With users today expecting speed, reliability, and responsiveness from every application they use, the delivery of seamless experiences across various platforms becomes essential for organizations. Caching solutions like Redis play a vital role in these ecosystems by storing frequently accessed data in memory, reducing the need to retrieve it from slower back-end systems, such as databases.

The 2024 State of the Database Landscape survey showed that 79% of businesses are now using two or more database platforms (vs 62% in 2020). Businesses revealed that this growth was largely due to the differing use cases across each platform, as well as the need to increase flexibility. Despite these recognized benefits, increasing the diversity of your database platforms can bring its own set of challenges, including skillset requirements, data integration complexities, security, and monitoring.

In the digital era, where data reigns supreme, the ability to extract meaningful insights from vast datasets has become indispensable. Though we have many data sources and data processing languages, SQL (Structured Query Language) stands as a cornerstone in this realm, empowering analysts, and data scientists to navigate through intricate databases with ease.

Before diving into how to ensure the reliability, availability, and optimal performance of your PostgreSQL database, it’s essential to understand the necessity of constant vigilance for its maintenance. This vigilance forms the backbone of a healthy PostgreSQL database. So, how exactly can you achieve this? The answer lies in comprehensive Postgres monitoring.

AWS’ Relational Database Service (RDS) is a pivotal component for businesses leveraging cloud technology for their database management needs. RDS simplifies a relational database’s setup, operation, and scaling, offering cost-efficiency while automating time-consuming administration tasks like hardware provisioning, database setup, patching, and backups. However, despite its benefits, navigating the cost structure of RDS to ensure efficient spending can be a complex task.

In database management, a seemingly silent adversary – database deadlocks – casts a shadow over the operational efficiency of industries. Picture a scenario where critical processes come to a sudden standstill, entangled in a web of deadlocks. This challenge transcends mere technical complexities; it has the potential to disrupt entire operations.

The rise of the digital era has led to increasing concerns among individuals around the protection of their personal data. In 2022, for example, 24% of individuals exercised their right to Data Subject Access Requests, and this rose to 28% in 2023 according to Statista research results.

Discover the power of Dragonfly: Scalability, resilience, and real-time messaging. Uncover the benefits of this cutting-edge in-memory data store solution now! Dragonfly is a performant and memory-efficient drop-in replacement for Redis®, utilizing a modern multi-threaded shared-nothing architecture. That’s it! In essence, we have already captured the ambitious mission of Dragonfly.

Time series data is becoming more prevalent across many industries. Indeed, it is no longer limited to financial data. As the need to handle time-stamped data increases, the demand for specialized databases to handle this type of data has also grown. The solution: Time series databases. In this introduction guide, we'll explain all the basics you need to know about time series databases, including what they are, how they work and are applied, and some of their benefits.

Deploying database code using pipelines in various CI/CD tools is a common practice among our customers who use Flyway. To enhance security and avoid hardcoding sensitive information like connection strings, they frequently employ variables and secrets in the pipelines.