Alooma vs Kafka

Overview

Kafka

Stacks24.2K

Followers22.3K

Votes607

GitHub Stars31.2K

Forks14.8K

Alooma

Stacks24

Followers47

Votes0

Alooma vs Kafka: What are the differences?

Data Processing Model: Alooma is a fully managed data pipeline that focuses on simplicity and ease of use, while Apache Kafka is a distributed event streaming platform designed for high-throughput, fault-tolerant, and real-time data processing.
Scalability: Apache Kafka is highly scalable and can handle a large volume of data streams by distributing them across multiple nodes, whereas Alooma may have limitations in scalability due to its managed service approach.
Community Support: Apache Kafka has a strong community support with active development and a wide range of plugins and integrations available, while Alooma is a more self-contained platform with limited community involvement.
Complexity: Alooma provides a more user-friendly and intuitive interface for setting up data pipelines and managing data streams, whereas Kafka requires more technical expertise and configuration for deployment and maintenance.
Performance: Apache Kafka excels in high-performance scenarios with low latency and high throughput, making it suitable for real-time data processing, while Alooma may have some performance limitations due to its managed nature.
Cost: Alooma is a cloud-based managed service where users pay for the resources they consume, while Kafka can be more cost-effective for large-scale deployments as it is open-source and can be self-hosted.

In Summary, Apache Kafka is known for its scalability, performance, and community support, whereas Alooma offers simplicity, user-friendliness, and managed services.

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Advice on Kafka, Alooma

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

Ishfaq

Feb 28, 2020

Needs advice

Our backend application is sending some external messages to a third party application at the end of each backend (CRUD) API call (from UI) and these external messages take too much extra time (message building, processing, then sent to the third party and log success/failure), UI application has no concern to these extra third party messages.

So currently we are sending these third party messages by creating a new child thread at end of each REST API call so UI application doesn't wait for these extra third party API calls.

I want to integrate Apache Kafka for these extra third party API calls, so I can also retry on failover third party API calls in a queue(currently third party messages are sending from multiple threads at the same time which uses too much processing and resources) and logging, etc.

Question 1: Is this a use case of a message broker?

Question 2: If it is then Kafka vs RabitMQ which is the better?

804k views804k

Comments

Roman

Senior Back-End Developer, Software Architect

Feb 12, 2019

Reviewon

Kafka

I use Kafka because it has almost infinite scaleability in terms of processing events (could be scaled to process hundreds of thousands of events), great monitoring (all sorts of metrics are exposed via JMX).

Downsides of using Kafka are:

you have to deal with Zookeeper
you have to implement advanced routing yourself (compared to RabbitMQ it has no advanced routing)

10.9k views10.9k

Comments

Detailed Comparison

Kafka	Alooma
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.	Get the power of big data in minutes with Alooma and Amazon Redshift. Simply build your pipelines and map your events using Alooma’s friendly mapping interface. Query, analyze, visualize, and predict now.
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	-
Statistics
GitHub Stars 31.2K	GitHub Stars -
GitHub Forks 14.8K	GitHub Forks -
Stacks 24.2K	Stacks 24
Followers 22.3K	Followers 47
Votes 607	Votes 0
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	No community feedback yet
Integrations
No integrations available	Chartio PostgreSQL MongoDB Salesforce Sales Cloud Cassandra Amazon Redshift MySQL Marketo Mixpanel Google Analytics

What are some alternatives to Kafka, Alooma?

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.

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

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.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

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.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

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.

Related Comparisons

Alooma vs Kafka: What are the differences?

Data Processing Model: Alooma is a fully managed data pipeline that focuses on simplicity and ease of use, while Apache Kafka is a distributed event streaming platform designed for high-throughput, fault-tolerant, and real-time data processing.
Scalability: Apache Kafka is highly scalable and can handle a large volume of data streams by distributing them across multiple nodes, whereas Alooma may have limitations in scalability due to its managed service approach.
Community Support: Apache Kafka has a strong community support with active development and a wide range of plugins and integrations available, while Alooma is a more self-contained platform with limited community involvement.
Complexity: Alooma provides a more user-friendly and intuitive interface for setting up data pipelines and managing data streams, whereas Kafka requires more technical expertise and configuration for deployment and maintenance.
Performance: Apache Kafka excels in high-performance scenarios with low latency and high throughput, making it suitable for real-time data processing, while Alooma may have some performance limitations due to its managed nature.
Cost: Alooma is a cloud-based managed service where users pay for the resources they consume, while Kafka can be more cost-effective for large-scale deployments as it is open-source and can be self-hosted.

In Summary, Apache Kafka is known for its scalability, performance, and community support, whereas Alooma offers simplicity, user-friendliness, and managed services.

Alooma vs Kafka

Overview