#7 Modern SQL Databases You Must Know in 2024
Tags: sql, dolt, snowflake, elasticsearch
Published: 2024-07-02 06:03:36

#clickhouse #MongoDB #Redis #MindsDB
 
 
 
 

1. Dolt
 

Dolt is an open-source, version-controlled database that combines the power of Git with the functionality of a relational database. With Dolt, you can fork, clone, branch, merge, push, and pull databases just like you would with a Git repository.

Dolt is MySQL-compatible, allowing you to run SQL queries and use the command line interface to manage your data. This version-controlled database is ideal for collaborative environments where tracking changes and maintaining data integrity are paramount.

Just like GitHub, DoltHub is a place where people can share their database. You can access the public database for free just like GitHub.

 

2. MongoDB
 

MongoDB is a popular NoSQL database known for its flexibility and scalability. it uses a document-oriented data model, which allows for the storage of semi-structured data. With its flexible data model and rich ecosystem of tools and services, MongoDB is a favorite among developers and enterprises alike. Its ability to handle large amounts of unstructured data makes it an ideal choice for modern applications.

MongoDB is available in different environments, including MongoDB Atlas (a fully managed service in the cloud), MongoDB Enterprise (a subscription-based, self-managed version), and MongoDB Community (a free-to-use, self-managed version).

 

3. Redis
 

Redis is a fast in-memory database used as a caching, vector search, message broker, and NoSQL databases that seamlessly fit into any tech stack. Known for its high performance and low latency, Redis is widely used in real-time applications such as caching, session management, and real-time analytics. Its support for various data structures like strings, hashes, lists, sets, and more makes it a powerful tool for developers.

 

4. MindsDB
 

MindsDB is a platform that enhances SQL databases with machine learning capabilities. It allows you to build, fine-tune, and serve machine learning models directly within your database using familiar SQL syntax. MindsDB integrates with numerous data sources, including databases, vector stores, and applications, and popular AI/ML frameworks for AutoML and LLMs.

Imagine Transformers, LangChain, Vector database, OpenAI API, SQL and NoSQL database, and agents all in one, and you can access them using SQL syntax. It is a dream for data engineers and analysts.

 

5. Clickhouse
 

ClickHouse is an open-source columnar database management system designed for online analytical processing (OLAP). It is known for its high performance and efficiency in handling large volumes of data. ClickHouse is particularly well-suited for real-time analytics and big data applications, providing fast query performance and scalability.

Apart from being blazing fast, ClickHouse is developer-friendly as complex data analysis can be done using simple SQL. Moreover, it is cost-effective with compression ratios that reduce storage and accelerate performance.

 

6. Elasticsearch
 

Elasticsearch is a distributed, RESTful search and analytics engine built on Apache Lucene. It securely stores your data for lightning-fast search, fine-tuned relevancy, and powerful analytics that scale quickly. Elasticsearch is often used with the ELK stack (Elasticsearch, Logstash, Kibana) for log and event data analysis, making it a popular choice for monitoring and observability solutions. With Elasticsearch, you can easily tackle large-scale data challenges, ensuring that your search and analytics capabilities grow alongside your data.

 

7. Snowflake
 

Snowflake is a cloud-based data warehousing solution that offers a unique architecture to handle diverse data workloads. It separates storage and compute, allowing for independent scaling of resources. Snowflake supports structured and semi-structured data, providing robust data sharing and collaboration features. Its seamless integration with various cloud platforms makes it a go-to choice for modern data warehousing needs.