Compared: Microservices with AWS Lambda and Azure Service Fabric

From the deck on Microservices with AWS Lambda and Azure Service Fabric by Paul Affentranger

AWS Lambda Azure Service Fabric
Features • Functional approach
• Stateless
• “Serverless” compute service
• Runs code on events
• Extends AWS services
• Provides back-end service
• High-availability
• Built-in Fault Tolerance
• Automatic scaling
• Integrated Security Model
• Java, Node.js, and Python		 • Massively scalable
• Self-healing
• Programmming in .NET, Java or node.js.
• Stateless and stateful
• Actor Model
• Highly reliable.
• Simplify by stateful microservices in place of caches
• Deploy to Azure or to on-premises clouds
• Running on Windows or Linux
• Develop with a "datacenter on your machine"
• Deploy support
• Applications at higher density
• Different Versions side by side
• Manage lifecycle without downtime
• Upgrade independently
• Monitor and diagnose
• Scale up or scale down your cluster Azure Service Fabric
Philosophy Functional More OO
State Stateless Stateless & Stateful
Persistence External Integrated
Reliable Messaging External Integrated
Languages Java, Node.js and Python Java, Node.js and .NET
Environments AWS only Azure, Azure Stack, Windows and Linux (Cloud and On-premises)
Use General Opinionated (for Microservices)
Deployment External Tool Integrated

What they have in common:
  • High-availability
  • Built-in Fault Tolerance
  • Automatic scaling
  • Integrated Security
  • Monitoring and logging

Comments

Post a Comment

Popular posts from this blog

Maven Crash Course - Learn Power Query, Power Pivot & DAX in 15 Minutes

Image
In the Maven Crash Course " Learn Power Query, Power Pivot & DAX in 15 Minutes ", Chris Dutton walks through a hypothetical business case while showcasing the power of modern Excel tools like Power Query, Power Pivot and DAX. Power Query is used to extract, transform, and load data from external sources like SQL databases, PDF and CSV files. A relational model is created to join the tables without writing a single formula. Exploratory analysis is done using Power Pivot and Data Analysis Expressions (DAX). Data Analysis Expressions is the language that we can use to define new calculated columns and measures to enhance our Data Model instead of the traditional cell formulas that we'd typically use if our data was stored in worksheets. Pivot Charts and slicers are used to design a simple interactive report that the sales managers can use to analyze revenue trends and product performance.
Read more

"Data Prep & Exploratory Data Analysis" course by Maven Analytics

Image
The Maven Analytics course on " Data Prep & Exploratory Data Analysis " by Alice Zhao is a beginner-friendly introduction to machine learning with Python. It covers the key steps of the data science workflow, including: Gathering Data Scoping a Project Modeling Data Sharing Insights Cleaning Data Exploring Data Modeling Data  Sharing Insights The presentation deck which is available as a download crisply summarizes the essential details and is a great reference to keep going back to. The course is well-suited for aspiring data scientists looking to get hands-on experience with the data prep and EDA stages of the machine learning workflow using Python and Jupyter Notebook. 
Read more

Oracle Cloud Infrastructure 2024 Generative AI Professional Course & Certification Exam (1Z0-1127-24)

Image
I was happy to learn about the Oracle Cloud Infrastructure 2024 Generative AI Professional Course with video tutorials and the Certification Exam (1Z0-1127-24) from their newsletter. I found it extremely useful, timely, and relevant. The course, with about 6 hours of videos, provided me with a comprehensive view of what it takes to build a Generative AI application. Although some parts reference OCI services, much of the course remains vendor neutral. Rohit Rahi and his team at Oracle have done a great job of structuring the beginner-friendly yet comprehensive course so well, covering all the essentials across four modules. The curriculum strikes an excellent balance between depth and accessibility.  While Gen AI output is typically non-deterministic, I was surprised to learn that they can be made completely deterministic under certain conditions. Specifically, in the OCI Gen AI service, setting the temperature parameter to 0 produces consistent, deterministic output for a g...
Read more