Operations | Monitoring | ITSM | DevOps | Cloud

Today’s cloud environments rely on microservices, service meshes, containers, and orchestration tools and are too complex for traditional tools to measure and monitor performance metrics effectively. The number of interdependent services—and the inherently ephemeral nature of cloud workloads—make it challenging to identify which metrics to monitor and issues to troubleshoot down to the root cause.

As part of President Biden’s $2 trillion infrastructure investment plan, the administration had committed $180 billion to “ R&D and industries of the future.” It’s been made clear if the United States wants to stay ahead of the game, quantum computing must be a part of these investments and integrated into agency infrastructures.

Many organizations invest in high availability and disaster recovery for their key applications. Too many of these organizations, however, forego the most important aspect of this process—testing the failover process regularly. Whether gripped by the fear of downtime or dreaded DNS problems, development teams are frequently hesitant to test out what they’ve built in the real world.

AWS is a comprehensive, evolving cloud computing program built to provide IT infrastructure for businesses, and the program has an active user base of roughly 1,000,000 customers. With the massive responsibility of providing services for such a large customer base, it’s important for their services to be reliable and trustworthy.

Have you ever dealt with an application workload suddenly increasing and the demand on SQL Server begins to increase exponentially as a result? Maybe you’ve never had one of those “my solution to X problem just went viral, and everybody’s signing up” moments, but inevitably, at some point in your career, you’ll have to deal with a scalability limitation affecting performance and the business will look to you for a solution.

Open-source software (OSS) relational database management systems (RDBMSes) are becoming incredibly popular in the cloud computing world. In this article, I’ll discuss one of the most popular OSS relational databases, PostgreSQL, and the options you have for this database in the Microsoft Azure cloud. It’s worth noting these products are large with many detailed aspects, so consider this article a cursory, high-level overview of each offering.

Open-source software (OSS) relational database management systems (RDBMSes) are becoming incredibly popular in the cloud computing world. In this article, I’ll discuss one of the most popular OSS relational databases, PostgreSQL, and the options you have for this database in the Microsoft Azure cloud. It’s worth noting these products are large with many detailed aspects, so consider this article a cursory, high-level overview of each offering.

As I navigate further into my career, I’m finding the scope of my role has shifted over the years. I thought I’d take some time to help relay the differences I’ve seen between traditional database administrators (DBAs), database reliability engineers (DBREs), and site reliability engineers (SREs). Before I start, I want to get a disclaimer out of the way: some of the comparisons here reflect only what I’ve seen and may not match what you’ve experienced.

One of my favorite consulting questions to ask when dealing with a scalability problem is, “If you could change the system design with the knowledge you have today, what would/wouldn’t you change and why?” Sometimes it’s best to ask this question on a one-on-one basis with different developers, DBAs, report writers, and architects to get honest answers that aren’t intimidated by the other people sitting in a meeting or to avoid potential debating and arguing over a solution.

The enterprise data warehouse (EDW) provides access to business data stored in a denormalized structure called a star schema, which makes it easy to analyze, visualize, and forecast essential business metrics. Business intelligence (BI) reporting makes use of the data warehouse for real-time self-service dashboards, custom visualizations, data marts, decision-making reports, and machine learning (ML).