Amazon RDS for Aurora vs Kafka

Overview

Kafka

Stacks24.2K

Followers22.3K

Votes607

GitHub Stars31.2K

Forks14.8K

Amazon Aurora

Stacks804

Followers744

Votes55

Amazon RDS for Aurora vs Kafka: What are the differences?

Introduction

When comparing Amazon RDS for Aurora and Kafka, there are some key differences to consider.

Database vs. Streaming Platform: Amazon RDS for Aurora is a relational database service, whereas Kafka is a distributed streaming platform. Aurora is designed for traditional database needs such as transactions and analytics, while Kafka is designed for real-time data processing and stream processing.
Data Model: Aurora stores data in tables with rows and columns, following a relational data model. On the other hand, Kafka stores data in topics, which are divided into partitions, following a pub/sub messaging model. This difference in data model reflects the different use cases each service is optimized for.
Scalability: Amazon RDS for Aurora provides automated scaling options for read replicas and storage capacity. In contrast, Kafka is horizontally scalable by adding more broker nodes to the cluster. Kafka's partitioning mechanism allows for parallel processing and high-throughput data handling.
Data Processing: Aurora supports complex SQL queries for analytics and reporting purposes. In comparison, Kafka offers capabilities for real-time data processing, event streaming, and building data pipelines. Kafka's distributed architecture enables high-throughput, low-latency data processing.
Data Durability: Aurora ensures data durability through automatic backups and replicas, providing high availability and fault tolerance. Kafka, on the other hand, prioritizes data throughput and real-time processing over durability. Replication in Kafka is used for availability and fault tolerance rather than durability.
Use Cases: Amazon RDS for Aurora is suitable for applications requiring ACID compliance, complex queries, and traditional database functionalities. Kafka is ideal for use cases such as real-time analytics, event-driven architectures, log aggregation, and stream processing applications. Kafka excels in scenarios where low latency and high throughput are paramount.

In Summary, Amazon RDS for Aurora and Kafka differ in terms of database vs. streaming platform, data model, scalability, data processing, data durability, and use cases, catering to distinct needs in the realm of data management and processing.

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Manual

Advice on Kafka, Amazon Aurora

viradiya

Apr 12, 2020

Needs adviceon

AngularJS

ASP.NET Core

MSSQL

We are going to develop a microservices-based application. It consists of AngularJS, ASP.NET Core, and MSSQL.

We have 3 types of microservices. Emailservice, Filemanagementservice, Filevalidationservice

I am a beginner in microservices. But I have read about RabbitMQ, but come to know that there are Redis and Kafka also in the market. So, I want to know which is best.

933k views933k

Comments

Kirill

GO/C developer at Duckling Sales

Feb 16, 2021

Decided

Maybe not an obvious comparison with Kafka, since Kafka is pretty different from rabbitmq. But for small service, Rabbit as a pubsub platform is super easy to use and pretty powerful. Kafka as an alternative was the original choice, but its really a kind of overkill for a small-medium service. Especially if you are not planning to use k8s, since pure docker deployment can be a pain because of networking setup. Google PubSub was another alternative, its actually pretty cheap, but I never tested it since Rabbit was matching really good for mailing/notification services.

266k views266k

Comments

Phillip

Developer at Coach Align

Mar 18, 2021

Decided

Using on-demand read/write capacity while we scale our userbase - means that we're well within the free-tier on AWS while we scale the business and evaluate traffic patterns.

Using single-table design, which is dead simple using Jeremy Daly's dynamodb-toolbox library

29.3k views29.3k

Comments

Detailed Comparison

Kafka	Amazon Aurora
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.	Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability.
Written at LinkedIn in Scala;Used by LinkedIn to offload processing of all page and other views;Defaults to using persistence, uses OS disk cache for hot data (has higher throughput then any of the above having persistence enabled);Supports both on-line as off-line processing	High Throughput with Low Jitter;Push-button Compute Scaling;Storage Auto-scaling;Amazon Aurora Replicas;Instance Monitoring and Repair;Fault-tolerant and Self-healing Storage;Automatic, Continuous, Incremental Backups and Point-in-time Restore;Database Snapshots;Resource-level Permissions;Easy Migration;Monitoring and Metrics
Statistics
GitHub Stars 31.2K	GitHub Stars -
GitHub Forks 14.8K	GitHub Forks -
Stacks 24.2K	Stacks 804
Followers 22.3K	Followers 744
Votes 607	Votes 55
Pros & Cons
Pros 126 High-throughput 119 Distributed 92 Scalable 86 High-Performance 66 Durable Cons 32 Non-Java clients are second-class citizens 29 Needs Zookeeper 9 Operational difficulties 5 Terrible Packaging	Pros 14 MySQL compatibility 12 Better performance 10 Easy read scalability 9 Speed 7 Low latency read replica Cons 2 Vendor locking 1 Rigid schema
Integrations
No integrations available	PostgreSQL MySQL

What are some alternatives to Kafka, Amazon Aurora?

Amazon RDS

Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Celery

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

ActiveMQ

Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Google Cloud SQL

Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.

Gearman

Gearman allows you to do work in parallel, to load balance processing, and to call functions between languages. It can be used in a variety of applications, from high-availability web sites to the transport of database replication events.

Related Comparisons

Amazon RDS for Aurora vs Kafka: What are the differences?

Introduction

When comparing Amazon RDS for Aurora and Kafka, there are some key differences to consider.

Database vs. Streaming Platform: Amazon RDS for Aurora is a relational database service, whereas Kafka is a distributed streaming platform. Aurora is designed for traditional database needs such as transactions and analytics, while Kafka is designed for real-time data processing and stream processing.
Data Model: Aurora stores data in tables with rows and columns, following a relational data model. On the other hand, Kafka stores data in topics, which are divided into partitions, following a pub/sub messaging model. This difference in data model reflects the different use cases each service is optimized for.
Scalability: Amazon RDS for Aurora provides automated scaling options for read replicas and storage capacity. In contrast, Kafka is horizontally scalable by adding more broker nodes to the cluster. Kafka's partitioning mechanism allows for parallel processing and high-throughput data handling.
Data Processing: Aurora supports complex SQL queries for analytics and reporting purposes. In comparison, Kafka offers capabilities for real-time data processing, event streaming, and building data pipelines. Kafka's distributed architecture enables high-throughput, low-latency data processing.
Data Durability: Aurora ensures data durability through automatic backups and replicas, providing high availability and fault tolerance. Kafka, on the other hand, prioritizes data throughput and real-time processing over durability. Replication in Kafka is used for availability and fault tolerance rather than durability.
Use Cases: Amazon RDS for Aurora is suitable for applications requiring ACID compliance, complex queries, and traditional database functionalities. Kafka is ideal for use cases such as real-time analytics, event-driven architectures, log aggregation, and stream processing applications. Kafka excels in scenarios where low latency and high throughput are paramount.

Amazon RDS for Aurora vs Kafka

Overview

Amazon RDS for Aurora vs Kafka: What are the differences?