apply to their own software/application development. They provide incredible advantages by saving time and effort by reusing pre-existing solutions while improving the quality and consistency of the code. It can also help enhance communication and collaboration with other developers, facilitate the adaptation and evolution of your code, and prevent common pitfalls and errors. All in all, design patterns are a great way to streamline your programming process. Broadly speaking there are three kinds of patterns: structural, creational, and behavioral.

Best Practices for Batch Processing in IBM App Connect Enterprise as a Service

Aggregated on: 2024-06-05 22:50:57

Batch processing is a capability of App Connect that facilitates the extraction and processing of large amounts of data. Sometimes referred to as data copy, batch processing allows you to author and run flows that retrieve batches of records from a source, manipulate the records, and then load them into a target system. This post provides recommendations for designing flows that use batch processing. It also includes a few pointers on how to troubleshoot any issues that you might see, and in particular which log messages to look out for. Here's some more information about batch processing in App Connect:

Dependency Injection

Aggregated on: 2024-06-05 21:50:57

Dependency Injection is one of the foundational techniques in Java backend development, helping build resilient and scalable applications tailored to modern software demands. DI is used to simplify dependency management by externalizing dependencies from the class itself, streamlining code maintenance, fostering modularity, and enhancing testability. Why is this technique crucial for Java developers? How does it effectively address common pain points? In this article, I present to you the practical benefits, essential practices, and real-world applications of Dependency Injection. Let's explore the practical strategies that underlie Dependency Injection in Java backend applications.

Mock The API Data (Request and Responses) With Playwright

Aggregated on: 2024-06-05 20:50:57

Mocking API responses refers to the practice of creating simulated responses from an API without actually interacting with the real API. This technique is commonly used in software development, especially during testing phases, to mimic the behavior of real API endpoints. By using mock responses, developers can isolate specific parts of their code for testing purposes without relying on external services, thus enabling more efficient and controlled testing environments. There are various tools and libraries available for mocking API responses in different programming languages and frameworks. Mocking API responses with Playwright is a useful technique for testing your web applications without relying on real API servers. It allows you to simulate different scenarios and responses from your APIs, making your tests more robust and independent of external services.

Beyond A/B Testing: How Multi-Armed Bandits Can Scale Complex Experimentation in Enterprise

Aggregated on: 2024-06-05 19:05:57

A/B testing has long been the cornerstone of experimentation in the software and machine learning domains. By comparing two versions of a webpage, application, feature, or algorithm, businesses can determine which version performs better based on predefined metrics of interest. However, as the complexity of business problems or experimentation grows, A/B testing can be a constraint in empirically evaluating successful development. Multi-armed bandits (MAB) is a powerful alternative that can scale complex experimentation in enterprises by dynamically balancing exploration and exploitation. The Limitations of A/B Testing While A/B testing is effective for simple experiments, it has several limitations:

How To Build a Shopping Cart AI Chatbot

Aggregated on: 2024-06-05 18:05:57

In our last article, we demonstrated our new AI functions. By building on top of these, we can build any amount of complexity into our AI chatbots — including a shopping cart AI chatbot, as illustrated in this article. If you want to try the AI chatbot we're building in this article you can find it below. Spy Gear Shopping Cart AI Chatbot

Add Flexibility to Your RAG Applications in Amazon Bedrock

Aggregated on: 2024-06-05 17:05:57

Knowledge Bases for Amazon Bedrock is a fully managed capability that helps you implement the entire RAG workflow from ingestion to retrieval and prompt augmentation without having to build custom integrations to data sources and manage data flows. There are several configurations you can tweak to customize retrieval and response generation. This is done via query configuration parameters which can be applied via the console, API, or the SDK.

Take Your First Steps for Building on LLMs With Google Gemini

Aggregated on: 2024-06-05 16:20:57

It seems like there are endless possibilities for innovation with LLMs. If you’re like me, you’ve used GenAI applications and tools—like ChatGPT built into Expedia, Copilot for code writing, or even DALL-E for generating images. But as a technologist, I want to do more than just use LLM-based tools. I want to build my own. I told DALL-E to "Generate a watercolor of a computer programmer thinking about all the things he could build with LLMs." Yeah, this is pretty spot-on.

How To Perform Data Migration in MongoDB Using Node.js

Aggregated on: 2024-06-05 15:20:57

Data migration in MongoDB refers to the process of transferring data from one database to another. This could involve moving data between different MongoDB instances, upgrading to a newer version of MongoDB, or restructuring the database schema. Data migration is crucial for maintaining database integrity and scalability. It ensures that data remains accessible and usable as the database evolves. Without proper migration processes in place, data may become fragmented, outdated, or inaccessible, leading to issues with data quality and system performance. Therefore, understanding how to perform data migration effectively is essential for developers working with MongoDB.

Optimizing Cloud Spend in the New Era: Strategies and Frameworks for Cost Management

Aggregated on: 2024-06-05 14:20:56

In today's cloud computing landscape, businesses are embracing the dynamic world of hybrid and multi-cloud environments and seamlessly integrating infrastructure and services from multiple cloud vendors. This shift from a single provider is driven by the need for greater flexibility, redundancy, and the freedom to leverage the best features from each provider and create tailored solutions. Furthermore, the rise of cloud-native technologies is reshaping how we interact with the cloud. Containerization, serverless, artificial intelligence (AI), and edge computing are pushing the boundaries of what's possible, unlocking a new era of innovation and efficiency. But with these newfound solutions comes a new responsibility: cost optimization. The complexities of hybrid and multi-cloud environments, coupled with the dynamic nature of cloud-native deployments, require a strategic approach to managing cloud costs.

Protecting PII Data With JWT

Aggregated on: 2024-06-05 13:20:56

The Challenge JWT tokens are widely used for securing APIs through authentication and authorization. When an API request arrives, the resource server decodes and verifies the JWT token, typically validating the signature for authentication and checking claims or scopes for authorization. For example, the server might use claims in the token to decide if the user can access a particular endpoint. However, finer access control is often needed. For instance, when a request fetches a customer's bank account details, the server must ensure the user is accessing their own account, not someone else's.

Telemetry Pipelines Workshop: Avoiding Telemetry Data Loss With Fluent Bit

Aggregated on: 2024-06-04 23:50:56

Are you ready to get started with cloud-native observability with telemetry pipelines? This article is part of a series exploring a workshop guiding you through the open source project Fluent Bit, what it is, a basic installation, and setting up the first telemetry pipeline project. Learn how to manage your cloud-native data from source to destination using the telemetry pipeline phases covering collection, aggregation, transformation, and forwarding from any source to any destination.

Rookie Mistakes Scrum Masters Make

Aggregated on: 2024-06-04 22:50:56

TL; DR: Top Five Rookie Mistakes by Self-Proclaimed Scrum Masters Are you struggling with imposter syndrome as a new Scrum Master? Avoid five common rookie mistakes Scrum Masters make. Instead, discover how to set clear Sprint Goals, build trust, balance metrics, and empower your team to make independent decisions. Don’t let early missteps define your journey. Learn from these mistakes and transform them into stepping stones towards mastery. By understanding and addressing these pitfalls, you’ll gain confidence, enhance your leadership skills, and truly embody the principles of Scrum.

I Built a Reusable Dashboard for Read the Docs Traffic Analytics Using Vizro-AI

Aggregated on: 2024-06-04 21:50:56

In this article, I'll explain how I built a dashboard to visualize the traffic data for some documentation I maintain as a technical writer. I have few design skills and limited Python experience, so I needed a simple, low-code approach to show the impact and usage of the documentation I maintain. This turned out to be an open-source solution: Vizro as a template for a low-code dashboard, and Vizro-AI to build individual charts with generative AI. TL;DR? If you want to jump right in, you can find the Jupyter Notebook code for the dashboard in my GitHub repo.

LLMs Get a Memory Boost with HippoRAG

Aggregated on: 2024-06-04 20:50:56

Large Language Models (LLMs) have quickly proven themselves to be invaluable tools for thinking. Trained on massive datasets of text, code, and other media, they can generate human-quality writing, translate languages, generate images, answer your questions in an informative way, and even write different kinds of creative content. But for all their brilliance, even the most advanced LLMs have a fundamental constraint: their knowledge is frozen in time. Everything they "know" is determined by the data they were trained on, leaving them unable to adapt to new information or learn about your specific needs and preferences. To address this limitation, researchers developed Retrieval-Augmented Generation (RAG). RAG allows LLMs access to access datastores that can be updated in real-time. This access to dynamic external knowledge bases, allows them to retrieve relevant information on the fly and incorporate it into their responses. Since they tend to rely on keyword matching, however, standard RAG implementations struggle when a question requires connecting information across multiple sources — a challenge known as "multi-hop" reasoning.

Efficiently Transforming JDBC Query Results to JSON

Aggregated on: 2024-06-04 20:05:56

A lot of enterprise data is stored in relational databases and accessed via SQL queries. Many web services are little more than HTTP-based wrappers around such queries. For example, the following service method (built using the open-source Kilo framework) retrieves all rows from an employees table and returns the results to the caller as JSON:

How to Quickly Learn Coding and Land a Job in Tech

Aggregated on: 2024-06-04 18:05:56

From the day I wrote my first Hello World program, it took me two years to land a job at Amazon and another two years to get into Google. That’s because I accomplished this without having a Computer Science degree or attending a boot camp. I made countless mistakes along the way which made my path to becoming a Software Engineer longer than it should have been. I spent countless hours watching YouTube tutorials and paid for numerous Udemy courses, only to find that they added no real value. If I could go back in time and undo all the things that didn't work, I would be in the exact same situation as today within six months of starting programming. That’s exactly why I am writing this helpful piece. Today, I'll cut out all the unnecessary fluff and provide you with the quickest route from beginner to full-time Software Engineer.

Security Considerations When Building SaaS

Aggregated on: 2024-06-04 18:05:56

There is no well-defined pattern or framework for building a managed SaaS. Building a well-managed SaaS involves solving a wide variety of problems. Some of these problems are common across different kinds of SaaS, and some are very specific to the SaaS. Examples of common problems are billing, analytics, metrics, and onboarding. Examples of problems that are different for every SaaS are provisioning (what resources should be provisioned when a new tenant onboards), data isolation, scaling, and performance needs. For the sake of this article, we will classify common problems as control plane problems, and different problems as data plane problems. This article briefly talks about the different models of providing tenant isolation and then delves into the security considerations of building a SaaS.

Python in Finance: A Developer’s Guide

Aggregated on: 2024-06-04 17:05:56

Introduction to Python in Finance Python has ascended as a formidable force within the financial sector, transforming the operational landscape and fostering innovation among financial entities. For software engineers, mastering Python’s utility in finance can unlock opportunities to develop advanced financial applications and instruments. Why Opt for Python in Finance? Simplicity and Comprehensibility Python's straightforward and lucid syntax empowers developers to craft intricate financial algorithms and models with ease. This clarity shortens the development lifecycle and curtails errors, which is indispensable in the high-stakes financial domain.

What Is Reverse ETL? Overview, Use Cases, and Key Benefits

Aggregated on: 2024-06-04 17:05:56

In the evolving landscape of data engineering, reverse ETL has emerged as a pivotal process for businesses aiming to leverage their data warehouses and other data platforms beyond traditional analytics. Reverse ETL, or “Extract, Transform, Load” in reverse, is the process of moving data from a centralized data warehouse or data lake to operational systems and applications within your data pipeline. This enables businesses to operationalize their analytics, making data actionable by feeding it back into the daily workflows and systems that need it most. How Does Reverse ETL Work? Reverse ETL can be visualized as a cycle that begins with data aggregated in a data warehouse. The data is then extracted, transformed (to fit the operational systems' requirements), and finally loaded into various business applications such as a CRM, marketing platforms, or other customer support tools. These concepts can be further explored in this resource on the key components of a data pipeline.