Author: Guy Shtub

In this hands-on lab from ScyllaDB University, you will learn how to use the ScyllaDB CDC source connector to push the row-level changes events in the tables of a ScyllaDB cluster to a Kafka server.

What Is ScyllaDB CDC?

To recap, Change Data Capture (CDC) is a feature that allows you to not only query the current state of a database’s table but also query the history of all changes made to the table. CDC is production-ready (GA) starting from ScyllaDB Enterprise 2021.1.1 and ScyllaDB Open Source 4.3.

Background

Mutants have emerged from the shadows and are wreaking havoc on the earth! Increased levels of malicious mutant behavior pose a threat to national security and the general public. Luckily, some mutants have teamed up with Government agencies to help protect people against the bad ones.

To better protect the citizens and understand more about the mutants, the Government enacted the Mutant Registration Act. As required by the act, each mutant must wear a small device that reports on their actions every second. Your mission, as a new member of Division 3, is to help the Government keep the Mutants under control by building a Mutant Monitoring System (MMS). The MMS will consist of a database that will contain a Mutant Catalog and Monitoring system. As the number of mutants is on the rise, Division 3 decided that we must use more applications to connect to the mutant catalog and decided to hire developers to create powerful applications that can monitor the mutants. 

Alternator is an open-source project that allows teams to build "run-anywhere" DynamoDB-compatible applications with ScyllaDB.  This means you can deploy wherever you want: on-premises or on any public cloud.

In this tutorial, we’ll start by introducing the project. Afterward, we’ll see a hands-on example of creating a one-node ScyllaDB cluster and performing some basic operations on it.

At ScyllaDB, we've been working hard to develop and improve the scylla-rust-driver. It’s an open-source ScyllaDB (and Apache Cassandra) driver for Rust, written in pure Rust with a fully async API using Tokio. You can read more regarding its benchmark results and how our developers solved a performance regression. 

In different benchmarks, the Rust driver proved more performant than other drivers, which gave us the idea of using it as a unified core for other drivers as well. 

To help people get started with ScyllaDB NoSQL, we published CarePet: a project that demonstrates a generic Internet of Things (IoT) use case. The application is written in Go and allows tracking of pets’ health indicators. It consists of three parts:

• A collar that reads and pushes sensors data
• A web app for reading and analyzing the pets’ data
• A database migration tool

In this article, I will cover the main points. Check out the GitHub repo with the full code. You can find more extensive lessons for this use case in these guides.

ScyllaDB, an open-source database for data-intensive applications that require high performance and low latency, is an excellent match for Rust. Similar to the Rust programming language and the Tokio framework, ScyllaDB is built on an asynchronous, non-blocking runtime that works well for building highly-reliable low-latency distributed applications.

The ScyllaDB team has developed the scylla-rust-driver, an open-source ScyllaDB (and Apache Cassandra) driver for Rust. It’s written in pure Rust with a fully async API using Tokio. You can read more regarding its benchmark results, plus how our developers solved a performance regression.

This tutorial will teach you how to deploy a Graph Data System, using JanusGraph and ScyllaDB as the underlying data storage layer. It is part of a broader training course ScyllaDB University course,  The Mutant Monitoring System (MMS)  A Graph Data System Powered by ScyllaDB and JanusGraph. 

A graph data system (or graph database) is a database that uses a graph structure with nodes and edges to represent data. Edges represent relationships between nodes, and these relationships allow the data to be linked and for the graph to be visualized. It’s possible to use different storage mechanisms for the underlying data, and this choice affects the performance, scalability, ease of maintenance, and cost.