Operations | Monitoring | ITSM | DevOps | Cloud

Data copying and migration are essential database tasks that arise frequently. Fortunately, many tools are available to assist database users in performing these tasks, with dbForge Studio for MySQL standing out as one of the most versatile and reliable solutions on the market. This article explores the data migration options offered by dbForge Studio for MySQL, highlighting how they allow users to achieve their goals quickly and efficiently.

Big tech companies are betting big on nuclear energy to keep their data centers running. With AI driving up energy use worldwide, it makes you wonder—could nuclear energy be the key to tackling the climate crisis caused by AI? Companies like Google and Microsoft have set ambitious sustainability goals by promising to move to 100% renewable energy before 2030. However, generative AI has made their goals difficult by increasing their electricity consumption multifold.

Just like in traditional software development, creating AI applications isn’t a one size fits all approach. However, many of the challenges and concerns facing AI/ML development teams share common threads – difficulties getting models to production, tangled infrastructure, data quality, security issues, and so on.

If your business relies on data for decision-making, you'll know how important data cleansing is. But it's not just a key part of gaining accurate and reliable insights — it's also important for security. We'll look at what data cleansing is, how it relates to vulnerability monitoring, and how to get started.

In the digital era, state and local governments are increasingly tasked with managing vast volumes of data while ensuring compliance with stringent regulatory requirements. These regulations, which can vary significantly depending on jurisdiction, often require the retention of data for extended periods — sometimes ranging from one to seven years.

Money makes the world go around and this has never been more true when it comes to the data economy as innovative start-ups seek investment to achieve their ambitions and unlock their potential. Led by insight from Atomico and Realm, this piece - the fifth in our data economy series - will look at the data economy from an investment perspective - what traits do they look for in data-driven companies? How are start-ups approaching it? These are the questions this piece will answer.

In modern application development, changes happen constantly: Deployments are pushed, feature flags are toggled, and Kubernetes events reshape infrastructure, to name just a few. While these practices drive innovation and scalability, they also introduce complexity—especially during incidents. Fragmented tools and workflows across teams and organizations make it difficult to pinpoint the root causes of issues, leading to longer resolution times.

FinOps, has become a critical element within companies that want to improve their financial aspect related with cloud. One of the key points in this practice is data ingestion that helps companies gather critical information about their cloud spending. In this guide, we will discuss what data ingestion is in FinOps, its need, recommendations, problems, and how we can contribute at Turbo360.

Data indexing and search are essential for quickly retrieving relevant information from large datasets. They help improve efficiency, save time, and support better decision-making by making data easily accessible. Elasticsearch has been a popular choice for data indexing and search, but newer tools like Quickwit now offer alternatives for specific needs. The right choice depends on performance, scalability, cost, and how well it fits your use case.