materialize VS axios

[2] https://materialize.com/

To fully leverage the data is the new oil concept, companies require a special database designed to manage vast amounts of data instantly. This need has led to different database forms, including NoSQL databases, vector databases, time-series databases, graph databases, in-memory databases, and in-memory data grids. Recent years have seen the rise of cloud-based streaming databases such as RisingWave, Materialize, DeltaStream, and TimePlus. While they each have distinct commercial and technical approaches, their overarching goal remains consistent: to offer users cloud-based streaming database services.

> Materialize no longer provide the latest code as an open-source software that you can download and try. It turned from a single binary design to cloud-only micro-service

Materialize CTO here. Just wanted to clarify that Materialize has always been source available, not OSS. Since our initial release in 2020, we've been licensed under the Business Source License (BSL), like MariaDB and CockroachDB. Under the BSL, each release does eventually transition to Apache 2.0, four years after its initial release.

Our core codebase is absolutely still publicly available on GitHub [0], and our developer guide for building and running Materialize on your own machine is still public [1].

It is true that we substantially rearchitected Materialize in 2022 to be more "cloud-native". Our new cloud offering offers horizontal scalability and fault tolerance—our two most requested features in the single-binary days. I wouldn't call the new architecture a microservices design though! There are only 2-3 services, each quite substantial, in the new architecture (loosely: a compute service, an orchestration service, and, soon, a load balancing service).

We do push folks to sign up for a free trial of our hosted cloud offering [2] these days, rather than trying to start off by running things locally, as we generally want folks' first impression of Materialize to be of the version that we support for production use cases. A all-in-one single machine Docker image does still exist, if you know where to look, but it's very much use-at-your-own-risk, and we don't recommend using it for anything serious, but it's there to support e.g. academic work that wants to evaluate Materialize's capabilities to incrementally maintain recursive SQL queries.

If folks have questions about Materialize, we've got a lively community Slack [3] where you can connect directly with our product and engineering teams.

[0]: https://github.com/MaterializeInc/materialize/tree/main

Some recent solutions to this problem include Differential Dataflow and Materialize. It would be neat if postgres adopted something similar for live-updating materialized views.

https://github.com/timelydataflow/differential-dataflow

https://materialize.com/

Materialize | Full-Time | NYC Office or Remote | https://materialize.com

Materialize is an Operational Data Warehouse: A cloud data warehouse with streaming internals, built for work that needs action on what’s happening right now. Keep the familiar SQL, keep the proven architecture of cloud warehouses but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date.

Materialize is the operational data warehouse built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable — all in a familiar SQL UI.

Senior/Staff Product Manager - https://grnh.se/69754ebf4us

Senior Frontend Engineer - https://grnh.se/7010bdb64us

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Investors include Redpoint, Lightspeed and Kleiner Perkins.

Materialize | EM (Compute), Senior PM | New York, New York | https://materialize.com/

You shouldn't have to throw away the database to build with fast-changing data. Keep the familiar SQL, keep the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date.

That is Materialize, the only true SQL streaming database built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable — all in a familiar SQL UI.

Engineering Manager, Compute - https://grnh.se/4e14099f4us

Senior Product Manager - https://grnh.se/587c36804us

VP of Marketing - https://grnh.se/9caac4b04us

How does it compare to https://materialize.com/ ?

axios: To fetch data from the Strapi CMS API and also to fetch responses from our Express server.

Note, you can use any library for HTTP requests like axios. This example uses the http and node-fetch libraries available on npm.

Interact with other APIs over an encrypted channel. For example, you can use Axios with Node.js.

For this article, I will be using the TypeScript Starter template as shown in the screenshot above. This comes with Nodejs, Cheerio, Axios

Axios is suitable for a wide range of web development projects, from simple single-page applications (SPAs) to complex, large-scale enterprise software.

Axios for streamlined API interactions, facilitating seamless data fetching.

Axios is a popular HTTP client library for Node.js that provides a more user-friendly and feature-rich way to make HTTP requests. Axios simplifies error handling and supports features like automatic JSON parsing and request/response interceptors, making it a great choice for many HTTP request scenarios.

For this tutorial, we’re going to scan the file for malware using Verisys Antivirus API, and so we’ll add a package to make it easier to make external HTTP requests. Popular choices include Axios and node-fetch - for this article, we’ll use node-fetch

In the dynamic realm of JavaScript and front-end development, selecting the appropriate tool for HTTP requests is critical. Axios and Fetch stand out as two leading contenders, each offering distinct features and benefits. This article delves into their differences and practical applications, providing a comprehensive comparison.

Once you start to handle all the corner-cases of the fetch, you will find that you don't want to repeat the boilerplate each time you call network, so you will write some wrapper around the fetch or use redaxios library from Jason Miller, which provides axios-like API on top of fetch so it weights only 800 bytes, which is nice. But then you might need the axios interceptors which redaxios do not implement and if your application upload files and you want to track the upload progress with ProgressEvent, the fetch does not support that, only XMLHttpRequest does, on which the original axios is based. And after you write all your custom wrappers around fetch and upload wrappers around XMLHttpRequest, you might reconcider the original statement, that axios library is obsoleted.