Cloud-Native Tech Status Update Q3 2022

Following our June 2022 post, let’s dive even deeper into the cloud computing (CC) trends we plan to follow in the coming months.

CC 2022 = AI Ops


  • CC Market
  • Key Services
  • Serverless Functions
  • Microservices
  • DevOps CI/CD
  • ML/AI Products
  • IoT Technology
  • Cybersecurity
  • Use-Cases
  • E-Training
  • Events
  • Explore More
  • Infographic
  1. Content
    1. CC Market
    2. Key Services
    3. Serverless Functions
      1. Going Cloud-Native
      2. What Serverless Does Not Mean
      3. Serverless Offerings
    4. Microservices
    5. DevOps CI/CD
    6. ML/AI Products
    7. IoT Technology
  2. Top Cloud IoT Platforms
    1. Cybersecurity
    2. Use-Cases
    3. E-Training
    4. Events
    5. Explore More
    6. Infographic

CC Market

  • Chicago, Aug. 30, 2022 (GLOBE NEWSWIRE) — Cloud Performance Management Market to grow from USD 1.5 billion in 2022 to USD 3.9 billion by 2027, at a Compound Annual Growth Rate (CAGR) of 17.6% during the forecast period, according to a new report by MarketsandMarkets™. 
  • Global cloud services spend up 33% to hit $62.3 billion in Q2 2022. The top three vendors in Q2 2022, namely Amazon Web Services (AWS), Microsoft Azure and Google Cloud, together accounted for 63% of global spending in Q2 2022 and collectively grew 42%.
  • AWS accounted for 31% of total cloud infrastructure services spend in Q2 2022, making it the leading cloud service provider. It grew 33% on an annual basis. Azure was the second largest cloud service provider in Q2, with a 24% market share after growing 40% annually. Google Cloud grew 45% in the latest quarter and accounted for an 8% market share.
  • Canalys VP Alex Smith said: “Cloud remains the strong growth segment in tech. While opportunities abound for providers large and small, the interesting battle remains right at the top between AWS and Microsoft. The race to invest in infrastructure to keep pace with demand will be intense and test the nerves of the companies’ CFOs as both inflation and rising interest rates create cost headwinds.”
  • Both AWS and Microsoft are continuing to roll out infrastructure. AWS has plans to launch 24 availability zones across eight regions, while Microsoft plans to launch 10 new regions over the next year. In both cases, the providers are increasing investment outside of the US as they look to capture global demand and ensure they can provide low-latency and high data sovereignty solutions.

Key Services

Types of CC architecture: public cloud, private cloud, and hybrid cloud.

Why database migration to cloud?

Types of cloud services: IaaS, PaaS, FaaS, and SaaS.

  • With IaaS, you rent IT infrastructure—servers and virtual machines (VMs), storage, networks, operating systems—from a cloud provider on a pay-as-you-go basis.
  • PaaS refers to cloud computing services that supply an on-demand environment for developing, testing, delivering, and managing software applications.
  • Overlapping with PaaS, FaaS focuses on building app functionality without spending time continually managing the servers and infrastructure required to do so. 
  • With SaaS, cloud providers host and manage the software application and underlying infrastructure, and handle any maintenance, like software upgrades and security patching.

IaaS delivered improvements in cost, agility, scalability, and reliability.

Cloud service models: IaaS, PaaS, and SaaS. Courtesy of David Chou.
Courtesy of David Chou
Courtesy of David Chou

The diagram above provides a simplified/generalized view of choices we have from a hosting perspective:

  • On-premises: represents the traditional model of purchasing/licensing and acquiring software, install them, and manage them in our own data centers
  • Hosted: represents the co-location or managed outsourced hosting services. For example, GoGrid, Amazon EC2, etc.
  • Cloud: represents cloud fabric that provides higher-level application containers and services. For example, Google App Engine, Amazon S3/SimpleDB/SQS, etc.

FaaS is a type of serverless cloud computing service that allows executing code in response to events without maintaining the complex infrastructure typically associated with building and launching microservices applications. With FaaS, users manage only functions and data while the cloud provider manages the application. This allows developers to get the functions they need without paying for services when code isn’t running. Some popular FaaS examples include: Amazon’s AWS LambdaGoogle Cloud Functions and Microsoft Azure Functions.

CC = Heart of Global Digital Transformation


GCP Digital transformation Funnel

Serverless Functions

Going Cloud-Native


What Serverless Does Not Mean

What Serveless Does Not Mean

FaaS executes logic in response to events. All methods are grouped into a single deployable unit – a stateless Function. Scaling is handled automatically.

FaaS use cases

Traditional vs Serverless Architecture

Traditional vs Serverless Architecture

Serverless Offerings

Serverless Offerings


Microservices: an architectural pattern that breaks down large application structures into smaller, independent services that are not dependent upon a specific coding language; derived from service-oriented architecture (SOA).

Top 6 microservices patterns:

Top 6 microservices patterns.
Source: MuleSoft

Microservices Use-Cases

Microservices Use-Cases

DevOps CI/CD

Jenkins CI/CD Automation with OpsMX

Jenkins is widely adopted for continuous integration (CI). Many organizations extend Jenkins with scripts and plug-ins to perform continuous delivery (CD) and deployments. Whether deploying to Kubernetes, VM, hybrid or multi-cloud environments, there is a better approach to implementing CD on top of Jenkins than using scripts and manual processes.

Automating CD processes using AI can increase the velocity and accuracy of releases, improve the productivity of DevOps teams and eliminate manual Jenkins deployment scripts. In addition, many organizations face challenges with achieving regulatory and security compliance and performing audits. Automating CD with AI and deploying a central policy engine can help to enforce regulatory and security requirements and enable organizations to conduct audits easily.

Modern CD solutions—those that incorporate AI and include open tool integration layers—can allow developers and DevOps teams to continue using the tools they know and love. Deep toolchain integrations provide real-time insights and diagnostics correlated with pipelines, which speeds approvals, verifications and triage activities. Modernizing your Jenkins CI/CD processing with CD automation offers key capabilities your teams need for software delivery success.

Codefresh vs Jenkins CI/CD tooling:

Codefresh vs Jenkins CI/CD tooling

Codefresh has a bigger scope than Jenkins. In fact, one of the most crucial points of the comparison is that Jenkins is only a Continuous Integration (CI) solution, while Codefresh covers both Continuous Integration and Continuous Delivery.

Cloud Modernization with CircleCI

GKE Autopilot is a new mode of operation in Google Kubernetes Engine (GKE)


designed to reduce operational costs around managing clusters, optimizing production time and driving higher workload availability. As a Google Cloud Platform (GCP) partner, CircleCI makes it easy to integrate CI/CD workflows with GCP and utilize modes of operation like Autopilot.


GitOps = IaC + MRs + CI/CD

  • IaC – GitOps uses a Git repository as the single source of truth for infrastructure

definitions. A Git repository is a .git folder in a project that tracks all changes

made to files in a project over time. Infrastructure as code (IaC) is the practice

of keeping all infrastructure configuration stored as code.

  • MRs – GitOps uses merge requests (MRs) as the change mechanism for all

infrastructure updates. The MR is where teams can collaborate via reviews and

comments and where formal approvals take place. A merge commits to your

master (or trunk) branch and serves as a changelog for auditing

and troubleshooting.

  • CI/CD – GitOps automates infrastructure updates using a Git workflow with

continuous integration and continuous delivery (CI/CD). When new code

is merged, the CI/CD pipeline enacts the change in the environment. Any

configuration drift, such as manual changes or errors, is overwritten by GitOps

automation so the environment converges on the desired state defined in Git.

GitLab uses CI/CD pipelines to manage and implement GitOps automation, but

other forms of automation such as definitions operators can be used as well.

ML/AI Products

AI Ops

Why is AI Cloud important to you?

MLOps vs DevOps:

MLOps is a key aspect of ML engineering that focuses on simplifying and accelerating the process of delivering ML models to production and maintaining and monitoring them. MLOps involves collaboration between different teams including data scientists, DevOps engineers, IT specialists and others.

DevOps combines the concepts of development and operations to describe a collaborative approach to performing the tasks usually associated with separate application development and IT operations teams. 

Technology Deployment StagesDevOpsMLOps
DevelopmentUsually, the code creates an interface or application. 
The code is wrapped into an executable or artifact before being deployed and tested with a set of checks. 
Ideally, this automated cycle will continue until the final product is ready.
The code enables the team to build or train machine learning models. 
The output artifacts include serialized files that can receive data inputs to generate inferences. 
Validation involves checking the trained model’s performance based on the test data.
This cycle should also continue until the model reaches a specified performance threshold.
Version ControlVersion control typically only tracks changes to code and artifacts.
There are few metrics to track.
MLOps pipelines usually have more factors to track. Building and training an ML model involves an iterative experimentation cycle, requiring tracking of various metrics and components for each experiment (essential for later audits). 
Additional components to track include training datasets, model building code and model artifacts. 
Metrics include hyperparameters and model performance indicators, such as error rates.
ReusabilityDevOps pipelines focus on repeatable processes.
Teams can mix and match processes without following a specific workflow.
MLOps pipelines repeatedly apply the same workflows. The common framework across projects helps improve consistency and allows teams to progress faster because they start with familiar processes. 
Project templates offer structure, enabling customization to address the unique requirements of each use case.
Uses centralized data management to consolidate the organization’s data to accelerate the discovery and training processes. Common approaches to centralization include a single source of truth and data warehouses.  
Continuous MonitoringSite reliability engineering (SRE) has been trending over the past few years, emphasizing the need for monitoring software from development through to production deployment. 
The software does not degrade in the way an ML model does. 
Machine learning models can degrade quickly, requiring constant monitoring and updating.
Conditions in the production environment affect the model’s accuracy. After deployment to production, the model starts generating predictions based on new data from the real world. This data is constantly changing and adapting, reducing model performance. 
MLOps ensures that algorithms remain production-ready by incorporating procedures to facilitate continuous monitoring and model retraining.
InfrastructureInfrastructure-as-code (IaC)
Build servers
CI/CD automation tools
Deep learning and machine learning frameworks
Cloud storage for large datasets
GPUs for deep learning and computationally-intensive ML models
DevOps Multi-Cloud Configuration Management (CM)

IoT Technology

The Internet of Things, or IoT, is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.

IoT ETL pipeline = data gathering, processing, analytics and ML

IoT data gathering, processing, analytics and ML

GCP IIoT Streaming Analytics with FaaS

IoT = Device + Gateway + Cloud

IoT = Device + Gateway + Cloud
Industry-4 GCP IoT Workflow

Google Cloud IoT solutions

Top Cloud IoT Platforms

  1. Thingworx 8 IoT Platform
  2. Microsoft Azure IoT Suite
  3. Google Cloud’s IoT Platform
  4. IBM Watson IoT Platform
  5. AWS IoT Platform
  6. Cisco IoT Cloud Connect
  7. Salesforce IoT Cloud
  8. Kaa IoT Platform
  9. Oracle IoT Platform
  10. Thingspeak IoT Platform
  11. GE Predix IoT Platform

Top 3 cloud-native IoT vendors

  • Dynatrace – Monitor the performance, availability, and health of your IoT devices through a single all-in-one platform powered by AI.
  • 2smart – No-code software for bringing smart devices to internet and market.
  • Emnify – Intuitive IoT Monitoring Dashboard.


  • AT&T Managed Threat Detection and Response (MDR) is a sophisticated managed detection and response service that helps you to detect and respond to advanced threats before they impact your business. It builds on the unified security management (USM) platform for threat detection and response, and AT&T Alien Labs™ threat intelligence.
  • Multifactor authentication (MFA) is becoming increasingly standard within software development organizations, with GitHub recently announcing that two-factor authentication (2FA) will be mandatory for all code contributors by the end of 2023.
  • Cloud computing platforms are rife with misconfigurations that cybercriminals regularly exploit. Developers using infrastructure-as-code tools simply lack the cybersecurity expertise required to make sure cloud application environments are secure. It’s up to the cybersecurity team to make sure that the policies and guardrails created to secure cloud platforms are observed, especially when it comes to APIs.
  • The advent of cloud-native, container-based architecture and microservices-based applications running on platforms like Kubernetes has sharpened the focus on API security and the software supply chain—from both security teams and cyberattackers. Software supply chains and APIs have become the new attack surfaces of choice, and with everyone from the White House to entry-level developers talking SBOMs, open source security and APIs, this is an area that’s getting lots of attention. 
  • BrightTalk Webinars of Interest

Cut Through Cybersecurity Complexity by Converging Key Capabilities at the Edge

Through the Eyes of the Wolf: Insights into the Chain of Cyber Threats

5 Signs the World Isn’t Paying Enough Attention to 5G Security

Managing User Identity in a Cloud-First World

How to Launch an Effective Zero Trust Initiative


Stock Markets

ML/AI Stock Forecasting

AI based TSLA stock forecast using AWS Forecast

Legal-as-a-Service (LaaS)

Deloitte Legal as a service 16 AI projects


HealthTech Pilots

HealthTech ML/AI Use-Cases

The Application of ML/AI in Diabetes

AI-Powered Stroke Prediction

HR Automation

GCP architecture - HR chatbot


edX c/o IBM MicroBachelors®

Full Stack Cloud Application Development


Coursera: Containerized Applications on AWS


AWS Startup Showcase
CLoud Storage & Security

If you’re interested in strategies to help 𝗗𝗲𝘁𝗲𝗰𝘁 𝗮𝗻𝗱 𝗣𝗿𝗼𝘁𝗲𝗰𝘁 𝗔𝗴𝗮𝗶𝗻𝘀𝘁 𝗧𝗵𝗿𝗲𝗮𝘁𝘀 in the cloud, this AWS Startup Showcase is for you.

Explore More

Cloud Tech Trends June 2022

Cloud computing in 2025

How AI and Cloud Computing help enterprises scale up in 2022?



Udemy Course: Serveless Concepts

Edge, Cloud, Core: Building an Affordable Hybrid Cloud

Scale, Build, and Manage Kubernetes Deployments


move to cloud database
Cloud Computing Virtualization

Gartner cloud security report
GCP Computing Infrastructure
IIoT Google BigQuery
Comparing Gitlab CI/CD to Jenkins CI
Version 4 of the industry’s most popular DevOps market landscape tool, the Periodic Table of DevOps. Selected Vendors: Snowflake, Moogsoft, Instana, DataDog, GitLab, among others.
Tweets by @xebialabs
Cloud security report 2022 by PaloAlto and PrismaCloud
Key cybersecurity trends Q1 2022


58 responses to “Cloud-Native Tech Status Update Q3 2022”

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