Overview

Microsoft SQL Server

Stacks20.2K

Followers15.5K

Votes540

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Cassandra vs Microsoft SQL Server: What are the differences?

Introduction

Cassandra and Microsoft SQL Server are both popular database management systems, but they have key differences in terms of architecture, scalability, query language, and data model.

Scalability: One of the major differences is in terms of scalability. Cassandra is designed to be highly scalable and can easily handle huge amounts of data and high write loads. It follows a masterless architecture where all nodes are equal, allowing for horizontal scaling. On the other hand, SQL Server follows a master-slave architecture and is typically limited in scalability compared to Cassandra.
Architecture: Cassandra is a distributed database system that is designed to handle big data workloads. It follows a peer-to-peer architecture where data is evenly distributed across multiple nodes. This architecture allows for fault tolerance and high availability. SQL Server, on the other hand, follows a client-server architecture where a central server handles all the database operations.
Query Language: Another significant difference lies in the query language used by the two databases. Cassandra uses Cassandra Query Language (CQL), a SQL-like language that is specifically designed for Cassandra. CQL supports a subset of SQL features along with additional features that are specific to Cassandra. SQL Server, on the other hand, uses Transact-SQL (T-SQL), a proprietary SQL dialect developed by Microsoft. T-SQL is a comprehensive SQL language that provides extensive support for storing, retrieving, and manipulating data.
Data Model: Cassandra follows a wide-column data model, also known as a columnar or column-family data model. It allows for flexible schema design and supports large, denormalized datasets. SQL Server, on the other hand, follows a relational data model where data is organized into tables with predefined schemas and relationships.
Consistency: Consistency is another key difference between Cassandra and SQL Server. Cassandra offers tunable consistency, allowing users to select the consistency level based on their application's requirements. It provides options like eventual consistency, strong consistency, and various levels in between. SQL Server, on the other hand, offers strong consistency by default, ensuring that data consistency is maintained at all times.
Support and Community: SQL Server is developed and maintained by Microsoft and has a large user base. It has extensive support and a well-established community, which means users can easily find resources and assistance. Cassandra, on the other hand, is an open-source project maintained by the Apache Software Foundation. It also has a strong community, but the support may not be as extensive as SQL Server.

In summary, Cassandra and SQL Server differ in terms of scalability, architecture, query language, data model, consistency, and support. While Cassandra excels in scalability and flexible data models, SQL Server offers strong consistency and extensive support options.

Advice on Microsoft SQL Server, Cassandra

Erin

IT Specialist

Mar 10, 2020

Needs adviceon

Microsoft SQL Server

MySQL

PostgreSQL

I am a Microsoft SQL Server programmer who is a bit out of practice. I have been asked to assist on a new project. The overall purpose is to organize a large number of recordings so that they can be searched. I have an enormous music library but my songs are several hours long. I need to include things like time, date and location of the recording. I don't have a problem with the general database design. I have two primary questions:

I need to use either MySQL or PostgreSQL on a Linux based OS. Which would be better for this application?
I have not dealt with a sound based data type before. How do I store that and put it in a table? Thank you.

668k views668k

Comments

Krishna Chaitanya

Head of Technology at Adonmo

Jun 27, 2021

Review

For such a more realtime-focused, data-centered application like an exchange, it's not the frontend or backend that matter much. In fact for that, they can do away with any of the popular frameworks like React/Vue/Angular for the frontend and Go/Python for the backend. For example uniswap's frontend (although much simpler than binance) is built in React. The main interesting part here would be how they are able to handle updating data so quickly. In my opinion, they might be heavily reliant on realtime processing systems like Kafka+Kafka Streams, Apache Flink or Apache Spark Stream or similar. For more processing heavy but not so real-time processing, they might be relying on OLAP and/or warehousing tools like Cassandra/Redshift. They could have also optimized few high frequent queries using NoSQL stores like mongodb (for persistance) and in-memory cache like Redis (for further perfomance boost to get millisecond latencies).

53.8k views53.8k

Comments

Umair

Technical Architect at ERP Studio

Feb 12, 2021

Needs adviceon

PostgreSQL

TimescaleDB

Druid

Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

462k views462k

Comments

Detailed Comparison

Microsoft SQL Server	Cassandra
Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.	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.
Statistics
GitHub Stars -	GitHub Stars 9.5K
GitHub Forks -	GitHub Forks 3.8K
Stacks 20.2K	Stacks 3.6K
Followers 15.5K	Followers 3.5K
Votes 540	Votes 507
Pros & Cons
Pros 139 Reliable and easy to use 101 High performance 95 Great with .net 65 Works well with .net 56 Easy to maintain Cons 4 Expensive Licensing 2 Microsoft 1 Replication can loose the data 1 Allwayon can loose data in asycronious mode 1 Data pages is only 8k	Pros 119 Distributed 98 High performance 81 High availability 74 Easy scalability 53 Replication Cons 3 Reliability of replication 1 Updates 1 Size

What are some alternatives to Microsoft SQL Server, Cassandra?

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.

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.

CouchDB

Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript.