CI CD in Azure Synapse Analytics Part 3
In this edition of our series we will create an Artifact / Build pipeline, create a Release pipeline, deploy our Azure Synapse Analytics environment from Dev to QA. Review what was created and what was NOT created. Also pause your SQL Pools if you are not using them.Oracle HA in Azure- Options
A common conversation for bringing Oracle workloads to Azure always surrounds the topic of Real Application Clusters, (RAC). As it’s been quite some time since I’ve covered this topic, I wanted to update from this previous post, as with the cloud and technology, change is constant. One thing that hasn’t changed is my belief RAC is A solution for Oracle for a specific use case and not THE solution for Oracle. The small detail that Oracle won’t support RAC in any third-party cloud is less important than the lack of need for RAC in most cases for those migrating to an enterprise level cloud such as Azure.Build serverless, full stack applications in Azure
Whether you’re new or seasoned to cloud, development, and SQL, building and architecting applications in the cloud has become a required skill for many roles. We recently announced a new learning path to help developers of all skill levels learn how to create applications quickly and effectively with Azure.Bring Vision to Life with Three Horizons, Data Mesh, Data Lakehouse, and Azure Cloud Scale Analytics
Bring Vision to Life with Three Horizons, Data Mesh, Data Lakehouse, and Azure Cloud Scale Analytics – Plus some bonus concepts! I have not posted in a while so this post is loaded with ideas and concepts to think about. I hope you enjoy it! The structure of the post is a chronological perspective of 4 recent events in my life: 1) Camping on the Olympic Peninsula in WA state, 2) Installation of new windows and external doors in my residential house, 3) Injuring my back (includes a metaphor for how things change over time), and 4) Camping at Kayak Point in Stanwood WA (where I finished writing this). Along with these series of events bookended by Camping trips, I also wanted to mention May 1 st which was International Workers Day (celebrated as Labor Day in September in the US and Canada). To reach the vision of digital transformation through cloud scale analytics we need many more workers (Architects, Developers, DBAs, Data Engineers, Data Scientists, Data Analysts, Data Consumers) and the support of many managers and leaders. Leadership is required so analytical systems can become more distributed and properly staffed to scale vs the centralized and small specialist teams that do not scale. Analytics could be a catalyst for employment with the accelerated building and operating of analytical systems. There is evidence that the structure of the teams working on these analytical systems will need to be more distributed to scale to the level of growth required. When focusing on data management, Data Mesh strives to be more distributed, and Data Lakehouse supports distributed architectures better than the analytical systems of the past. I am optimistic that cloud-based analytical systems supported by these distributed concepts can scale and progress to meet the data management, data engineering, data science, data analysis, and data consumer needs and requirements of many organizations.Data Architecture and Designing for Change in the Age of Digital Transformation
Change is constant whether you are designing a new product using the latest design thinking and human-centered product development, or carefully maintaining and managing changes to existing systems, applications, and services. In this post I would like to provide both food for thought related to data architecture and change, as well as provide exposure to a practical analytics accelerator to capture change in data pipelines. Along the way I also want to discuss a couple of terms often referenced in data management and analytics discussions: 1) One Version of the Truth, and 2) Data Swamp. I have never liked either of these terms and will try to explain why realistically these are loaded, misleading, and rather biased terms. Here is the Analytics Accelerator on Change Data Management https://github.com/DataSnowman/ChangeDataCapture
