Recent Discussions
Software Architecture Frameworks A Guide to the Landscape and Their Differences
In the ever-evolving world of software engineering, architecture acts as the blueprint for building robust, scalable, and maintainable systems. But just like in urban planning or civil engineering, different contexts demand different approaches. That’s where software architecture frameworks come into play. They provide standardized methods, principles, and models for describing, designing, and evolving complex software systems. This blog explores some of the most widely recognized software architecture frameworks, how they differ, and how to choose the right one for your project. https://dellenny.com/software-architecture-frameworks-a-guide-to-the-landscape-and-their-differences/Software Architecture Frameworks and Artificial Intelligence Building Smarter Systems
The rise of Artificial Intelligence (AI) is transforming how we think about software systems. It’s no longer enough for applications to just work—they must learn, adapt, and scale in ways that traditional architectures weren’t originally designed for. This is where software architecture frameworks intersect with AI, providing structure, governance, and adaptability to increasingly intelligent systems. https://dellenny.com/software-architecture-frameworks-and-artificial-intelligence-building-smarter-systems/MLOps Architectures Building Scalable AI Systems
Artificial intelligence is no longer just about building models in a research environment. To create real-world impact, machine learning (ML) models must be deployed, monitored, and continuously improved in production. That’s where MLOps (Machine Learning Operations) comes in. Much like DevOps revolutionized software delivery, MLOps focuses on streamlining the end-to-end lifecycle of ML systems—from data ingestion to model deployment, monitoring, and retraining. At the heart of any successful MLOps implementation lies its architecture. The architecture defines how data, models, infrastructure, and teams interact to support scalable, reliable, and reproducible ML workflows. https://dellenny.com/mlops-architectures-building-scalable-ai-systems/
Setting Up SCD Type 2 using "Variable to set date values"
I am creating an SCD type 2 dimension and using start and end dates to identify current and expired records. In my destination table, there are start date and end date columns. I am unable to select the variable to set date values in the SSIS package. When I click in the "Variable to set date values" box, I don't see a drop-down to select my system variables from. I want to use the System::ContainerStartTime. Do you think this is something I need to configure in the SSIS package? If so, how?Automating Geneva MDM & MDS queries
Here in Hybrid Networking we need to implement data collection and management for network MDM metrics and MDS log data that extend what is available in Geneva. For Data Science projects we need to automate scraping selected metrics & logs to create longitudinal datasets suitable for detailed model building. As such we have some adhoc connectors using available C# APIs to MDM / MDS to periodically scrape data and archive it in ADL2, to make it accessible for processing in SPARK. However, a standard solution would be preferable. The ADL2 - SPARK (e.g. DataBricks) combination I believe has clear advantages over use of Cosmos (not CosmosDB, but the legacy batch job system) for which a wider range of connectors are available. Has anyone else implemented automation tools for such a data pipeline?
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