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20
Dec, 2022

Agile Vs. Waterfall: Which Project Management Methodology Is Best For Your Team?

Agile and Waterfall are two well-known project management methodologies. Both of them are popular in software development but each is best suited for different types of projects. The main difference is that Waterfall is a linear system of working that requires the team to complete each project phase before moving on to the next one while Agile encourages the team to work simultaneously on different phases of the project.

Agile Methodology

Agile methodology was developed as a response to Waterfall’s more rigid structure. As a result, it’s a much more fluid form of project management. A software development project can take years to complete, and technology can change significantly during that time. Agile was developed as a flexible method that welcomes incorporating changes of direction even late in the process, as well as accounting for stakeholders’ feedback throughout the process.

In Agile, the team will work on phases of the project concurrently, often with short-term deadlines. Additionally, the team, rather than a project manager, drives the project’s direction. This can empower the team to be motivated and more productive, but also requires a more self-directed team.

Pros
Cons
Scrum can help teams complete project deliverables quickly and efficiently
Scrum often leads to scope creep, due to the lack of a definite end-date
Scrum ensures effective use of time and money
The chances of project failure are high if individuals aren't very committed or cooperative
Large projects are divided into easily manageable sprints
Adopting the Scrum framework in large teams is challenging
Developments are coded and tested during the sprint review
The framework can be successful only with experienced team members
Works well for fast-moving development projects
Daily meetings sometimes frustrate team members
The team gets clear visibility through scrum meetings
If any team member leaves in the middle of a project, it can have a huge negative impact on the project. Quality is hard to implement until the team goes through an aggressive testing process

Waterfall Methodology

Waterfall methodology is a linear form of project management ideal for projects where the end result is clearly established from the beginning of the project. The expectations for the project and the deliverables of each stage are clear and are required in order to progress to the next phase.

Pros
Cons
Uses clear structure
Makes changes difficult
Determines the end goal early
Excludes the client and/or end user
Transfers information well
Delays testing until after completion

Agile and Waterfall Comparison
Waterfall
Agile
Timeline
Waterfall has a fixed timeline. The idea is that the start and finish of the project are already mapped out from the beginning.
Agile is a lot more flexible and accounts for experimenting with different directions. Rather than a fixed timeline, the schedule adapts as the project progresses. The Agile Manifesto, an online document released in 2001 by a group of software developers, says team members are expected to, “​​Deliver working software frequently, from a couple of weeks to a couple of months, with a preference to the shorter timescale.”
Client Involvement
Once the end goal is established, Waterfall does not involve the client or project owner during the process, apart from specific check-ins or for deliverables. The course of the project is outlined from the start, so incorporating client feedback is not an ongoing part of the process.
A fundamental part of Agile is including clients in the project development at every step. The Agile Manifesto states, “Our highest priority is to satisfy the customer through early and continuous delivery of valuable software.” Therefore, business owners are expected to be involved and give feedback to the software development team as they progress through the different phases of the project.
Flexibility
Waterfall is not as flexible as Agile because each phase needs to be fully completed before moving on to the next phase. The project is also planned out ahead of time, making this management system ideal for teams with a clear vision of where they are headed from start to finish.
Flexibility is built into the Agile method. Agile values short bursts of work, which are called sprints. The method welcomes adapting to different directions, incorporating new information even at a later stage of the project.
Budget
Fixed. The budget for projects using the Waterfall methodology is generally fixed. Because the project is determined from start to finish, there is less room to change the budget mid-project.
Flexible. Agile is open to adaptation, encourages experimentation and welcomes changes of direction, even in later phases of the project. Because of this, the budget tends to be more flexible.

Which project management methodology should you use?

Unsurprisingly, the answer to this question depends on your unique team and its aims. To help you decide, ask yourself these two questions.

What goals do I have for my team?

While each methodology has the same goal of project completion, their secondary aims make them truly distinct. Your goals can help you decide which methodology is the best fit for you.

Determine what you want most for your team. If you simply want to produce work faster, try Scrum. If you want to improve your production process, use Kanban. If your projects demand a linear workflow, implement Waterfall. If you’re not sure, explore other Agile options and ask yourself the next question.

Which methodology will we actually stick to?

The differences in project management methodologies only matter if you use the methodology consistently. Without WIP limits, for example, Kanban is just another complicated Agile methodology. And if you don’t keep your phases discrete when using Waterfall, then you might as well just use an Agile methodology.

As such, the best project management methodology for your team is the one you’ll execute perfectly. Using piecemeal parts of a methodology will only make you lose out on the benefits that popularized the methodology in the first place. So while you certainly can adapt methodologies for your team’s use, it’s best to use a methodology as intended, adjusting only as necessary.

16
Dec, 2022

What Is Fintech? What you need to know about Fintech before it explodes in 2023

Fintech, a combination of the terms “financial” and “technology,” refers to businesses that use technology to enhance or automate financial services and processes. The term encompasses a rapidly growing industry that serves the interests of both consumers and businesses in multiple ways. From mobile banking and insurance to cryptocurrency and investment apps, fintech has a seemingly endless array of applications.

Today, the fintech industry is huge. And if recent venture capital investments in fintech startups — which reached an all-time high in 2021 — can be considered a vote of confidence, the industry will continue to expand for years to come.

One driving factor is that many traditional banks are supporters and adopters of newfangled fintech, actively investing in, acquiring or partnering with fintech startups. Those are ways for established banking institutions to give digitally minded customers what they want, while also moving the industry forward and staying relevant.

How Does Fintech Work?

The inner workings of financial technology products and services vary. Some of the newest advancements utilize machine learning algorithms, blockchain and data science to do everything from process credit risks to run hedge funds. There’s even an entire subset of regulatory technology dubbed regtech, designed to navigate the complex world of compliance and regulatory issues of industries like — you guessed it — fintech.

As fintech has grown, so have concerns regarding cybersecurity in the fintech industry. The massive growth of fintech companies and marketplaces on a global scale has led to increased exposure of vulnerabilities in fintech infrastructure while making it a target for cybercriminal attacks. Luckily, technology continues to evolve to minimize existing fraud risks and mitigate threats that continue to emerge.

Types of Fintech Companies

Mobile Banking

Mobile banking refers to the use of a mobile device to carry out financial transactions. The service is provided by some financial institutions, especially banks. Mobile banking enables clients and users to carry out various transactions, which may vary depending on the institution.

Mobile banking services can be categorized into the following:

1. Account information access

Account information access allows clients to view their account balances and statements by requesting a mini account statement, review transactional and account history, keep track of their term deposits, review and view loan or card statements, access investment statements (equity or mutual funds), and for some institutions, management of insurance policies.

2. Transactions

Transactional services enable clients to transfer funds to accounts at the same institution or other institutions, perform self-account transfers, pay third parties (such as bill payments), and make purchases in collaboration with other applications or prepaid service providers.

3. Investments

Investment management services enable clients to manage their portfolios or get a real-time view of their investment portfolios (term-deposits, etc.)

4. Support services

Support services enable clients to check on the status of their requests for loan or credit facilities, follow up on their card requests, and locate ATMs.

5. Content and news

Content services provide news related to finance and the latest offers by the bank or institution.

Challenges Associated With Mobile Banking

Some of the challenges associated with mobile banking include (but are not limited to):

  • Accessibility based on the type of handset being used
  • Security concerns
  • Reliability and scalability
  • Personalization ability
  • Application distribution
  • Upgrade synchronization abilities

Cryptocurrency Fintech

Of course, one of the biggest examples of fintech in action is cryptocurrency. Cryptocurrency exchanges have grown significantly over the past few years. They connect users to financial markets, allowing them to buy and sell different types of cryptocurrencies. Furthermore, cryptocurrency uses blockchain technology, which has become popular throughout the industry. Because of the security provided by blockchain technology, it can help people reduce fraud. That increases people’s confidence in the financial markets, further expanding cryptocurrency and all companies that use blockchain technology.

Right now, it is difficult to say what the future of fintech and crypto will look like. The only certainty is that it will play a major role in the business world moving forward. Cryptocurrency itself has contributed to the development of numerous new technologies, including blockchain technology and cybersecurity, that will be foundational to financial markets in the future.

Fintech Investment and Savings

One such new trend has been rising interest in savings and investing applications, the type of service fintech startups offer consumers. TechCrunch has covered this trend, noting a number of American fintech and finservices seeing hugely rising user activity and revenue.

Robinhood, the best-known American zero-cost trading app, has seen its trading volume skyrocket along with new user signups. Research into the company’s filings show that its revenue grew to over $90 million in the period as its income from more exotic investments like options advanced.

The trend of growing consumer interest in saving money (reasonable during an economic crisis) and investing (intelligent when equity prices fell off a cliff in March and April) has helped smaller fintech startups as well. Personal finance platform M1 Finance and Public, a rival zero-cost stock trading service, have also seen growing demand. The trend is so pronounced that new stories seem to crop up every few days concerning yet another savings or investing fintech that is blowing up, like this recent piece concerning Current.

 

Machine Learning and Trading

Being able to predict where markets are headed is the Holy Grail of finance. With billions of dollars to be made, it’s no surprise that machine learning has played an increasingly important role in fintech — and in trading specifically. The power of this AI subset in finance lies in its ability to run massive amounts of data through algorithms designed to spot trends and risks, allowing consumers, companies, banks and additional organizations to have a more informed understanding of investment and purchasing risks earlier on in the process.

Payment Fintech

Moving money around is something fintech is very good at. The phrase “I’ll Venmo you” or “I’ll CashApp you” is now a replacement for “I’ll pay you later.” These are, of course, go-to mobile payment platforms. Payment companies have changed the way we all do business. It’s easier than ever to send money digitally anywhere in the world. In addition to Venmo and Cash App, popular payment companies include Zelle, Paypal, Stripe and Square.

Fintech Lending

Fintech is also overhauling credit by streamlining risk assessment, speeding up approval processes and making access easier. Billions of people around the world can now apply for a loan on their mobile devices, and new data points and risk modeling capabilities are expanding credit to underserved populations. Additionally, consumers can request credit reports multiple times a year without dinging their score, making the entire backend of the lending world more transparent for everyone. Within the fintech lending space, some companies worth noting include Tala, Petal and Credit Karma.

Insurtech — Insurance Fintech

While insurtech is quickly becoming its own industry, it still falls under the umbrella of fintech. Insurance is a somewhat slow adopter of technology, and many fintech startups are partnering with traditional insurance companies to help automate processes and expand coverage. From mobile car insurance to wearables for health insurance, the industry is staring down tons of innovation. Some insurtech companies to keep an eye on include Lemonade, Kin and Insurify.

8
Dec, 2022

Top 10 Security Tools for Your AWS Environment

Amazon Web Services (AWS) enables organizations to build and scale applications quickly and securely. However, continuously adding new tools and services introduces new security challenges. According to reports, 70 percent of enterprise IT leaders are concerned about how secure they are in the cloud and 61 percent of small- to medium-sized businesses (SMBs) believe their cloud data is at risk.

AWS provides many different security tools to help customers keep their AWS accounts and applications secure. In fact, there was significant focus on AWS security best practices at re:Invent 2020. See the Best practices with Amazon S3 recap and Jeremy Cowan’s Securing your Amazon EKS applications: Best practices session for some of the details.

In this article, we’ll review the top ten AWS security tools you should consider using to improve your security posture in 2021 and beyond. Before we do that, we will briefly explain AWS account security versus application and service security.  Organizations must focus on keeping both secure to protect against different types of attacks.

Account Security Versus Application And Service Security

AWS provides security tools designed to improve both account security and application and service security.

An AWS account is an attack vector, as resources and data are accessible through the public application programming interface (API). Implementing a secure identity and access management strategy helps prevent leaking data — such as in S3 buckets — to the public. AWS’s many tools provide insights into your configured permissions and access patterns, and record all actions for compliance and audit purposes.

Applications and services hosted in AWS are susceptible to different kinds of threats from the outside. Cross-site scripting (XSS), SQL injection, and brute-force attacks target public endpoints. Distributed denial-of-service (DDoS) attacks may attempt to bring down your services, potentially compromising your architecture security. Without proper management, sensitive information — such as database credentials — may leak.

Therefore, it’s critical that organizations migrating to the cloud focus on minimizing risk and improving their overall security posture by addressing both account security as well as application and service security. The following AWS services lock down your cloud security, helping keep your customer data and systems safe from attack.

Top 6 AWS Account Security Tools

1. AWS Identity and Access Management (IAM)

AWS Identity and Access Management (IAM) is a web service for securely controlling access to AWS resources. It enables you to create and control services for user authentication or limit access to a certain set of people who use your AWS resources.

The IAM workflow includes the following six elements:

  1. A principal is an entity that can perform actions on an AWS resource. A user, a role or an application can be a principal.
  2. Authentication is the process of confirming the identity of the principal trying to access an AWS product. The principal must provide its credentials or required keys for authentication.
  3. Request: A principal sends a request to AWS specifying the action and which resource should perform it.
  4. Authorization: By default, all resources are denied. IAM authorizes a request only if all parts of the request are allowed by a matching policy. After authenticating and authorizing the request, AWS approves the action.
  5. Actions are used to view, create, edit or delete a resource.
  6. Resources: A set of actions can be performed on a resource related to your AWS account.

Let us explore the components of IAM in the next section of the AWS IAM tutorial.

To review, here are some of the main features of IAM:

  • Shared access to the AWS account. The main feature of IAM is that it allows you to create separate usernames and passwords for individual users or resources and delegate access.
  • Granular permissions. Restrictions can be applied to requests. For example, you can allow the user to download information, but deny the user the ability to update information through the policies.
  • Multifactor authentication (MFA). IAM supports MFA, in which users provide their username and password plus a one-time password from their phone—a randomly generated number used as an additional authentication factor.
  • Identity Federation. If the user is already authenticated, such as through a Facebook or Google account, IAM can be made to trust that authentication method and then allow access based on it. This can also be used to allow users to maintain just one password for both on-premises and cloud environment work.
  • Free to use. There is no additional charge for IAM security. There is no additional charge for creating additional users, groups or policies.
  • PCI DSS compliance. The Payment Card Industry Data Security Standard is an information security standard for organizations that handle branded credit cards from the major card schemes. IAM complies with this standard.
  • Password policy. The IAM password policy allows you to reset a password or rotate passwords remotely. You can also set rules, such as how a user should pick a password or how many attempts a user may make to provide a password before being denied access.

In the last section of the AWS IAM tutorial, let us go through a demo on how to create an S3 bucket using the multifactor authentication (MFA) feature.

2. Amazon GuardDuty

Amazon GuardDuty is a threat detection service that continuously monitors your AWS accounts and workloads for malicious activity and delivers detailed security findings for visibility and remediation. These include use of compromised credentials, simplified forensics and continuous monitoring of all security events seen in an AWS customers environment. With the announcement of new Malware Production, GuardDuty will scan EBS-backed EC2 instances with malicious behavior based on GuardDuty’s existing findings and report malware detected on EC2 and containers running on EC2 and instantly send data to Trellix Helix.

3. Amazon Macie

Amazon Macie is a security service that uses machine learning to automatically discover, classify and protect sensitive data in the Amazon Web Services (AWS) Cloud. It currently only supports Amazon Simple Storage Service (Amazon S3), but more AWS data stores are planned.

Macie can recognize any PII or Protected Health Information (PHI) that exists in your S3 buckets. Macie also monitors the S3 buckets themselves for security and access control. This all can help you meet regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) and General Data Privacy Regulation (GDPR) or just continually achieve the security you require in the AWS Cloud environment.

Within a few minutes after enabling Macie for your AWS account, Macie will generate your S3 bucket list in the region where you enabled it. Macie will also begin to monitor the security and access control of the buckets. When it detects the risk of unauthorized access or any accidental data leakage, it generates detailed findings.

The dashboard provides you with a summary that shows you how the data is accessed or moved. This dashboard gives you a view of the total number of buckets, the total number of objects, and the total number of S3 storage consumed.

It also breaks down S3 buckets by whether they are shared publicly, encrypted or not, and buckets shared inside and outside your AWS account or AWS Organization.

Create and run sensitive data discovery jobs to automatically discover, record, and report sensitive data in Amazon S3 buckets.

You can configure the job to run only once for on-demand analysis, or periodically for periodic analysis and monitoring.

A finding is a detailed report of potential policy violations for sensitive data in S3 buckets or S3 objects. Macie provides two types of findings: policy findings and sensitive data findings.

Macie can also send all findings to Amazon CloudWatch Events so you can build custom remediation and alert management.

4. AWS Config

AWS Config is a fully managed service that provides you with an AWS resource inventory, configuration history, and configuration change notifications to enable security and governance.

With AWS Config you can discover existing AWS resources, export a complete inventory of your AWS resources with all configuration details, and determine how a resource was configured at any point in time.

These capabilities enable compliance auditing, security analysis, resource change tracking, and troubleshooting.

Allow you to assess, audit and evaluate configurations of your AWS resources.

Very useful for Configuration Management as part of an ITIL program.

Creates a baseline of various configuration settings and files and can then track variations against that baseline.

5. AWS CloudTrail

AWS CloudTrail is an application program interface (API) call-recording and log-monitoring Web service offered by Amazon Web Services (AWS).

AWS CloudTrail allows AWS customers to record API calls, sending log files to Amazon S3 buckets for storage. The service provides API activity data including the identity of an API caller, the time of an API call, the source of the IP address of an API caller, the request parameters and the response elements returned by the AWS service.

CloudTrail can be configured to publish a notification for each log file delivered, allowing users to take action upon log file delivery — a process that according to AWS should only take about 15 minutes. It can also be configured to aggregate log files across multiple accounts so that log files are delivered to a single S3 bucket.

The service can facilitate regulatory compliance reporting for organizations that use AWS and need to track the API calls for one or more AWS account. CloudTrail can also be configured to support security information (SIEM) and event management platforms and and resource management.

6. Security Hub

AWS Security Hub combines information from all the above services in a central, unified view. It collects data from all security services from multiple AWS accounts and regions, making it easier to get a complete view of your AWS security posture. In addition, Security Hub supports collecting data from third-party security products. Security Hub is essential to providing your security team with all the information they may need.

A key feature of Security Hub is its support for industry recognized security standards including the CIS AWS Foundations Benchmark and Payment Card Industry Data Security Standard (PCI DSS).

Combine Security Hub with AWS Organizations for the simplest way to get a comprehensive security overview of all your AWS accounts.

Now that we have addressed the top account security tools, let’s focus on the top four AWS application sSecurity tTools you should consider.

Top 4 AWS Application Security Tools

1. Amazon Inspector

Amazon Inspector is an AWS software tool that automatically assesses a customer’s AWS cloud deployment for security vulnerabilities and deficiencies. Amazon Inspector evaluates cloud applications for weak points or deviations from best practices before and after they are deployed, validating that proper security measures are in place. The service then provides and prioritizes a list of security findings, including detailed descriptions of issues and recommendations to fix problems.

Amazon Inspector is available through the AWS Management Console and is installed as an agent on the operating system of Elastic Compute Cloud instances. Amazon Inspector requires an AWS Identity and Access Management (IAM) role, which grants the service permission to itemize instances as well as tags to assess before evaluating the security of a cloud deployment. The service can create an AWS IAM role, if needed.

An IT administrator defines an assessment template, which includes the rules packages to follow, the duration of the assessment run, the topics that result in notifications from Amazon Simple Notification Service and other attributes. The analysis of the target environment is called the assessment run, which analyzes behavioral data within a target, including network traffic on running processes and communication between cloud services.

Amazon Inspector pulls best practices from a knowledge base consisting of hundreds of rules (individual security practices or tests) that are updated by AWS security researchers. Amazon Inspector provides public-facing APIs that allow a user to incorporate the service on non-cloud technologies, such as email or security dashboards.

Amazon Inspector is billed based on the number of assessment runs and systems assessed, combining those elements into a metric called agent-assessments. Amazon provides a free trial before billing a customer per agent-assessment.

2. AWS Shield

AWS Shield protects AWS components against DDoS attacks. These attacks produce huge numbers of artificially generated requests to disrupt public applications. Shield is available in two presentations: Standard and Advanced.

AWS Shield Standard is enabled by default in CloudFront and Route 53 at no extra cost. AWS Shield Advanced is available for those two services plus several others: Elastic Load Balancing, EC2, Elastic IPs and Global Accelerator.

AWS Shield Standard offers protection against certain attacks but lacks flexibility for custom configurations. Shield Advanced integrates with the AWS WAF service to configure specific protection rules. Additionally, Shield Advanced provides access to the AWS Shield response team, a 24/7 support group available for emergencies. It also protects against extra AWS charges that could incur as a result of increased usage due to a DDoS attack; affected customers can request credits.

AWS Shield Advanced costs $3,000 per month. There is an additional data transfer fee, which varies depending on the protected resource type and the amount of data transferred (e.g., <100 TB, 400 TB, 500 TB). The Shield Advanced data transfer fee could be between $25 to $50 for 1 TB of data transferred within the initial 100 TB bracket, depending on the protected resource type. This is in addition to the data transfer fees applicable to each protected resource. The monthly fee is applicable per AWS Organization. Therefore, deployments across multiple AWS accounts within one Organization would pay only a single fee.

6
Dec, 2022

AWS vs GCP – Which Cloud Services to Choose in 2023?

  • Google Cloud is a suite of Google’s public cloud computing resources & services whereas AWS is a secure cloud service developed and managed by Amazon.
  • Google Cloud offers Google Cloud Storage, while AWS offers Amazon Simple Storage Services.
  • In Google cloud services, data transmission is a fully encrypted format on the other hand, in AWS, data transmission is in the general format.
  • Google Cloud volume size is 1 GB to 64 TB while AWS volume size is 500 GB to 16 TB.
  • Google Cloud provides backup services, but AWS offers cloud-based disaster recovery services.

What is AWS?

Amazon Web Services (AWS) is a platform that offers flexible, reliable, scalable, easy-to-use, and cost-effective cloud computing solutions.

AWS cloud computing platform offers a massive collection of cloud services that build up a fully-fledged platform. It is known as a powerhouse of storage, databases, analytics, networking, and deployment/delivery options offered to developers.

Here are the important pros/benefits of selecting AWS web services:

  • Amazon Web Services (AWS) offers easy deployment process for an app
  • You should opt for AWS when you have DevOps teams who can configure and manage the infrastructure
  • You have very little time to spend on the deployment of a new version of your web or mobile app.
  • AWS web service is an ideal option when your project needs high computing power
  • Helps you to improve the productivity of the application development team
  • A range of automated functionalities including the configuration, scaling, setup, and others
  • It is a cost-effective service that allows you to pay only for what you use, without any up-front or long-term commitments.
  • AWS allows organizations to use the already familiar programming models, operating systems, databases, and architectures.
  • You are allowed cloud access quickly with limitless capacity.

Important features of Amazon Web Services (AWS) are:

  • Total Cost of Ownership is very low compared to any private/dedicated servers.
  • Offers Centralized Billing and management
  • Offers Hybrid Capabilities
  • Allows you to deploy your application in multiple regions around the world with just a few clicks

What is Google Cloud?

Google launched the Google Cloud Platform (GCP) in 2011. This cloud computing platform helps a business to grow and thrive. It also helps you to take advantage of Google’s infrastructure and providing them with services that is intelligent, secure, and highly flexible.

Here are the pros/benefits of selecting Google cloud services:

  • Offers higher productivity gained through Quick Access to innovation
  • Employees can work from Anywhere
  • Future-Proof infrastructure
  • It provides a serverless environment which allows you to connect cloud services with a large focus mainly on the microservices architecture.
  • Offers Powerful Data Analytics
  • Cost-efficiency due to long-term discounts
  • Big Data and Machine Learning products
  • Offers Instance and payment configuration

Important features of Google Cloud are:

  • Constantly including more Language & OS.
  • A better UI helps you to improves user experience.
  • Offers an on-demand self-service
  • Broad network access
  • Resource pooling and Rapid elasticity

AWS vs. GCP - Products and Services

AWS and GCP have over 100 products and services in their catalogs that efficiently help customers work with cloud technologies. We will look at the differences between the popular services that AWS and GCP offer to their clients. 

Compute Engine is a compute and host service that provides scalable virtual machines to clients for running their workload tasks and applications. 

GCP provides four types of compute engine instances that offer specific features:

  • General Purpose – It is used for general workloads with reasonable price and performance ratios. 

  • Compute Optimised – It is optimized for compute-intensive workloads and offers higher performance than general-purpose instances. 

  • Memory Optimised – It is designed for memory-intensive tasks, providing up to 12TB of memory per core.

  • Accelerator Optimised – It is designed for parallel processing and GPU-intensive processes. 

AWS: Typically, AWS provides different EC2 instances similar to the list above. 

  • General Purpose instances provide diverse functionalities like compute, storage, and networking in equal proportions. General Purpose instances are suitable for web servers.

  • Compute Optimised instances are ideal for high-performance tasks that require high-speed processors and are compute-intensive—for example – game servers, media encoding devices, etc. 

  • Memory Optimised instances are optimal for situations where a large amount of data is processed in memory. These EC2 instances come to EBS optimized by default and are powered by the AWS Nitro System.

  • Storage Optimised instances offer high sequential and random read/write operations capability. These are used primarily for workloads that perform read/write on huge data stored in local storage. 

  • GPU/Accelerated instances are used for graphics processing and floating-point calculation that require colossal processing power. Accelerated Instances use extra processors and dedicated GPUs that boost hardware performance. 

Kubernetes is open-source container management and orchestration system that helps in application deployment and scaling. Containers are resources that run code along with its constituent dependencies, and Kubernetes provides container management and portability with optimal resource utilization for application development. It is easier to run Kubernetes on GCP because Google has been involved in the development of Kubernetes from its inception. Elastic Kubernetes Service in AWS provides no resource monitoring tool compared to Stackdriver by GCP. 

Serverless computing is a prevalent Function-as-a-Service example that does not require the deployment of virtual machine instances. AWS Lambda is the serverless offering from AWS, and Cloud Functions is its GCP counterpart. Google Cloud Functions support only Node.js, while AWS Lambda functions support many languages, including Java, C, python, etc. It is also easier to run cloud functions when compared to AWS Lambda since it needs a few steps. On the other hand, AWS Lambda is faster than Google Cloud Functions by 0.102 million executions per second. 

Amazon and Google both have their solution for cloud storage. Let’s look at the features one by one:

AWS S3 

  • Each object is stored in a bucket, and one needs the developer given keys to retrieve these buckets. 

  • An S3 bucket can be stored from a list of regions depending on the proximity, availability, latency, and cost-related issues.  AWS has a vast web of connected data centers worldwide in all areas. It is bound to provide higher performance and speed when storing and retrieving data across large distances. 

GCP Storage 

  • Google Cloud storage provides high availability.  

  • It offers data consistency across regions and different locations. 

  • It also gives google developer console projects.

AWS glue is a fully managed, serverless extract, transform and load (ETL) service to discover, prepare and integrate data from multiple sources for machine learning, analytics, and application development. It is a serverless data integration service that makes data preparation easier, cheaper and faster. 

On the other hand, GCP Dataflow is a fully managed data processing service for batch and streaming big data processing. Dataflow allows a streaming data pipeline to be developed fast and with lower data latency. 

AWS vs. Google Cloud - Pricing

AWS: AWS offers three unique pricing features or models

  • Pay as you go: The model makes resource usage adaptable and flexible by pricing only the company’s current resources.

  • Save when you commit: The feature means that if you use AWS services for a certain period, like one year, you will be eligible to have saving offers. 

  • Pay Less by using more: AWS promotes more usage of its services by tiering the price. That means the more one uses a service, the cheaper it gets, and vice versa. 

GCP: GCP also offers features on pricing with some similarities to AWS

  • Only pay for what you use: Similar to AWS’s Pay-as-you-go model, you are only paying for resources you end up using. Thus, making it on-demand pricing.

  • Save on workloads by prepaying: The model saves customers money if they commit to using a service and pay early for the resources at discount prices. 

  • Stay in control of your spending: GCP offers many cost management tools that are freely available and provide valuable analytics like price and usage forecasts, intelligent recommendation on cost-cutting, etc. Using these, customers can inspect their spending and optimize it accordingly. 

  • Price Calculator or Estimator: GCP provides a price calculator tool using which customers can estimate the overall price for the product and services before subscribing to them and preemptively make amends in their budgets. 

GCP provides 300$ in credits to new customers to use their services and products up to the free monthly usage limit. GCP is relatively cheaper in pricing than its Amazon counterpart, AWS. It also charges for computing minute-wise and is more strict to the pay-what-you-use model. 

AWS vs. Google Cloud - Machine Learning

AWS and GCP offer cutting-edge machine learning tools from their portfolio that help develop, train, and test a machine learning model. AWS has three powerful tools: Amazon SageMaker, Amazon Lex, and Amazon Rekognition. In contrast, Google gives the clients two major options – Google Cloud AutoML for beginners and Google Cloud Machine Learning Engine for heavy-duty tasks and granular control. GCP also offers Vertex AI and Tensorflow for advanced machine learning capabilities.

AWS Machine Learning Services 

  • Amazon SageMaker is a full-fledged machine learning platform that runs on EC2 instances and can develop traditional machine learning implementations. 

  • Amazon Lex brings Natural Language Processing toolkit and speech recognition possibilities, focusing on integrating Chatbot applications. 

  • Amazon Rekognition is a computer vision suite that renders the development and testing of face/object recognition models. It can easily perform complex CV tasks like object classification, scene surveillance, and facial analysis. 

GCP Machine Learning Products 

  • Google Machine Learning Engine: It is the machine learning offering at scale from Google. Google ML engine can perform complicated Machine Learning tasks using GPU and Tensor Processing Unit while running externally trained models. With great efficacy, Google Machine Learning Engine automates resource provisioning, monitoring, model deploying, and hyperparameter tuning.  

  • Google Cloud AutoML is a machine learning toolkit explicitly built for beginners in the field. It offers functionalities like data model upload, training, and testing through its web interface. AutoML integrates well with other Google cloud services like cloud storage. It can perform all the complex machine learning problems like Face Recognition, etc.

  • Tensorflow: Tensorflow is an already renowned name in the machine learning community. Tensorflow is an open-source library for numerical computation and analysis. It is used widely in deep learning models and packs many useful Machine Learning functions.

  • Vertex AI is an MLOps platform that promotes experimentation through pre-trained APIs for natural language processing, image analysis, and computer vision.

AWS vs. GCP - Regions and Availability

Google Cloud network locations are available across 106 zones and 35 regions worldwide and over 200 countries and territories. In contrast, AWS is present in more than 245 countries and territories, with 29 launched regions and 93 availability zones. GCP is expanding its reach in different countries like Doha, Paris, Milan, Toronto, etc. At the same time, AWS is bringing its services to places such as Israel, UAE, Hyderabad, Switzerland, Jakarta, etc. 

Criteria
AWS
GCP
Overview
Amazon Web Services is the largest cloud provider worldwide, developed and maintained by Amazon, which provides cloud storage and computing services. Launched in 2006.
GCP is the cloud platform developed by Google that provides many cloud computing services that run and use the already established infrastructure used by other cloud services. Launched in 2008.
Availability
AWS has across 93 availability zones and 29 geographic regions worldwide.
GCP is present in more than 200+ countries and 106 zones across the globe.
Pricing and Billing
Minimum instance: 2vCPUs + 8GB RAM ~69USD/month Maximum instance: 3.84TB RAM + 128vCPUs ~$3.97/hour
Minimum instance: 2vCPUs + 8GB RAM ~52USD/month Maximum instance: 3.75TB RAM + 160vCPUs ~$5.32/hour
Market Share
AWS is leading with 34% of public cloud market share.
Google Cloud Platform commands 11% of the world cloud market.
Job Market and Salaries
LinkedIn search for AWS Cloud Engineers shows 45k+ job results. The average salary of an AWS Cloud Engineer in the USA is $136,453 per year.
LinkedIn search for GCP Engineers shows 24k+ results. The average salary of GCP Engineer in the USA is $141,375 per year.
Compute Services
Elastic Compute Cloud (EC2)
Compute Engine

AWS vs. GCP - Which is Better?

Comparing these two cloud giants at the forefront of the industry is complex. AWS and GCP are the most significant cloud providers and competitors like Microsoft Azure, Alibaba Cloud, IBM cloud, etc. To draw a differentiation between these technologies is like comparing iOS and Android or Mercedes and BMW. Both are good and have their own thriving cloud communities. 

We, as users, have to decide and pick a cloud platform that is compatible with our business foundation and allows us better control over our needs and demands. For example, Google offers myriad machine learning frameworks and utilities that integrate well with Google Cloud. If our goal is analytics, GCP could be a good choice. It is subjective in the end and contingent on the user/company. 

Everything is moving slowly to the cloud, and fewer on-premise applications and products remain. As cloud professionals, it is essential to have the expertise and know-how of various cloud providers in the industry. You can make critical decisions even if you have to switch between vendors. Learning the ins and outs of different cloud service providers, whether AWS or GCP, takes time and effort. Persistence is the key, ultimately. 

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