Serverless Databases are gaining popularity in the business world for their flexibility, ease of use, and fast deployment. Aside from being easy to set up, they are also very cost-effective.
Non-relational Serverless databases are a good way to store large amounts of unstructured data. They are often used to store documents and forms, and they are also useful for storing complex data.
Structured & Meta Data
Relational databases are more structured and contain metadata. Data is arranged in tables. Tables are grouped together by a foreign key. The key is the unique identifier that is assigned to each row in the table. This allows for relationships to be established.
A graph database is another good choice for highly interconnected data. It is a storage system that is designed to facilitate easy queries and fast access to information.
Most Well-Known Database Query Languages
One of the most well-known database query languages is SQL. However, it is not always the best option for your needs. You might want to consider NoSQL solutions instead. These solutions can handle data storage across multiple servers and can handle dynamic user numbers.
Both relational and non-relational databases have their strengths and weaknesses. While a non-relational database can perform better, it might be more challenging to manage.
Row-Based Storage & Transactional Capabilities
One of the newest announcements at Snowflake Summit 2022 is the Unistore. This new technology combines row-based storage and transactional capabilities in a single database. It enables users to optimize data and run analytics on transactional and analytical workloads simultaneously.
Previously, most organizations have stored their transactional data in different silos. These data silos are difficult to join together for analytical purposes. The ETL/ELT process is a resource intensive and latency-infested operation. However, companies have moved to the cloud and cloud-based solutions, such as Snowflake, are simplifying the path for developers to onramp their applications.
Hybrid Tables are another recent innovation. They provide improved support for transactional workloads, along with the capability to run analytics directly on tables. By combining row-based and column-based storage, they can improve query performance by up to 50x, according to Snowflake.
Snowflake will support both JSON and relational hybrid tables. These tables will have an INDEX keyword. In addition, they can have BTREE indexes.
Snowflake will also support the ability to update data. You can perform operations, such as deleting, on JSON and relational hybrid tables.
Disaggregation of Storage & Compute
Disaggregation of storage and compute is a popular concept in the history of databases. It’s a way to provide flexibility to a company by allowing them to share database access. The benefits include low latency, improved resource utilization, and data privacy.
Cost of Large Database Clusters
In the past, the cost of large database clusters was the limiting factor for many workloads. However, this is changing as companies look for cheaper ways to scale storage resources. Today, startups are leading the pack with computational storage products such as Lightbits, Liqid, and Nebulon.
Many workloads are dominated by Direct Attached Storage (DAS). While RAM is a fast storage, it’s prone to latency, which is often measured in nanoseconds.
Surreal DB allows users to store data in multiple tables and switch between them in queries. It also supports multiple-table transactions and a variety of data types. You can even limit data to specific columns.
High-Speed NVMe SSDs
Disaggregation of storage and compute allows you to optimize your resource consumption by leveraging high-speed NVMe SSDs and logical storage pools over the network fabric. This is especially important at higher storage densities. With this unique storage solution, you can be confident that your data is secure, scalable, and available.
Surreal DB is a cloud-hosted, serverless database that provides support for structured and unstructured data. This enables applications to be built with both a schemaful and a schemaless model. It can be run as a single in-memory node or as a distributed cluster.
SurrealDB is designed to speed up the development process of modern apps, while providing advanced querying and analytics. It supports multi-column indexes and live SQL queries. By combining these aspects, it reduces the time it takes to build back-end APIs.
As a result, it provides a complete Backend-as-a-Service for realtime collaborative applications. In addition, it eliminates the need for complicated database development. It offers a high degree of security for multi-tenant access, as well as inbuilt permissions and granular access.
Moreover, SurrealDB is compatible with Linux, Windows, and macOS. The database also supports JSON-RPC modification over WebSockets. Additionally, it supports strong-typed data types and full-text indexing.
Another feature that makes Surreal DB stand out from other databases is its unique querying language, which is based on SQL. It can be used in web browsers or from client devices.