Overview

Kafka

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GitHub Stars31.2K

Forks14.8K

Amazon Athena

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Amazon Athena vs Kafka: What are the differences?

Key Differences Between Amazon Athena and Kafka

Amazon Athena and Kafka are both widely used technologies in the world of data processing and analytics, but they serve different purposes and have distinct features. Below are six key differences between the two platforms:

Data Processing Model: Amazon Athena is a serverless interactive query service that allows users to analyze data stored in Amazon S3 using standard SQL. It is designed for ad-hoc querying and analysis of data, without the need for infrastructure management. On the other hand, Kafka is a distributed streaming platform that allows for the high-throughput, fault-tolerant publish-subscribe messaging system, enabling real-time data streaming and processing.
Data Source: Amazon Athena is primarily used for querying and analyzing data stored in Amazon S3. It provides the ability to run queries directly against the data without the need to load it into a separate database or data warehouse. In contrast, Kafka acts as a data pipeline and broker that enables the streaming of data from various sources to different applications or systems for processing and consumption.
Real-time vs Batch Processing: While both platforms can handle large volumes of data, Kafka is designed for real-time processing and streaming of data streams, making it ideal for use cases that require low-latency data consumption and real-time analytics. On the other hand, Athena is more suited for batch processing and ad-hoc querying, where the focus is on running SQL queries on stored data in S3 rather than real-time processing.
Scalability and Elasticity: Amazon Athena is a fully managed service that automatically scales up or down the resources required to execute queries based on the amount of data being processed. It offers horizontal scalability and can handle large data sets efficiently. Kafka, on the other hand, can also scale horizontally, allowing users to add more brokers to the cluster to handle increased data throughput. It offers high scalability, fault tolerance, and the ability to handle terabytes of data per day.
Data Format and Schema: Amazon Athena supports various data formats such as CSV, Parquet, JSON, and ORC, and provides the ability to define explicit schemas for structured data. It leverages AWS Glue and the Hive metastore for data cataloging and schema inference. In contrast, Kafka does not enforce any specific data format, allowing users to stream both structured and unstructured data. It provides flexibility in data serialization and deserialization.
Integration and Ecosystem: Amazon Athena integrates seamlessly with other AWS services, such as AWS Glue for data cataloging, AWS Lambda for serverless data transformations, and Amazon QuickSight for data visualization. It benefits from the broader AWS ecosystem and offers tight integration with other AWS analytics tools. Kafka, on the other hand, has a wide ecosystem of connectors and integrations with various data processing frameworks and messaging systems, making it a versatile choice for building real-time data pipelines.

In summary, Amazon Athena is a serverless interactive query service designed for ad-hoc querying and analysis of data stored in S3, while Kafka is a distributed streaming platform designed for real-time data streaming and processing. Athena is focused on batch processing, while Kafka excels in real-time processing and supports various data formats. Athena benefits from tight integration with the AWS ecosystem, while Kafka has a vast ecosystem of connectors and integrations.

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Advice on Kafka, Amazon Athena

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

Kevin

Co-founder at Transloadit

Dec 18, 2020

Review

Hey there, the trick to keeping costs under control is to partition. This means you split up your source files by date, and also query within dates, so that Athena only scans the few files necessary for those dates. I hope that makes sense (and I also hope I understood your question right). This article explains better https://aws.amazon.com/blogs/big-data/analyze-your-amazon-cloudfront-access-logs-at-scale/.

5.07k views5.07k

Comments

Detailed Comparison

Kafka	Amazon Athena
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.	Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
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 24.0K	Stacks 505
Followers 22.3K	Followers 840
Votes 607	Votes 49
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 16 Use SQL to analyze CSV files 8 Glue crawlers gives easy Data catalogue 7 Cheap 6 Query all my data without running servers 24x7 4 No data base servers yay
Integrations
No integrations available	Amazon S3 Presto

What are some alternatives to Kafka, Amazon Athena?

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.

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

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.

Presto

Distributed SQL Query Engine for Big Data

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.

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.