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.

Elasticsearch is primarily a search engine optimized for fast, complex search queries, especially text searches, and is often used for log and event data analysis. MongoDB, on the other hand, is a general-purpose, document-oriented database that excels in storing and retrieving structured and semi-structured data. It is commonly used for mobile, social, and IoT applications. While Elasticsearch provides superior search capabilities, MongoDB offers more robust data processing and storage features.

A large proportion of our customers rely on eG Enterprise to monitor and troubleshoot application and end-user experience problems caused by problems in underlying database dependencies. Our end-to-end unified monitoring and root-cause analysis platform supports all major database technologies. Over recent years, we have witnessed a significant shift from traditional on-premises databases to more dynamic, scalable solutions.