Recently I read a very informative article by Stephen Catanzano in Tech Target (Avoid data sprawl with a single source of truth). To be honest, this is an age-old challenge, and it's getting worse. IDC states that by 2025 the global datasphere will grow to 175 Zeta bytes and that 90% of the data in the world is a replica. Why does this matter? As Stephen points out in his article, a single source of truth is a fundamental concept in data management.

Almost every organization needs a top-notch monitoring system to make sure everything is in place and operating at its best, given the developments in the cloud and changing software development life cycle (SDLC) processes. Observability is one of the significant areas that can guarantee your system’s health and ensures that the performance is visible and monitored properly.

The practice of DevOps — development operations — has taken organizations by storm. According to a 2021 report by Redgate Software, 74 percent of enterprises surveyed say they now use DevOps in some form or fashion, compared with just 47 percent in 2016. DevOps practitioners seek to improve the software development lifecycle by fostering closer collaboration between developers and IT operations teams.

The performance and resilience of an application are used to measure the speed and scale of an organization. This is one of the key reasons businesses opt to modernize legacy applications using cloud-native technologies and platforms. According to a Gartner report, more than 95% of new digital efforts would be built on cloud-native platforms by 2025, up from less than 40% in 2021.

AIOps is an emerging technology that applies machine learning and analytics techniques to IT operations. AIOps enables IT teams to leverage advanced algorithms to identify performance issues, predict outages, and optimize system performance. Nodesource sees significant advantages for developers and teams to increase software quality by leveraging AIOPS.

Apache Spark has become one of the most popular open-source data processing platforms in the enterprise ecosystem, especially thanks to its ability to handle both batch and real-time data processing on large amounts of data.

DevOps was started more than a decade ago as a movement, not a product or solution category. DevOps offered us a way of collaborating between development and operations teams, using automation and optimization practices to continually accelerate the release of code, measure everything, lower costs, and improve the quality of application delivery to meet customer needs.

Organizations need to manage data across ecosystems, develop data pipelines, APIs, insight into their metadata, and try to make sure that silos and data quality issues are managed effectively. Enter data observability platforms. This blog post looks at what drives many organizations to adopt data observability to ensure the health of your data across systems and providers.

We often find it hard to remember the world we left behind, but cast your mind back, say, 20 years, and we lived in a very different world. Personal Computers and the internet were on the rise, and businesses were all becoming connected. This provided companies with immense opportunities in terms of collaboration and digital adoption, and on the flip side, it eased the distribution of computer viruses. Today we barely even think about our antivirus software and policies.