TLDR for new technologies (for architects that have little time)
Everyday there is new technology coming up and we don’t have much time to digest. Since you don’t have time, if you scan through the description, often you are led to believe the technology can do something that it does not do. When you design solutions, you may choose wrong technology, which is frustrating. Especially big cloud vendors have lot of technologies that have overlapping functionalities on surface. How can we describe new technologies accurately in short time? This article provides a quick recap of some new technologies. Currently the focus is on cloud technologies.
Let’s take an example, Azure stack, which is Microsoft Azure in your datacenter. However, now there are Azure stack Edge, Azure stack HCI, Azure stack Hub. How are they related to previous Azure stack and what are the differences?
Azure
Azure stack
Confusion: what is the difference between 3 offerings in Azure stack portfolio?
Clarification
Azure stack Hub: the traditional Azure stack, Microsoft Azure in your datacenter
Azure stack HCI: used in Branch, rebranding of Windows Server 2019, offering hyper-converged infrastructure (computing, storage, HyperV)
Azure stack edge: used at the edge (not datacenter)
Azure Edge Zones
Take Azure experience closer to customer
Azure Edge Zones: Azure-managed, but data center is located in population centers, e.g. NY, CA, etc. (typical Azure data centers are located far away from metropolitan area)
Azure Edge Zones with Carrier: placed in mobile operators’ datacenters in population centers, 5G-enabled
Azure Private Edge Zones: powered by Azure stack edge, deployed on-premise, run private mobile networks (private LTE, private 5G). Private mobile networks (not WiFi/802.11) are the future for private networks
Azure Synapse Analytics
Confusion: just rebranding of Azure SQL Data Warehouse
Clarification
It is Azure SQL Data Warehouse (like AWS Redshift, MPP SQL, Provisioned SQL pool) + Azure Databricks (Spark)+ AWS Athena (serverless/on-demand SQL)
Virtual WAN
Provides large-scale site-to-site connectivity and is built for throughput, scalability, and ease of use. A virtual network gateway VPN is limited to 30 tunnels. For connections, you should use Virtual WAN for large-scale VPN. You can connect up to 1,000 branch connections per Virtual Hub with aggregate of 20 Gbps per Hub.
AWS
AWS Local Zones
Local Zones: similar to Azure Edge Zones
AWS Wavelength: similar to Azure Edge Zones with carrier
AWS Proton
Assumes a central platform team defining/managing service template incorporating security, scaling, observability best practice, application team using them, suitable for large organization that need to centrally manage service template
Industrial AutoML
Amazon Lookout for Vision: Defect Detection for Manufacturing
Amazon Lookout for Equipment: anomaly detection for equipment, for customers that have existing sensors
Amazon Monitron: for customers that do not have existing sensors
Amazon Lookout for Metrics: general Anomaly Detection Service
AppSync: managed service to develop GraphQL APIs
App Runner: similar to GCP Cloud run, quickly deploy containerized web applications and APIs
AWS X-Ray: distributed tracing
Amazon Quantum Ledger Database: centralized ledger database
AWS Panorama: computer vision at the edge, like Azure Vision AI devkit appliance + service
AWS Glue DataBrew: visual data preparation tool
AWS Aurora global database: don’t think you can write data from anywhere and expect strong consistency. Main idea about Global Database is that it uses storage-based replication with typical latency of less than 1 second. The difference vs read replica is that read replica should use snapshot/restore technology, which is slower
GoogleCloud Platform
GKE Autopilot: it is a mode of GKE (vs. Standard mode which provides more flexibility and management overhead), lets user focus on actual workload (pod spec), not the underlying infrastructure of Kubernetes
Cloud run: with GKE autopilot, will cloud run run on GKE autopilot to make more knative-native?
Vertex AI: rebranded AI platform, with new services like Data Labeling, Feature store, ML Metadata, Hyperparameter tuning, Explainable AI, Edge manager (experimental), Model monitor (drift detection). Google rebrands its AI platform frequently ( ML engine, AI hub, AI platform)
Full-stack web
Azure App Service, AWS Amplify, GoogleCloud Platform Firebase
Migration services
Azure Migrate: server migration, lift and shift
Azure Database migration service: specific for database (to Azure Database, Azure SQL Managed Instance, Azure Cosmos DB)
