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.

When cloud service providers first started popping up, many developers were “wowed” by being able to spin up and scale all kinds of infrastructure to deploy their web applications on demand. However, big-box cloud service providers are often complex to use, scaling out is expensive and default monitoring solutions are not very insightful. Besides, we are spoiled developers, and we expect things to be easy.

Monitoring and observability are critical components of modern IT infrastructures, enabling organizations to gain insights into the performance, health, and security of their systems. Agents play a crucial role in gathering and forwarding telemetry from various sources to observability platforms.

Integrating time series data with the power of vector databases opens up a new frontier for analytics and machine learning applications. Time series data, characterized by its sequential order and timestamps, is pivotal in monitoring and forecasting across various domains, from financial markets to IoT devices. InfluxDB, a leading time series database, excels in handling such data with high efficiency and scalability.

You may have heard of test data management (TDM). It’s part of the software delivery process – some would say a crucial part, involving the creation, management, and maintenance of environments for software development and testing. By provisioning fresh, production-like data, it allows developers to test their proposed changes early, thoroughly, and repeatedly with the right test data, when they need it and where they need it.

We recently sat down with James Phillips, CIO at Rev.io, to talk about test data management (TDM) and the growing attention it’s getting from the C-suite. It’s been prompted by the recognition that provisioning test and development environments with realistic production-like data improves the quality of code being developed, reduces errors, and deliver new features to customers faster.