I have always had a special fondness for Postgres, particularly captivated by its JSONB capabilities, which is at the core of what I love as it stands as a testament to its adaptability. This functionality exemplifies Postgres’s ability to adapt to modern apps’ rapidly changing needs to support structured and unstructured data to coexist seamlessly within the same database environment. The capabilities of PostgreSQL are not limited to its ability to manage relational and non-relational data.

In the fast-paced world of software engineering, efficient data management is a cornerstone of success. Imagine you’re working with streams of data that not only require rapid analysis but also need to store that data for long-term insights. This is where the powerful duo of time series databases (TSDBs) and data lakes can help.

Until recently, the default model for application development was SQL. However, in recent years NoSQL has become a popular alternative. The wide variety of data that is stored today and the workload that servers must support force developers to consider other more flexible and scalable options. NoSQL databases provide agile development and ease of adapting to changes. Even so, they cannot be considered as a replacement for SQL nor are they the most successful choice for all types of projects.

Effective memory management is crucial for optimal MongoDB performance and helps ensure seamless database operations and user experience. Allocating enough memory lets the database store frequently used data and indexes in RAM and cut down on disk I/O operations. This boosts query response times and system responsiveness. Poor memory management can cause delays in retrieving data from disk, leading to performance degradation.

MongoDB’s flexibility and document-oriented nature have always stood out to me as its most compelling features, setting it apart from the strict schema constraints of traditional relational databases. This adaptability is a boon for application development, allowing for more dynamic data interactions that mirror real-world information complexities and freeing table schemas’ constraints.

Monitoring can get expensive due to the huge quantities of data that need to be processed. In this blog post, you’ll learn the best ways to store and process monitoring metrics to reduce your costs, and how VictoriaMetrics can help. This blog post will only cover open-source solutions. VictoriaMetrics is proudly open source. You’ll get the most out of this blog post if you are familiar with Prometheus, Thanos, Mimir or VictoriaMetrics.

Database Administrators (DBAs) rely on time series data every day, even if they don’t think of time series data as a unique data type. They rely on metrics such as CPU usage, memory utilization, and query response times to monitor and optimize databases. These metrics inherently have a time component, making them time series data. However, traditional databases aren’t specifically designed to handle the unique characteristics and workloads associated with time series data.

Financial institutions handle vast amounts of sensitive and confidential data, including customer information, transaction details, and regulatory compliance records. A trusted database ensures the security and privacy of this sensitive information, protecting it from unauthorised access, breaches, or cyber threats. MongoDB is the ideal fit, and it’s one of the most widely used databases in the financial services industry. It provides a sturdy, adaptable and trustworthy foundation.

We recently launched the State of the Database Landscape 2024 survey results, with information from almost 4,000 database professionals from around the globe. A clear picture emerged from the results, suggesting that 2024 is the year that skill diversification among database professionals is imperative. There’s the need to manage multiple databases, to migrate to the cloud, to introduce continuous delivery with DevOps, and even incorporating Generative AI into the mix.

Azure Cosmos DB, a global, multi-model database by Microsoft Azure, ensures globally responsive and scalable applications with low-latency, high-throughput data access. With support for diverse data models, global distribution, flexible consistency models, automatic scaling, and comprehensive SLAs, it’s crucial for modern applications requiring agility, security, and compliance.