dozer VS PostgreSQL

https://github.com/getdozer/dozer/issues/1631#issuecomment-1...

and then something has gone off the rails about the accounting process since

  Trigger.dev

Hi HN,

I'm Matteo and co-founder of Dozer (https://github.com/getdozer/dozer), a backend tool for building data products. Over the past few months we have done good progress with the product development but our documentation needs a lot of work. Myself, my co-founder and the entire team are juggling among many activities, and one of them is producing good documentation, and that is the part where we are bit struggling.

We have tried to hire a couple of tech writers but it's really not working out as we wanted. Our product is a dev product, and it is very hard to find tech writers who have that depth to think what a developer need. At the same time, asking the dev team to write documentation produces low quality docs (obviously devs prefer to write code and they just to go back to that as fast they can).

The result is that I end up writing most of documentation or refactoring the entire content produced by tech writer. But this approach will not scale.

How do you guys solved it? I'm curios to know about the approach of other startups.

Thanks

This blog post explores how to enhance these aspects by implementing a low-latency cache using Dozer and PostgreSQL. We delve into the importance of caching in data management, the limitations of not having a cache, and the unique advantages of using Dozer as a cache layer. We also provide a guide on how to use Dozer for real-time data ingestion from PostgreSQL. This post is a must-read for developers seeking to improve their application's performance and responsiveness, and for those looking to simplify their data infrastructure.

I recently penned a blog post on an interesting topic - leveraging Dozer with PostgreSQL to create a high-speed, low-latency cache. In case you're not familiar with Dozer, it's an open-source data API backend built entirely in Rust, known for its fast processing and lower resource usage.

some time ago I shared about our OSS project Dozer with the group. We just did a presentation and demo at the Python Meetup group here in Singapore and just wanted to share the full video here.

Hey HN,

As one of the co-founders of Dozer (https://github.com/getdozer/dozer), I stumbled upon this awesome article from the Temporal folks (https://temporal.io/blog/saga-pattern-made-easy) and got super excited about the potential synergy between Dozer and Temporal.

While Temporal nails the orchestration of microservices, I couldn't help but notice the challenge of consolidating data across services. In the article, they talk about three services: flight booking, hotel booking, and experience booking. But imagine trying to create a complete trip view by stitching together data from all three services - it can be quite a headache!

And that's where Dozer swoops in to save the day. With Dozer, I can tap into all the change data capture (CDC) events from each microservice, merge the data in real-time, stash it in a cache, and make it available through APIs. The best part? Since Dozer operates in real-time, my cached data is always fresh. Boom! Now I can simply leverage Dozer's APIs to access a comprehensive trip view.

Raise your hand if you've faced this challenge before! Let's hear your thoughts.

👋🏼 Are you looking for a platform that simplifies the process of building and deploying data-driven applications? Look no further than Dozer! 💻 is a powerful Data API backend that simplifies the process of building and deploying data-driven applications & APIs for real-time data processing and analysis, predictive analytics, data caching, and much more. Dozer is a 100% open-source project.

Star our repository and get a chance to win an amazing Swagpack!!🎁🎉 We will announce the winners in our Discord server. So, don’t forget to join.🤗

You can consider using Dozer (https://github.com/getdozer/dozer), an open-source data API backend. Dozer has a PostgreSQL connector that you can use to monitor any changes in your database schema and data. Although Dozer is not specifically designed for ETL tasks, you can still use it for this purpose by using Lambda functions. Dozer provides a Lambda runtime to execute user-defined functions. So you can use PostgreSQL connector ( to capture CDC) with Lambda runtime to load data to snowflake.

Enter Dozer, it simplifies the process of creating low-latency data APIs (gRPC and REST) from any data source easily with OpenAPI specification. Dozer is a real-time data engineering tool built with Rust, which enables seamless integration of data streams(sources), efficient data processing, and lightning-fast response times. This allows developers to focus on building applications without worrying about the complexity of managing real-time data.

Data Handling: Utilizes Windmill for data pipelines, with a primary database powered by PostgreSQL. Auxiliary data storage is handled by MongoDB, with Redis for caching to optimize performance

PostgreSQL

The LMS Moodle Operator serves as a meta-operator, orchestrating the deployment and management of Moodle instances in Kubernetes. It handles the entire stack required to run Moodle, including components like Postgres, Keydb, NFS-Ganesha, and Moodle itself. Each of these components has its own Kubernetes Operator, ensuring seamless integration and management.

Another key feature of txtai is being able to quickly move from prototyping to production. This article will demonstrate how txtai can integrate with Postgres, a powerful, production-ready and open source object-relational database system. After txtai persists content to Postgres, we'll show it can be directly queried with SQL from any Postgres client

SQL (Structured Query Language) databases are relational databases. They organize data into tables with rows and columns, and they use SQL for querying and managing data. Examples include MySQL, PostgreSQL, and SQLite.

Node.js, MySQL and PostgreSQL servers installed on your machine

Each app’s front end is built with Qwik and uses Tailwind for styling. The server-side is powered by Qwik City (Qwik’s official meta-framework) and runs on Node.js hosted on a shared Linode VPS. The apps also use PM2 for process management and Caddy as a reverse proxy and SSL provisioner. The data is stored in a PostgreSQL database that also runs on a shared Linode VPS. The apps interact with the database using Drizzle, an Object-Relational Mapper (ORM) for JavaScript. The entire infrastructure for both apps is managed with Terraform using the Terraform Linode provider, which was new to me, but made provisioning and destroying infrastructure really fast and easy (once I learned how it all worked).

I've used MySQL for years. But recently, I found myself working PostgreSQL and simple things like dumping and restoring a database are different enough that I decided to document the process. It's straightforward enough once I knew how.

A running Rails application needs a database to connect to. You may already have your database of choice installed, but if not, I recommend PostgreSQL, or Postgres for short. On a Mac, probably the easiest way to install it is with Posrgres.app. Another option, the one I prefer, is to use Homebrew. With Homebrew installed, this command will install PostgreSQL version 16 along with libpq: