Apache Pulsar VS PostgreSQL

Ingest real-time data from Kafka, Pulsar, or CDC sources like Postgresand MySQL, with built-in support for Debezium.

Apache Pulsar: Pulsar is a distributed messaging platform developed under the Apache Foundation. Its notable features include extremely low latency, support for multi-tenancy, geo-replication across regions, and tiered storage capabilities.

From Apache, there's Pulsar, a distributed pub-sub messaging platform with a flexible messaging model, and an intuitive client API. The latest version brings enhanced Key_Shared subscription implementation, secure Docker runtime based on Alpine Linux and Java 21, rate limiting, enhanced client compatibility, and more. Check out the release notes to read more about all the changes since the last release.

Apache Pulsar is an open-source distributed messaging platform originally developed by Yahoo! It provides a highly scalable solution for messaging and stream processing with robust durability and fault tolerance.

Stream-processing platforms such as Apache Kafka, Apache Pulsar, or Redpanda are specifically engineered to foster event-driven communication in a distributed system and they can be a great choice for developing loosely coupled applications. Stream processing platforms analyze data in motion, offering near-zero latency advantages. For example, consider an alert system for monitoring factory equipment. If a machine's temperature exceeds a certain threshold, a streaming platform can instantly trigger an alert and engineers do timely maintenance.

Edit: Here's a good example of what I'm looking for: https://github.com/apache/pulsar. It is a full application that happens to be deployed (or deployable) with Terraform, and the configuration files are available.

In recent years, it has become apparent that almost no production system is complete without real-time data. This can also be observed through the rise of streaming platforms such as Apache Kafka, Apache Pulsar, Redpanda, and RabbitMQ.

It's colored black on the refreshed Apache Pulsar site. https://pulsar.apache.org/

Understanding a database starts with understanding its structure. For PostgreSQL users, one of the most effective ways to visualize and manage your schema is by using an Entity-Relationship Diagram (ERD). Either if you're working with a large legacy database or starting something new, an ER diagram shows how your tables are connected and how your data is organized.

PostgreSQL supports two ways to store structured data in a column: JSON and JSONB.

On PostgreSQL create a database. In this tutorial, we will name it imageboard.

PostgreSQL and Partitioning PostgreSQL versions 10 and after have advanced native partitioning, making it ideal for time-series data, logs, and historical datasets. For example, a fintech app partitioning by month to improve query speeds.

The following commit was added to the PostgreSQL master branch (REL_18_BETA1):

We got a database for free when we provisioned last time, but if we want to target another database, we can surely do that as the service at this time of writing supports transforming to flavors of Postgres as well.  For this we will outline how to wrangle an external database, via Amazon RDS, and connect it to the service.

This quick start guide gets you up and running with Quarkus on macOS, including necessary tools. You will build a basic database application using Quarkus, Java 17, PostgreSQL, and Hibernate ORM Panache. While Hibernate ORM is the standard, powerful Jakarta Persistence implementation capable of complex mappings, it doesn't always make the most common tasks trivial. Hibernate ORM with Panache is Quarkus's solution to this, focusing specifically on making your data entities and repositories simple, straightforward, and fun to write by reducing boilerplate code for common persistence operations.

Hey everyone! I recently got fed up with waiting minutes for simple analytics queries to finish, so I threw together a little demo to see how SingleStore, MySQL, and PostgreSQL stack up against each other on a 110 million‑row banking transactions dataset.

In this tutorial, you'll set up a complete CDC pipeline using Debezium (version 3.1) with Kafka Connect to capture changes from a PostgreSQL database. By the end, you'll have a working system that streams every insert, update, and delete operation from your database into Apache Kafka topics.