Amazon Athena vs Cassandra

Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Amazon Athena

Stacks521

Followers840

Votes49

Amazon Athena vs Cassandra: What are the differences?

Key Differences between Amazon Athena and Cassandra

Amazon Athena and Cassandra are popular NoSQL database solutions that offer different functionalities and use cases. Here are the key differences between the two:

Architecture and Data Model: Amazon Athena follows a serverless architecture, allowing users to execute ad-hoc SQL queries on the data stored in Amazon S3. It allows for schema-on-read and supports structured, semi-structured, and unstructured data. On the other hand, Cassandra is a distributed database system that follows a decentralized architecture using a peer-to-peer (P2P) model. It offers a column-based data model and is designed to handle high write throughput.
Scalability and Elasticity: Amazon Athena automatically scales based on query demands, making it highly scalable. It enables users to process large amounts of data without managing infrastructure. Cassandra also supports horizontal scalability by adding more nodes to the cluster as data grows. However, it requires manual setup and configuration to ensure optimal performance.
Data Consistency: In Amazon Athena, the data consistency is eventually consistent as it reads data directly from Amazon S3. This means that changes to the data may not be instantly reflected in query results. In Cassandra, users can configure the consistency level based on their requirements, allowing for stronger consistency if needed.
Query Language: Amazon Athena uses Standard SQL for querying data stored in Amazon S3. It supports common SQL functionalities, including joins, window functions, and complex aggregations. Cassandra, on the other hand, uses Cassandra Query Language (CQL), which is similar to SQL but with some specific syntax and limitations.
Data Storage: Amazon Athena stores data in Amazon S3, which provides durable and scalable object storage. It is suitable for storing large volumes of data cost-effectively. Cassandra, on the other hand, uses its own distributed file system called Cassandra File System (CFS). It replicates data across multiple nodes for high availability and fault tolerance.
Data Replication and Durability: Amazon Athena relies on the durability and availability of data in Amazon S3. It inherits the replication and durability features provided by Amazon S3. Cassandra, on the other hand, offers multiple replication strategies, allowing users to configure data replication across multiple data centers for high availability and durability.

In summary, Amazon Athena is a serverless query service for processing data stored in Amazon S3, offering scalability and flexibility. Cassandra, on the other hand, is a distributed database system designed for high write throughput and data replication across multiple nodes.

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Advice on Cassandra, Amazon Athena

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/.

5.08k views5.08k

Comments

Pavithra

Mar 12, 2020

Needs adviceon

Amazon S3

Amazon Athena

Amazon Redshift

Hi all,

Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?

522k views522k

Comments

Vinay

Head of Engineering

Sep 19, 2019

Needs advice

The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.

The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.

174k views174k

Comments

Detailed Comparison

Cassandra	Amazon Athena
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.	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.
Statistics
GitHub Stars 9.5K	GitHub Stars -
GitHub Forks 3.8K	GitHub Forks -
Stacks 3.6K	Stacks 521
Followers 3.5K	Followers 840
Votes 507	Votes 49
Pros & Cons
Pros 119 Distributed 98 High performance 81 High availability 74 Easy scalability 53 Replication Cons 3 Reliability of replication 1 Size 1 Updates	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 Cassandra, Amazon Athena?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

Related Comparisons

Amazon Athena vs Cassandra: What are the differences?

Key Differences between Amazon Athena and Cassandra

Amazon Athena and Cassandra are popular NoSQL database solutions that offer different functionalities and use cases. Here are the key differences between the two:

Architecture and Data Model: Amazon Athena follows a serverless architecture, allowing users to execute ad-hoc SQL queries on the data stored in Amazon S3. It allows for schema-on-read and supports structured, semi-structured, and unstructured data. On the other hand, Cassandra is a distributed database system that follows a decentralized architecture using a peer-to-peer (P2P) model. It offers a column-based data model and is designed to handle high write throughput.
Scalability and Elasticity: Amazon Athena automatically scales based on query demands, making it highly scalable. It enables users to process large amounts of data without managing infrastructure. Cassandra also supports horizontal scalability by adding more nodes to the cluster as data grows. However, it requires manual setup and configuration to ensure optimal performance.
Data Consistency: In Amazon Athena, the data consistency is eventually consistent as it reads data directly from Amazon S3. This means that changes to the data may not be instantly reflected in query results. In Cassandra, users can configure the consistency level based on their requirements, allowing for stronger consistency if needed.
Query Language: Amazon Athena uses Standard SQL for querying data stored in Amazon S3. It supports common SQL functionalities, including joins, window functions, and complex aggregations. Cassandra, on the other hand, uses Cassandra Query Language (CQL), which is similar to SQL but with some specific syntax and limitations.
Data Storage: Amazon Athena stores data in Amazon S3, which provides durable and scalable object storage. It is suitable for storing large volumes of data cost-effectively. Cassandra, on the other hand, uses its own distributed file system called Cassandra File System (CFS). It replicates data across multiple nodes for high availability and fault tolerance.
Data Replication and Durability: Amazon Athena relies on the durability and availability of data in Amazon S3. It inherits the replication and durability features provided by Amazon S3. Cassandra, on the other hand, offers multiple replication strategies, allowing users to configure data replication across multiple data centers for high availability and durability.

Amazon Athena vs Cassandra

Overview

Amazon Athena vs Cassandra: What are the differences?