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Create a Full-Stack App Using Nuxt.js, NestJS, and DataStax Astra DB (With a Little Help From GitHub Copilot)

Building a full-stack application can be daunting because you have to not only think about how the frontend will display the data but where the data will come from and where it’s stored. However, it’s not as hard as you might think to get the basics of a full-stack application up and running.

If you want to create a full-stack application, complete with dynamic data retrieved from a cloud database by an API, then watch the tutorial below, created by Eddie Jaoude. In his tutorial, Eddie shows you how to do it in less than an hour using Nuxt.js with VuetifyJS for the frontend, NestJS to create RESTful APIs, and DataStax’s Astra DB for the cloud database service. Also, you’ll use GitHub Copilot as your AI-powered pair programmer.

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Python, NoSQL and FastAPI Tutorial: Web Scraping on a Schedule

Web Scraping image

Can there be other use cases for Cassandra beyond messaging and chat? In this tutorial, we show you how to web scrape on a schedule by integrating the Python framework called FastAPI with Astra DB, a serverless, managed Database-as-a-Service built on Cassandra.

Recently, I caught up with the Pythonic YouTuber Justin Mitchell from CodingEntrepreneurs and we discussed how today’s apps are tackling global markets and issues. He pointed out that Discord stores 120 million messages with only four backend engineers—and that was back in 2017.

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Why a Serverless Data API Might be Your Next Database

App development stacks have been improving so rapidly and effectively that today there are a number of easy, straightforward paths to push code to production, on the cloud platform of your choice. But what use are applications without the data that users interact with? Persistent data is such an indispensable piece of the IT puzzle that it’s perhaps the reason the other pieces even exist. 

Enter cloud and internet scale requirements, essentially mandating that back-end services must be independently scalable / modular subsystems to succeed. Traditionally, this requirement has been difficult in the extreme for stateful systems. No doubt, database as-a-service (DBaaS) has made provisioning, operations, and security easier. But as anyone who has tried to run databases on Kubernetes will tell you: auto scaling databases, especially ones that are easy for developers to use, remain out of reach for mere mortals.

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Fast JMS for Apache Pulsar: Modernize and Reduce Costs with Blazing Performance

Written by: Chris Bartholomew

DataStax recently announced the availability of Fast JMS for Apache Pulsar, a JMS 2.0 API. By combining the industry-standard Java Messaging Service (JMS) API with the cloud-native and horizontally scalable Apache Pulsar™ streaming platform, DataStax is providing a powerful way to modernize your JMS infrastructure, improve performance, and reduce costs. Fast JMS is open source and is included in DataStax’s Luna Streaming Enterprise support of Apache Pulsar.

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Best Practices for Data Pipeline Error Handling in Apache NiFi

According to a McKinsey report, ”the best analytics are worth nothing with bad data”. We as data engineers and developers know this simply as "garbage in, garbage out". Today, with the success of the cloud, data sources are many and varied. Data pipelines help us to consolidate data from these different sources and work on it. However, we must ensure that the data used is of good quality. As data engineers, we mold data into the right shape, size, and type with high attention to detail. 

Fortunately, we have tools such as Apache NiFi, which allow us to design and manage our data pipelines, reducing the amount of custom programming and increasing overall efficiency. Yet, when it comes to creating them, a key and often neglected aspect is minimizing potential errors.

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Build a TikTok Clone With a Twist

It is a really great time to be a developer. 

We have tons of APIs integrated within great tools for building dynamic, full stack apps. If you are a developer, you probably are using technologies like schemaless data stores, serverless architectures, JSON APIs, and/or the GraphQL language.