Apache Cassandra VS foundation

Apache Cassandra{:target="_blank"} is a highly scalable and fault-tolerant database that can handle large volumes of data across multiple nodes or clusters. It provides fast read and write operations, making it suitable for real-time analytics or applications with high throughput requirements.

Wide Column: Apache Cassandra, ScyllaDB and DynamoDB

Cassandra, a replicated, fault-tolerant, decentralized and scalable database system.

A popular database you might already be familiar with is Apache Cassandra®, which powers high-performing applications for thousands of companies including Hulu, Netflix, Spotify, and Apple. While this free, open-source database is known for its high availability, scalability, and resilience; the downside is that it’s also notoriously complex to set up and manage.

In our previous article, we looked at augmenting our dashboard to store and display individual events from the Avengers using DataStax Astra, a serverless DBaaS powered by Apache Cassandra using Stargate to offer additional APIs for working with it.

Example: Apache Cassandra, CouchDB.

For our TikTok database, we’re using DataStax Astra DB: a cloud-based database that fully manages Apache Cassandra®, one of the most robust and scalable NoSQL databases around.

Data partitioning in Apache Cassandra.

GraphQL is a query language and runtime for APIs. It provides a flexible and efficient way for clients to request and retrieve specific data from a server using a single API endpoint.

When you use technologies like GraphQL, it is trivial to derive TypeScript types. A GraphQL API is created by implementing a schema. Generating the TypeScript type definitions from this schema is simple, and you do not have to do any more work than just making the GraphQL API. This is one reason why I like GraphQL so much.

REST and GraphQL have advantages, drawbacks, and use cases for different environments. REST is for simple logic and a more structured architecture, while GraphQL is for a more tailored response and flexible request.

A Gatsby site uses Gatsby, which leverages React and GraphQL to create fast and optimized web experiences. Gatsby is often used for building static websites, progressive web apps (PWAs), and even full-blown dynamic web applications.

In my usual NodeJS tech stack, which includes GraphQL, NestJS, SQL (predominantly PostgreSQL with MikroORM), I encountered these limitations. To overcome them, I've developed a new stack utilizing Rust, which still offers some ease of development:

At Woovi we are GraphQL lovers, hence we develop many helpers around this tool to bring a good developer experience.

A query language for APIs that allows clients to request only the data they need. It provides a more flexible and efficient alternative to RESTful APIs. GraphQL provides a single endpoint for multiple data sources, making it efficient for clients to retrieve only the necessary information forexample if a Node.js microservice uses GraphQL, a client can send a query to request specific data, and the GraphQL service will fetch the required information from the underlying data sources and respond accordingly.

This approach is particularly beneficial for teams looking to enhance their digital presence without the need for extensive coding or software development. By leveraging the GraphQL API endpoint, developers can prepare and execute queries to retrieve the desired content. This makes the Headless Channel a valuable tool 🔨 for content managers and developers, offering ease of use and practicality.

To know about Graphql and why it's being used head over to for more details, Now let's cut to the chase.

GraphQL is an excellent choice for real-time data synchronization in React Native apps. By using GraphQL, you can retrieve precisely the data you need and receive updates in real-time, reducing network usage and providing a seamless user experience.