Operations | Monitoring | ITSM | DevOps | Cloud

I find it amazing how much opportunity and flexibility cloud environments are creating for organizations of all sizes. I’m seeing more and more companies experimenting with open-source software (OSS) relational database systems, which years ago would’ve been too complicated for the customer to set up. With Azure, you can spin up OSS systems like MySQL or PostgreSQL quickly to determine if the engine fits their needs. If it does, you can continue development on it.

Redis is an open-sourced, BSD 3 licensed, highly efficient in-memory data store. It is used widely in the industry because of its incredible performance and ease of use. It can easily be used as a distributed, in-memory key-value store, cache, or message broker. It can hold virtually any data structure, making it highly versatile. Redis was architectured and developed with speed in mind and designed to keep all the data in memory.

Azure Cosmos DB for PostgreSQL is a fully managed relational database service for PostgreSQL that is powered by the open source Citus extension. With remote query execution and support for JSON-B, geospatial data, rich indexing, and high-performance scale-out, Cosmos DB for PostgreSQL enables users to build applications on single- or multi-node clusters.

As organizations adopt cloud technologies and modernize their applications, the data they generate and ingest often grows exponentially, leaving them with difficult choices for storing and using this data. Customers are beginning to explore moving away from traditional relational database management systems (RDBMS) because of the data volume to be ingested, as these RDBMS often cannot handle workloads.

For many workloads, using a time series database is a smart choice that saves time and storage space. Developers and companies have more database choices than ever. Choosing the right database for a project saves time when writing and querying data. As companies work with larger datasets to make increasingly intelligent and automated systems, efficiency is key. For many workloads, using a time series database is a smart choice that saves time and storage space.

One of the biggest mistakes I’ve had to repeatedly help companies fix has been poor partitioning design. I’ve seen many database architectures designed in an attempt to make queries faster. While faster queries can be a product of implementing partitioning correctly for a given design, I’ve often seen query response times get much slower from implementing partitioning incorrectly for the database design.

Database management systems are an essential component of business applications. Over the years, MS SQL has earned its place in the hearts of database administrators (DBAs) as the most trusted relational database management system. It is still the go-to choice for many DBAs as it helps them leverage its extensive capabilities across various dimensions such as security, portability, transaction processing and analytics.