In-Memory Databases: Employing High-Availability and Low Latency for Skyrocketing Data Generation

Modern applications require databases that can constantly adapt to their real-time needs, introducing a variety of complexities when it comes to selecting the right database for the job. The era of the one-size-fits-all approach by employing a singular database is no longer viable; databases crafted for the specific demands of applications are the present—and future—of effective application development and maintenance. AWS Databases, which are purpose-built for a variety of application needs, are the solution amid complexity.

DBTA recently held a webinar, sponsored by AWS, titled “Real-World Use Cases: Amazon MemoryDB for Redis,” featuring speakers Giselle Goicochea, senior product marketing manager at AWS Databases, and Siva Karuturi, worldwide specialist solutions architect for in-memory at AWS Databases, to discuss the nuance of in-memory databases and its relevance towards the ever-scaling generation of data.

Making data actionable, in a world where IDC forecasts over 972ZB of data will be created and replicated in 2022 alone, is a financial priority for any enterprise. According to an Accenture study, 68% of organizations reported that they are still unable to realize tangible and measurable value from data. Mountains upon mountains of data is left underutilized, greatly reducing data-driven business opportunities—and in turn, enterprise finances.

Typically, a search for a modern database application is then needed to handle all that data—and can come ripe with other technological challenges. Developers must negotiate database advantages regarding performance, scalability, and comprehensive data type integration. A monolithic database can no longer prosper in the modern tech landscape, creating a need for purpose-built databases—which AWS supplies.

In-memory database Amazon MemoryDB is designed to be a highly durable, low latency, fully-managed, scalable database that relies on memory for storage, contrasting database types that store on SSDs or hard disks. The nature of its storage provides the foundation for minimizing response times due to disk access, as well as avoiding data loss risks from process or service failure due to exclusive memory storage.

Further, support for microservice architectures is inherent to Amazon MemoryDB. MemoryDB provides the necessary speed, flexibility, and ease of management that hot data, emphasized by microservice applications, requires. Karuturi broke down the technological specifics of MemoryDB’s operation for webinar viewers, illustrating the ways in which the database is fundamentally designed to support simplified architecture access and microservices.

The database can process up to 13 trillion requests per day, with over 160 million requests processed per second, and its Redis compatibility closely maps low-level data structures to your application’s architectures for improved flexibility. It has superior scalability, as well as a multi-AZ transaction log for durability and replicas for high availability. MemoryDB also provides encryption at-rest and in-transit, Access Control Lists (ACLs), and Amazon VPC for enhanced security.

The real-world use cases for Amazon MemoryDB for Redis are extensive; though it is a purpose-built database specializing in a particular need, those needs represent a variety of industries that require its expertise. Areas of industry ranging from web and mobile, retail/e-commerce, gaming, banking and finance, media and entertainment, and IoT find value within in-memory databases.

To learn more about Amazon MemoryDB and its use cases, you can view an archived version of this here.