Operations | Monitoring | ITSM | DevOps | Cloud

As organizations move their workloads into the public cloud, their environments gradually evolve. Initially, an organization tends to aim for a 1:1 migration of resources, typically in the form of virtual machines (VMs). Although this approach allows for an easier data migration path, it’s more costly and offers less flexibility than taking advantage of some of the serverless and Platform-as-a-Service (PaaS) offerings in the public cloud.

Since schools transitioned to remote learning, districts have added or upgraded their IT education technology and monitoring tools to provide better outcomes for all. But it’s not necessarily a good thing. The saturation of edtech tools may be leading to wasted money, inefficiencies, and missed opportunities. In 2020, the deployment of edtech tools in schools increased by nearly 90% year-over-year.

Some of the best-selling books of all time are self-help books. Reading about the potential for change always intrigues people enough to buy the book. To succeed in enacting positive change, you must do just that—change. People’s habits are familiar and safe, and change requires effort down an unknown path. People aren’t sure of the outcome, and as a result, usually stick with the proven method—even if it’s what they’re trying to change.

Many organizations have migrated their environments from on-premises to the cloud, and one of the clouds of choice is Microsoft Azure. For SQL Server workloads, organizations can use platform as a service solutions with features like Azure SQL Database and Managed Instance or infrastructure as a service solutions with Azure SQL virtual machines. Azure SQL virtual machines are an easy target for organizations migrating to Azure due to the simplicity of the migration.

As organizations marshal their data to drive business value throughout the enterprise, it’s becoming more important than ever to ensure the quality of this data. Building data quality, data validation testing, and aligning the data with your organization’s master data (such as products, customers, assets, and locations) is critically important to ensure your reporting is consistent and accurate.

Am I the only one who thinks blogs were in their prime in the mid-2010s, before the internet began consuming content at wicked fast rates (thanks, Instagram and TikTok)? When someone says the word blog, I think of the chokehold 2015 southern American female fashion bloggers had on the internet. Pure pumpkin spice latte fashion. And though those blogs have, probably for the best, slipped away from the limelight, blogs are still alive and well.

The MongoDB _id field is fundamental to every collection in MongoDB, and, by default, it has some useful properties users can take advantage of when familiar with how _id is generated. Understanding the field's default behavior and the advantages and pitfalls associated with it can be useful for managing your collections as well as understanding when to override it.

In today’s world of heterogeneous data ecosystems, managing and consuming data can be cumbersome. Organizations often have multiple systems of truth in corresponding to the applications managing the data. While data engineers dream of software that would make it easy to consume and digest different data streams from disparate systems, that scenario rarely comes to fruition.

Logging is a fundamental part of software development. While an app is being developed, we rely on logging to confirm our inputs and outputs match our expectations. In production, logging can be an invaluable resource for tracking down bugs or measuring how users interact with the app. We can also consider logs as a sort of time-series value, where a timestamp is associated with a user’s specific action. These logs can be structured, gathered, and analyzed to provide teams with more information.