Operations | Monitoring | ITSM | DevOps | Cloud

With the huge growth in volume and complexity, data management has become a key priority in most Enterprises. But for this data to be utilised in a meaningful way, how do you tackle the added complexity of controlling access across both technical and business departments? Our CPO, David Gummer recently interviewed the CEO of the popular Universal Database management tool, DBeaver, Tatiana Krupenya, to discuss the often-feared topic of sharing access to the database. Prefer video? Watch the interview here.

More and more organizations are migrating their data to the cloud but the majority are likely to end up managing hybrid estates, with some data managed through a cloud provider and some managed locally. This leads to some unique issues, especially around monitoring and services. If the objective of migrating to the cloud is simply to implement virtual machines (VMs) in a different location than you traditionally do, there will be fewer challenges.

The SPACE framework is an acronym for an approach to measuring, understanding, and optimizing engineering productivity. Outlined by researchers from GitHub, Microsoft, and the University of Victoria, it encourages leaders to look at productivity holistically, placing leading metrics in context with each other and linking them to output, often team goals, rather than individual effort. The SPACE framework breaks productivity into five metrics.

Over the last decade, I’ve rarely met a data professional whose organization wasn’t experiencing data growth and making more demands of their data. We build and deploy new applications faster than we retire old ones, and new data is accumulating dramatically faster on our existing systems than our ability to decide to delete older information. Additionally, the ever-growing number of users and devices interacting with that data increases the strain on the infrastructure underpinning it.

In a series of blog posts about AI, I’ve been looking at how intelligent ChatGPT is, how good ChatGPT and Bing are when you employ them as a technology writer, and how the engineering team at Redgate is using GitHub Copilot to aid with writing code. Now it’s time to take a look at image creation tools, and where better to start than Andy Warhol? I like Andy Warhol.

The rise in popularity of open-source RBDMs has encouraged many organizations to adopt PostgreSQL, but as a DBA or Developer, it can be challenging when exploring new database platforms, no matter how experienced you are. When looking at SQL Server, it has many similarities to PostgreSQL, but there are several big differences too.

As the size and complexity of database estates increases, with more workloads and data being hosted on more platforms, both on-premises and in the cloud, so the appeal of third-party database monitoring tools has also grown. Their ability to provide a holistic view of an entire estate and monitor multiple databases and platforms from a single dashboard has been shown to save DBAs and IT teams many hours of time when compared to home-grown solutions.

The last few years have seen a big change in the size, make-up, and nature of database estates. Data is growing both in volume and complexity, it is now normal to have workloads as well as data in multiple public clouds, and organizations are increasingly using different kinds of databases for different use cases.

Migrating to the cloud has many advantages … until you’re the one tasked with making it happen. For many data professionals, this will be a first step from the traditional, predictable challenges of managing on-premises servers to the many and varied databases and platforms in the cloud. As many have discovered, it’s not simply a case of ‘lift and shift’.

There’s been a lot of talk about GitHub Copilot recently, loudly touted as Your AI pair programmer. According to GitHub, Copilot for Business brings the power of generative AI to engineering teams, accelerating the speed of software development and innovation. At the back end is OpenAI Codex, a modified version of the GPT-3 Large Language Model (LLM) used in ChatGPT. At the front end it integrates with code editors like Visual Studio and JetBrains to automatically generate code.