15 Future of Databases: Moving Fast and Breaking Things
Tags: database, mysql, nosql, graphql
Published: 2023-03-03 05:26:09


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If you want to succeed in the tech world, you need to move fast and break things. Exactly what I said to my boss before he fired me for deleting a production database. While SQL is a reliable tool in managing databases, there's always room for improvement. The new breed of databases is like a group of mischievous toddlers, ready to push boundaries and break things (and hopefully not set anything on fire!).

# 1. Planetscale
Planetscale is a cloud-native database that is designed to scale easily and handle large amounts of data. It is a relational database that uses the SQL language. The creators of Planetscale wanted to create a database that could handle the needs of modern web applications, which require fast and scalable databases that can handle large amounts of data.

**Pros:**
- High scalability: Planetscale is designed to scale easily as your data needs grow.
- Cloud-native: Planetscale is built for the cloud, which means it's optimized for modern web applications.
- Ease of use: Planetscale is easy to set up and use.

**Cons:**
Limited to SQL: Planetscale only supports SQL, so if you need a NoSQL database, this may not be the best option.
Cost: Planetscale is a paid service, which may not be ideal for smaller applications.

# 2. Yugabyte
Yugabyte is a distributed SQL database that is designed to handle large amounts of data and high transaction rates. It is designed to be highly available and fault-tolerant, with built-in replication and load balancing features. Yugabyte supports both SQL and NoSQL interfaces, making it a versatile database solution.

**Pros:**
- Distributed architecture: Yugabyte is designed to be highly available and fault-tolerant, with built-in replication and load balancing features.
- Supports both SQL and NoSQL: Yugabyte supports both SQL and NoSQL interfaces, making it a versatile database solution.
- Scalable: Yugabyte is designed to scale easily as your data needs grow.

**Cons:**
- Complexity: Yugabyte's distributed architecture can be complex to set up and manage.
- Limited community support: Yugabyte is a relatively new database, so community support may be limited compared to more established databases.

# 3. Neon
Neon is a distributed NoSQL database that is designed for high performance and scalability. It is designed to be highly available and fault-tolerant, with built-in replication and sharding features. Neon supports a variety of data models, including key-value, document, and graph.

**Pros:**
- High performance: Neon is designed for high performance, with a distributed architecture that can handle high transaction rates.
- Scalable: Neon is designed to scale easily as your data needs grow.
- Versatile: Neon supports a variety of data models, making it a versatile database solution.

**Cons:**
- Limited community support: Neon is a relatively new database, so community support may be limited compared to more established databases.
- Complexity: Neon's distributed architecture can be complex to set up and manage.

# 4. Dolt
Dolt is a relational database that is designed to work like a Git repository. It allows you to version control your data, making it easy to track changes and collaborate with others. Dolt supports SQL, making it a versatile database solution.

**Pros:**
- Version control: Dolt allows you to version control your data, making it easy to track changes and collaborate with others.
- Ease of use: Dolt is easy to set up and use.
- Supports SQL: Dolt supports SQL, making it a versatile database solution.

**Cons:**
- Limited to SQL: Dolt only supports SQL, so if you need a NoSQL database, this may not be the best option.
- Limited scalability: Dolt may not be the best option for applications with high data needs, as it may not scale as easily as other databases.

# 5. CockroachDB
CockroachDB is a distributed SQL database that is designed for high availability, scalability, and strong consistency. It is inspired by Google's Spanner database and is designed to handle large amounts of data and high transaction rates. CockroachDB supports SQL, making it a versatile database solution.

**Pros:**
- Distributed architecture: CockroachDB is designed to be highly available and fault-tolerant, with built-in replication and load balancing features.
- Strong consistency: CockroachDB ensures strong consistency across all nodes, even in the face of network partitions or other failures.
- Scalable: CockroachDB is designed to scale easily as your data needs grow.

**Cons:**
- Complexity: CockroachDB's distributed architecture can be complex to set up and manage.
- Limited community support: CockroachDB is a relatively new database, so community support may be limited compared to more established databases.

# 6. Cloudflare D1
Cloudflare D1 is a distributed key-value store that is designed for high performance and scalability. It is designed to be highly available and fault-tolerant, with built-in replication and load balancing features. Cloudflare D1 supports a variety of data models, including key-value and document.

**Pros:**
- High performance: Cloudflare D1 is designed for high performance, with a distributed architecture that can handle high transaction rates.
- Scalable: Cloudflare D1 is designed to scale easily as your data needs grow.
- Distributed architecture: Cloudflare D1 is designed to be highly available and fault-tolerant, with built-in replication and load balancing features.

**Cons:**
- Limited data models: Cloudflare D1 only supports key-value and document data models, so if you need other data models, this may not be the best option.
- Limited community support: Cloudflare D1 is a relatively new database, so community support may be limited compared to more established databases.

# 7. Xata
Xata is a cloud-native database designed for data science and machine learning workloads. It is a NoSQL document-oriented database that uses JSON for data storage. The main features of Xata include schemaless and flexible data modeling, built-in version control for data, and support for complex queries. Xata was created to help data scientists work more efficiently by providing a database that integrates well with data science workflows.

**Pros:**
- Schemaless and flexible data modeling makes it easy to adapt to changing data requirements.
- Built-in version control provides an audit trail for changes made to data.
- Easy to integrate with data science workflows.

**Cons:**
- Not suitable for transactional workloads or high write loads.
- Limited support for SQL-like querying.

# 8. 8Base
8Base is a low-code development platform that includes a fully managed backend-as-a-service (BaaS) with a built-in SQL database. The main features of 8Base include a drag-and-drop data modeling tool, serverless functions for custom business logic, and automatic scaling and redundancy. 8Base was created to help developers build and deploy applications quickly, without having to worry about backend infrastructure.

**Pros:**
- Low-code platform makes it easy to build and deploy applications.
- Automatic scaling and redundancy ensures high availability and performance.
- Built-in SQL database supports complex querying and transactions.

**Cons:**
- Limited customization options for more complex applications.
- Not suitable for workloads that require advanced analytics or machine learning.

# 9. EdgeDB 
 EdgeDB is a relational database management system that uses a hybrid data model combining the best aspects of SQL and NoSQL databases. It features a rich data type system, declarative schema migrations, and a powerful query language. EdgeDB was created to address the limitations of existing databases in handling complex data models.

**Pros:**
- Hybrid data model combines the best aspects of SQL and NoSQL databases.
- Rich data type system supports complex data models.
- Declarative schema migrations make it easy to manage changes to data structures.

**Cons:**
- Limited ecosystem and community support compared to more established databases.
- Learning curve for the powerful query language may be steep for some users.

# 10. SurrealDB 
 SurrealDB is a distributed database management system designed for high-performance, low-latency workloads. It features a multi-model database with support for key-value, document, and graph data, and a distributed transaction layer for strong consistency. SurrealDB was created to provide a database that can handle the demands of modern, high-traffic applications.

**Pros:**
- Distributed transaction layer ensures strong consistency and fault tolerance.
- Multi-model database supports various types of data structures.
- High-performance and low-latency make it suitable for demanding workloads.

**Cons:**
- Limited ecosystem and community support compared to more established databases.
- Learning curve for configuring and optimizing performance may be steep for some users.

# 11. Fauna 
 Fauna is a globally distributed serverless database that supports multiple data models, including document, graph, and relational. It features a consistent transaction model, automatic scaling and redundancy, and a flexible query language. Fauna was created to provide a serverless database that can handle modern application workloads.

**Pros:**
- Globally distributed architecture ensures high availability and low-latency.
- Multiple data models support various types of data structures.
- Consistent transaction model ensures data integrity and consistency.

**Cons:**
- Limited customization options for more complex applications.
- Learning curve for the flexible query language may be steep for some users.

Conclusion: Overall, the databases on this list offer a wide range of features and capabilities, from distributed SQL databases that can handle high availability and fault tolerance to serverless NoSQL databases that provide global consistency and low latency for real-time applications. Additionally, there are databases that offer features like version control, in-memory storage, and search engine capabilities. Choosing the right database for a particular use case will depend on factors such as the volume of data, the complexity of queries, and the need for scalability and high availability.

[reference: fireship.io ](https://www.youtube.com/watch?v=jb2AvF8XzII)