DalmatinerDB vs Microsoft SQL Server

Overview

Microsoft SQL Server

Stacks21.3K

Followers15.5K

Votes540

DalmatinerDB

Stacks6

Followers10

Votes1

GitHub Stars690

Forks43

DalmatinerDB vs Microsoft SQL Server: What are the differences?

Data Model: DalmatinerDB is a time series database that focuses on storing and querying time series data, while Microsoft SQL Server is a relational database management system that supports a wide variety of data types and structures. DalmatinerDB is optimized for high-performance storage and retrieval of time-stamped data points, making it ideal for monitoring and IoT applications, whereas Microsoft SQL Server is designed for transactional and analytical workloads that require complex data relationships.
Query Language: DalmatinerDB uses the Erlang-based Query Language (EQL) for querying time series data, offering a simple and efficient syntax tailored for time series operations. In contrast, Microsoft SQL Server utilizes Transact-SQL (T-SQL) as its query language, providing a powerful and feature-rich language for working with relational data and complex queries. EQL is optimized for time series operations like aggregation, filtering, and interpolation, while T-SQL offers a broader range of functionalities for managing relational data.
Scaling: DalmatinerDB is designed to scale horizontally across multiple nodes, allowing users to distribute time series data and queries across a cluster of servers for increased performance and capacity. On the other hand, Microsoft SQL Server supports both vertical scaling (increasing the resources of a single server) and horizontal scaling (sharding data across multiple servers) for handling larger workloads. DalmatinerDB's focus on horizontal scaling makes it a preferred choice for handling large volumes of time series data efficiently.
Storage Engine: DalmatinerDB employs a custom storage engine optimized for fast writes and reads of time series data, using advanced techniques like sharding and compression to ensure efficient data storage and retrieval. In comparison, Microsoft SQL Server utilizes a traditional relational database storage engine that is well-suited for managing structured data with complex relationships. The storage engine in DalmatinerDB is tailored specifically for time series data, offering better performance and scalability for time-based queries.
Open Source vs. Commercial: DalmatinerDB is an open-source time series database that is freely available for download and use, allowing developers to contribute to its development and customize it to suit their specific requirements. In contrast, Microsoft SQL Server is a commercial database management system that requires a license for deployment, offering a comprehensive set of features and support services from Microsoft. The choice between DalmatinerDB and Microsoft SQL Server may depend on factors like budget, support needs, and the flexibility of open-source software.
Use Cases: DalmatinerDB is well-suited for use cases that involve collecting, storing, and analyzing time series data from sensors, logs, and monitoring systems, where fast ingestion and querying of time-stamped data points are critical. On the other hand, Microsoft SQL Server is commonly used for a wide range of applications, including e-commerce, business intelligence, and enterprise resource planning, that require robust transactional support, complex querying capabilities, and data integrity features.

In Summary, DalmatinerDB and Microsoft SQL Server differ in their data models, query languages, scaling capabilities, storage engines, licensing models, and use cases.

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Advice on Microsoft SQL Server, DalmatinerDB

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}|tool:1025| or @{PostgreSQL}|tool:1028| on a @{Linux}|tool:10483| 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.

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Detailed Comparison

Microsoft SQL Server	DalmatinerDB
Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.	DalmatinerDB is a no fluff purpose built metric database. Not a layer put on top of a general purpose database or datastore.
Statistics
GitHub Stars -	GitHub Stars 690
GitHub Forks -	GitHub Forks 43
Stacks 21.3K	Stacks 6
Followers 15.5K	Followers 10
Votes 540	Votes 1
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 The maximum number of connections is only 14000 connect 1 Replication can loose the data 1 Allwayon can loose data in asycronious mode	Pros 1 Light and Fast

What are some alternatives to Microsoft SQL Server, DalmatinerDB?

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.

Cassandra

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.

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

DalmatinerDB vs Microsoft SQL Server: What are the differences?

Data Model: DalmatinerDB is a time series database that focuses on storing and querying time series data, while Microsoft SQL Server is a relational database management system that supports a wide variety of data types and structures. DalmatinerDB is optimized for high-performance storage and retrieval of time-stamped data points, making it ideal for monitoring and IoT applications, whereas Microsoft SQL Server is designed for transactional and analytical workloads that require complex data relationships.
Query Language: DalmatinerDB uses the Erlang-based Query Language (EQL) for querying time series data, offering a simple and efficient syntax tailored for time series operations. In contrast, Microsoft SQL Server utilizes Transact-SQL (T-SQL) as its query language, providing a powerful and feature-rich language for working with relational data and complex queries. EQL is optimized for time series operations like aggregation, filtering, and interpolation, while T-SQL offers a broader range of functionalities for managing relational data.
Scaling: DalmatinerDB is designed to scale horizontally across multiple nodes, allowing users to distribute time series data and queries across a cluster of servers for increased performance and capacity. On the other hand, Microsoft SQL Server supports both vertical scaling (increasing the resources of a single server) and horizontal scaling (sharding data across multiple servers) for handling larger workloads. DalmatinerDB's focus on horizontal scaling makes it a preferred choice for handling large volumes of time series data efficiently.
Storage Engine: DalmatinerDB employs a custom storage engine optimized for fast writes and reads of time series data, using advanced techniques like sharding and compression to ensure efficient data storage and retrieval. In comparison, Microsoft SQL Server utilizes a traditional relational database storage engine that is well-suited for managing structured data with complex relationships. The storage engine in DalmatinerDB is tailored specifically for time series data, offering better performance and scalability for time-based queries.
Open Source vs. Commercial: DalmatinerDB is an open-source time series database that is freely available for download and use, allowing developers to contribute to its development and customize it to suit their specific requirements. In contrast, Microsoft SQL Server is a commercial database management system that requires a license for deployment, offering a comprehensive set of features and support services from Microsoft. The choice between DalmatinerDB and Microsoft SQL Server may depend on factors like budget, support needs, and the flexibility of open-source software.
Use Cases: DalmatinerDB is well-suited for use cases that involve collecting, storing, and analyzing time series data from sensors, logs, and monitoring systems, where fast ingestion and querying of time-stamped data points are critical. On the other hand, Microsoft SQL Server is commonly used for a wide range of applications, including e-commerce, business intelligence, and enterprise resource planning, that require robust transactional support, complex querying capabilities, and data integrity features.

In Summary, DalmatinerDB and Microsoft SQL Server differ in their data models, query languages, scaling capabilities, storage engines, licensing models, and use cases.

DalmatinerDB vs Microsoft SQL Server

Overview