OpenEBS’ Post

Managing OpenTelemetry Node.js libraries just got easier! Our latest blog post explores the use of environment variables to include or exclude specific OpenTelemetry libraries in your Node.js projects. This approach simplifies your setup, reduces configuration overhead, and ensures that only the necessary libraries are loaded, optimizing your application's performance. Learn how to streamline your OpenTelemetry configuration and make your observability stack more efficient. https://lnkd.in/dF-vhEQY #OpenTelemetry #NodeJS #EnvironmentVariables #Tracetest #DevOps #PerformanceOptimization

🚀 Just published a new blog post on Medium: "A Beginner’s Guide to GraphQL using Node.js"! 🚀 Learn how to create a powerful and flexible GraphQL API from scratch using Apollo Server and Node.js. Perfect for both beginners and experienced developers looking to enhance their API development skills. Check it out and start building scalable APIs today! 💻🔗 #GraphQL #ApolloServer #NodeJS #WebDevelopment #APIs #Programming #Tech

For Elixir newbie, it's so hard to understand how GenServer work, how to use it in right ways (use cases). I write a topic about GenServer and hope it can help people. link: https://lnkd.in/gn8ranNS

I recommend this article to anyone interested in APIs.

I'm about to be working with labstack/echo again, so I thought I would port over my customizable gin-pagination library into a similar one for echo. It has a slightly larger API as echo doesn't have as an extensive an API itself, but very comprable in terms of functionality. Have a look at https://lnkd.in/eeF9wUAa #go #softwareengineer #coding #softwareengineering #golang #opensource #api #framework

Dev staff monday! 😍 My project Implements real-time WebSocket communication with Binance to receive BTC/USDT order book updates, process the data to calculate mid-price, fit the Log-Periodic Power Law Singularity (LPPLS) model every second, and broadcast predictions and real-time data to connected WebSocket clients. The system also simulates virtual trades based on the LPPLS predictions. ---- And if you are tired 🤙 Why Go Kicks Rust's Ass for Your Real-Time BTC/USDT Order Book Analysis Project with LPPLS Model. You're working on a real-time BTC/USDT order book analysis using the LPPLS model, and you're stuck choosing between Go and Rust. Well, here’s why Go is your best bet: Concurrency on Steroids: Go’s goroutines are lightweight, easy to use, and perfect for real-time data streams. You're going to be drowning in order book updates, and Go handles that shit smoothly without breaking a sweat. Rust’s concurrency is powerful but way more complicated. You don’t need to be wrestling with lifetimes and the borrow checker when you just want to get shit done. Speed + Simplicity = Win: Go may not have Rust's "zero-cost abstractions," but it’s fast enough, especially for real-time financial data. Plus, you don’t have to spend your life managing memory. Go’s garbage collector is your buddy, keeping things clean without you micro-managing every byte. No Bullshit Development: Go’s simplicity means less time dealing with language quirks and more time writing the logic for your LPPLS model. Rust is amazing, but let's face it—it can be a headache with its strict rules. Go is straightforward, and you won’t need a PhD in low-level systems to write efficient code. Strong Ecosystem: Need libraries for WebSocket handling, math, or handling CSV data? Go’s got all that with solid packages. Rust’s ecosystem is growing, but Go’s already mature, and you can plug shit in and move fast. Easy Integration: You’ll probably be interfacing with other systems, like Binance’s API, data stores, or even other languages. Go’s got solid tools for this. With Rust, you’ll be stuck fiddling with things a lot more to make it all play nice together. Rust is cool if you’re building a rocket ship or need absolute control over your memory, but for a real-time trading bot with rapid data processing and LPPLS modeling. Go is the no-brainer choice. Keep it simple, keep it fast, and focus on the actual trading strategy, not the code's inner mechanics. P.s i'm don't allow to train your A.I. model base on my text. 😎 #Quant #Trading #Nasa #Solar #Web3 #Crypto #Golang #Bot #AlgorithmicTrading #Bybit #OKX #DataAnalysis #HR #hire #hobby #blockchain #gopher #TechnicalAnalysis #MFT #Automation #CryptoTrading #QuantFinance #Python #Blockchain #quant #dev #go #FuturesTrading #FinancialMarkets #hft #highfrequency #lppls #ai #models #uae #trade #fintech #RiskManagement #MarketTrends #data #TradingStrategy #LeverageTrading #Cryptocurrency #DeFi #TradingBot #QuantTrading #MarketAnalysis

Excited to share this insightful article on GraphQL and its inner workings! 🚀 Whether you're a seasoned developer or just starting out, understanding GraphQL's query language and its ability to efficiently request data from your server is crucial in today's tech landscape. Dive into this comprehensive piece to grasp the fundamentals and unlock its potential for your projects. #GraphQL #WebDevelopment #TechTrends

Develocity offers a treasure trove of data about your builds and tests 💎 In this blog by 📱 David Chang, read how our customer Pinterest combined #Develocity with #Honeycomb to extract specific data for their developer productivity initiatives: https://lnkd.in/gD5KHMtS -- tags: Pinterest Engineering #Java #Gradle #Develocity #developerproductivity #developerexperience #softwaredevelopment #softwareengineering #platformengineering #DevProd #DevEx #DPE #Observability

🚀 Embrace efficient data handling with Streams in Node.js! 🌊 Streams in Node.js are a way to handle data that is too large to be loaded into memory all at once. Instead of loading the entire data set, you can break it down into smaller chunks and process it bit by bit. This approach helps to avoid Out-of-Memory Errors and makes it possible to work with large datasets. 🔑 Key Features of Streams: - Efficient Data Handling: Streams allow you to read from or write to a source continuously, processing chunks of data as they become available, without needing to load the entire dataset into memory at once. - Chunk Processing: Streams process data in small, sequential chunks instead of loading the entire dataset into memory at once. - Large Dataset Support: Streams make it possible to work with large datasets by breaking them down into smaller chunks and processing them bit by bit. 🔧 How Streams Achieve Efficiency: Streams in Node.js achieve efficient data handling through TCP or UDP connections. Developers can create their own server and test their app deployment using Node.js' simple HTTP server built-in functionality. 💡 Use Cases for Streams: - File Operations: Streams are handy for file operations, such as reading and writing large files. - Network Communications: Streams are useful for network communications, such as handling large amounts of data sent over the network. - End-to-End Data Exchange: Streams enable efficient end-to-end data exchange by processing data in small, sequential chunks. Level up your Node.js skills with Streams and unlock new possibilities in data management. Dive deeper into this powerful feature today! 🌟 #NodeJS #DataHandling #DeveloperTools #StreamProcessing #TechInnovation #JavaScript #Coding #BackendDevelopment #FileOperations

𝐑𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐞 𝐲𝐨𝐮𝐫 𝐌𝐄𝐑𝐍 𝐬𝐭𝐚𝐜𝐤 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐰𝐢𝐭𝐡 𝐆𝐫𝐚𝐩𝐡𝐐𝐋! 🚀 Learn how to streamline data fetching, enhance performance, and build more efficient applications. Discover the power of precise data querying and optimized responses. #GraphQL #MERNstack #webdevelopment #developer #coding #tech #data #api [ GraphQL, MERNstack, webdevelopment, developer, coding, tech, data, api, efficiency ]