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.

Today we’re happy to announce our new VictoriaMetrics Anomaly Detection solution, which harnesses machine learning to make database alerts more relevant, accurate and actionable for enterprise customers. VictoriaMetrics Anomaly Detection lightens the load on overworked data engineers, focusing their scarce resources on the alerts that matter most to their organization.

Today, many companies generate and store huge amounts of data. To give you an idea, decades ago, the size of the Internet was measured in Terabytes (TB) and now it is measured in Zettabytes (ZB). Relational databases were designed to meet the storage and information management needs of the time. Today we have a new scenario where social networks, IoT devices and Edge Computing generate millions of unstructured and highly variable data.

After many cups of coffee and takeaway pizzas, something changed in the world of SQL Prompt in November 2023. For the first time in SQL Prompt’s history, our engineering team at Redgate brought AI to its breadth of capabilities and called it Prompt+. Using generative AI-powered insights and context-based awareness, Prompt+ takes natural language queries and turns them into SQL coding suggestions.