Terraformer

Terraformer is a CLI tool to convert your existing infrastructure to tf/json and tfstate files. The supported providers include major cloud like AWS, Azure, AliCloud, and IBM Cloud. The tfstate file has information about the provisioned infrastructure the terraform manages.

Terraformer supports terraform 0.13. To upgrade resources with new fields, upgrade the relevant Terraform providers.

How to install Terraformer

Follow the link here to install Terraformer according to the OS you prefer. If you are using Windows, install via Chocolatey.

Care should be taken that you have Terraform installed and added to the path variables (Windows).

Check for installation:

$ terraformer version
Terraformer v0.8.13

Terraformer Example

In this example, we will launch a simple EC2 instance in the AWS console and generate terraform files from the infrastructure. We give the Instance a “Name” tag with the value “terraformer.”

Create an empty folder to store the generated files. Open cmd inside this folder, and enter the following command:

terraformer import aws –resources=ec2_instance –filter=\”Name=tags.Name;Value=Terraformer\” –regions=us-east-1

The above command highlights two important things, — the resources parameter and the –filter parameter. The resources parameter indicates the name of the service that needs to be imported. We use –resources=\”*\”, and when using the “*” if there is a service that needs to be excluded, then –resources=\”*\” –excludes=\”iam\”.

With the –filter parameter, one can choose which resource Terraform imports. The filters work with resource identifiers or attributes, and multiple filtering values are separated by ‘:’. The resource identifier should be wrapped in “ ‘ ” if it contains’:. ‘ Identifier-based filters will be executed before Terraformer tries to refresh the remote state.

Filtering also has a Type option; this helps in filtering several types of resources. Also, multiple filters can be combined when importing different resource types. For example:

terraformer import aws -r sg,vpc –filter Type=sg;Name=vpc_id;Value=VPC_ID –filter Type=vpc;Name=id;Value=VPC_ID

The sg Name is different for both the filtering options.Furthermore, filtering is based on Terraform resource ID patterns. For valid ID patterns for your resources, refer Terraform documentation here. Example:

terraformer import aws –resources=vpc,subnet –filter=vpc=myvpcid –regions=us-east-1

After the brief introduction to the filtering and resources parameters, we will try to create a tf file from a simple EC2 infrastructure we created earlier with the Instance “Name” tag with value “terraformer”.

First, we go to the directory where we want all the files generated from Terraformer. Then, enter the below command:

terraformer import aws –resources=ec2_instance –filter=\”Name=tags.Name;Value=terraformer\” –regions=us-east-1

Upon entering the command chooses from the ec2 instance resource, filters the instance with tag terraformer from us-east-1 region.The respective terraform files are generated in the directory where terraformer import is run. For me here, that directory is “terraformer_poc”. So, the files generated by terraformer will be inside generated/aws/ec2_instance. If terraformer import is done other resources, then the tf files are generated with their respective resource names as shown below:We can see a new instance.tf file is created along with other files.

Reuploading The Terraformer File Created

Let us try uploading the instance.tf created by terraformer and see if we can directly create the exact same resource from the .tf file.

Below is the instance.tf file which was generated from terraformer:

resource \"aws_instance\" \"tfer--i-002D-xxxxxxxxxxxxxxxxxxxx_terraformer\" {
  ami                         = \"ami-0e1d30f2c40c4c701\"
  associate_public_ip_address = \"true\"
  availability_zone           = \"us-east-1a\"
  capacity_reservation_specification {
    capacity_reservation_preference = \"open\"
  }
  cpu_core_count       = \"1\"
  cpu_threads_per_core = \"1\"
  credit_specification {
    cpu_credits = \"standard\"
  }
  disable_api_termination = \"false\"
  ebs_optimized           = \"false\"
  enclave_options {
    enabled = \"false\"
  }
  get_password_data                    = \"false\"
  hibernation                          = \"false\"
  instance_initiated_shutdown_behavior = \"stop\"
  instance_type                        = \"t2.micro\"
  ipv6_address_count                   = \"0\"
  key_name                             = \"xxxxxxxx\"
  metadata_options {
    http_endpoint               = \"enabled\"
    http_put_response_hop_limit = \"1\"
    http_tokens                 = \"optional\"
    instance_metadata_tags      = \"disabled\"
  }
  monitoring = \"false\"
  private_ip = \"10.10.x.xxx\"
  root_block_device {
    delete_on_termination = \"true\"
    encrypted             = \"false\"
    tags = {
      Name = \"aw2tfsqs\"
    }
resource \"aws_instance\" \"tfer--i-002D-xxxxxxxxxxxxxxxxxxxx_terraformer\" {
  ami                         = \"ami-0e1d30f2c40c4c701\"
  associate_public_ip_address = \"true\"
  availability_zone           = \"us-east-1a\"
  capacity_reservation_specification {
    capacity_reservation_preference = \"open\"
  }
  cpu_core_count       = \"1\"
  cpu_threads_per_core = \"1\"
  credit_specification {
    cpu_credits = \"standard\"
  }
  disable_api_termination = \"false\"
  ebs_optimized           = \"false\"
  enclave_options {
    enabled = \"false\"
  }
  get_password_data                    = \"false\"
  hibernation                          = \"false\"
  instance_initiated_shutdown_behavior = \"stop\"
  instance_type                        = \"t2.micro\"
  ipv6_address_count                   = \"0\"
  key_name                             = \"xxxxxxx\"
  metadata_options {
    http_endpoint               = \"enabled\"
    http_put_response_hop_limit = \"1\"
    http_tokens                 = \"optional\"
    instance_metadata_tags      = \"disabled\"
  }
  monitoring = \"false\"
  private_ip = \"10.10.x.xxx\"
  root_block_device {
    delete_on_termination = \"true\"
    encrypted             = \"false\"
    tags = {
      Name = \"aw2tfsqs\"
    }
    volume_size = \"8\"
    volume_type = \"gp2\"
  }
  source_dest_check = \"true\"
  subnet_id         = \"subnet-xxxxxxxxxxxxxxxxxxxx\"
  tags = {
    Name = \"terraformer\"
  }
  tags_all = {
    Name = \"terraformer\"
  }
  tenancy                = \"default\"
  vpc_security_group_ids = [\"sg-xxxxxxxxxxxxxxxxxxxx\"]
}
    volume_size = \"8\"
    volume_type = \"gp2\"
  }
  source_dest_check = \"true\"
  subnet_id         = \"subnet-xxxxxxxxxxxxxxxxxxxx\"
  tags = {
    Name = \"terraformer\"
  }
  tags_all = {
    Name = \"terraformer\"
  }
  tenancy                = \"default\"
  vpc_security_group_ids = [\"sg-xxxxxxxxxxxxxxxxxxxx\"]
}

When you run “terraform plan” after initializing Terraform inside this directory (terraform init), there will an output saying that there are no changes in the infrastructure, this proves that there is a correct “replication” of the infrastructure to the instance.tf, as shown below:Now, let’s delete the instance we created the Name tag “terraformer” and try to create infrastructure FROM code via the terraformer created instance.tf file.

After deleting, run “terraform plan” once again. This will detect changes and informs that there is a Plan: 1 to add, 0 to change, 0 to destroy. This indicates that there is a resource to be added.

Now run “terraform apply”. When the process is complete, it ends in an error as shown below: So, this shows us that the instance.tf file generated by terraformer has to modify for reusability.

Conclusion

After going through a basic example of terraformer, i.e., creating a basic EC2 instance terraform file, it is evident that terraformer does indeed have the capacity to convert the infrastructure into code which is acknowledged by Terraform (“terraform plan”). However, going through a more complex architecture remains to be seen, which will be explored in further blogs based on this topic. Also, another advantage of using this CLI tool is that one can also look at some best practices about terraform file creation.

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Though this data is a significant enabler for businesses, as it piles up in volume and velocity, transmitting it for processing becomes untamable. Innovations in technology, such as edge computing, were hence born.

What is Edge Computing?

Edge computing refers to decentralizing the processing power of networks, bringing them as close to the source (user) as possible. As a result of this arrangement, data doesn’t need to travel across storage networks. By doing so, edge computing reduces the backhaul traffic to the central data repository, pacing up data processing by manifolds.

Why Edge Computing? 

The speed at which data generates will never slow down. It’s only going to see an upward trend by the day. As a result, businesses will be heavily relying on technologies like edge computing in the future. An IDC research predicts that in 3 years, 45% of IoT-created data will be stored, processed, analyzed, and acted upon close to or at the network’s edge. The research also indicates that 6 billion devices will be connected to edge computing solutions.

By bringing in ‘decentralization’ to cloud networks, edge computing has added to the many advantages businesses can reap from data.

For example, disruptions can be limited to only one point in the entire network. Consider that there is a cyberattack that leads to a power outage. With edge computing, you can curb its impact on only the local applications rather than letting it spread through the entire network. That’s just one use case. Every industry can benefit tremendously from edge computing.

How Edge Computing can transform your business?

Below are a few use cases detailing how industrial edge computing can transform business processes with this new-age technology.

1. Improved Customer Experience

When data can be processed right at the source, marketers can configure automation to respond instantly to customer requirements, queries, and other inputs. To top this, with edge computing, immediate data processing has become a possibility. Therefore, onsite reactions to a customer’s action happen even before a customer leaves the site or closes the tab.

Data such as customer’s location, previous onsite interactions, etc., can all be processed in real-time for hyper-personalized exchanges in a flash of light. Data-based interactions’ tremendous speed and relevancy ultimately result in significantly enhanced customer experience.

2. Increased Data Privacy

Businesses today deal not just with high volumes of data but also with how sensitive and prone all this data has become to cyber-attacks. The number and complexity of online crimes have been on an upswing. Businesses state that data privacy and security are the biggest concerns.

Edge computing helps businesses address these security concerns. It does so by increasing the data bandwidth and achieving a low latency. But edge computing also allows companies to become 100% GPR compliant. This means businesses that use edge technology will always work towards fulfilling all data privacy and security norms, even as they change with the industry.

3. Enhanced Augmented Reality Capabilities

Both augmented and virtual reality are highly dependent on their local environment. Most VR tools need to understand and scan the environment around them.

Though data can be stored on the cloud for AR and VR, translating it into vivid and fast experiences can be challenging. Imagine watching an AR clip where the tech is interrupted because data couldn’t be retrieved and processed at the required pace. Edge computing makes sure that never happens.

AR and VR are extensively being deployed in eCommerce too. Brands like Nordstrom have succeeded in creating visually immersive experiences that give shoppers a real-like, in-store experience. However, the solutions behind such technology are rarely looked into. That’s where edge computing lies.

4. Improvement in Autonomous Vehicles

Self-driving cars run on data. But, because they are running in real life, it becomes critical for them to process all the data faster. They should be efficient enough to learn things without contacting the cloud for data processing.

Engines must run even when there is limited or no connectivity to the cloud. Coordinating with other vehicles on the road without asking the cloud or a remote server, estimating weather conditions and analyzing jams requires more innovative processing with solutions like edge computing.

Consider what would happen if an autonomous vehicle on a congested road cannot gauge the nearest approaching vehicle. The probability of accidents would increase due to the lack of efficient data processing.

Besides the above mentioned areas, edge computing is also tremendously leveraged in industrial IoT. We’ll be delving into This aspect of edge computing in detail.

How can Industrial IoT benefit from Edge Computing?

Let’s return to the point in this post where we discussed edge computing ‘decentralizing’ cloud networks. Now, where most people think that cloud and industrial edge computing are two distinct approaches, we say they are not. Edge computing rather enhances cloud computing. But what’s wrong with cloud computing as a standalone approach?

1. There are data security threats related to IoT that can’t be handled using a traditional cloud-based approach.
2. There are performance issues related to augmented technologies, such as in the case of cloud-based lighting that we mentioned in the smart buildings use case.
3. As the amount of data you use, process, and share increases, the cost of the cloud also grows by leaps and bounds.

All these challenges can be met when you bring the best cloud and edge computing to use them together. In cloud computing, data can be generated and stored—for instance, your favourite Netflix series! With industrial edge computing, this data can be developed and processed closest to the source, using machines or robots all across the globe over the world!

While the benefits of edge computing enhanced cloud computing are transparent, businesses still use them in isolation. As per the Automation World survey, more than 50% of the respondents have deployed cloud computing and nearly 45% use IoT edge computing for IoT implementations.

‘Manufacturers’ in the survey respondents report that utilizing these technologies has reaped tremendous benefits in their initial IoT deployments. As per the survey:

•   50% of the companies launching edge/fog or cloud computing IoT initiatives report significant reductions in downtime.
•   38% report improvements to production output
•   37% tout profitability increases
•   30% highlight a decrease in production costs

If standalone, cloud and edge computing can achieve so much, what amazing results can be achieved when they are combined! In fact, this marriage is much needed for futuristic IoT deployments that follow a typical rollout pattern.

The pattern consists of phases in which phase one is concerned with the cloud to host core enterprise analytics applications. The next step is to invest in new-age computing technologies such as intelligent automation systems that bring in personalization, real-time communication, and speed of execution in the entire process.

An example to describe the combined real power cloud and edge computing could be about offshore wind turbines. Consider an offshore wind turbine farm that uses cloud computing for its business operations. The cloud aggregate signals about weather conditions. If edge computing is used alongside, critical adjustments can be made to individual wind turbines, such as automatically fine-tuning turbine speeds for optimal fleet performance.

Conclusion

Edge computing has revolutionized innovative technology, adding speed to operations. This is *the* technology that IoT demands.

As requirements and demands of technology increase, the trend of using cloud computing along with edge computing will get pushed further. Combining cloud and edge computing for IoT implementation spells your betterment.

Think your business needs to get edge computing or cloud computing to its technology stack? Get in touch with the AWS cloud service provider experts at Rapyder today!

[Read Next : Alibaba Cloud Or AWS – Which Is Better For Your Business? ]

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Joe Biden’s victory and the return of liberal Democrats to power in the US have brought optimism to the Indian IT industry and the global tech industry at large. Indian IT sector has welcomed the change with open arms for obvious reasons.

The last few years have been unpredictable and challenging for the Indian IT services sector, primarily due to the outgoing president, Donald Trump’s policies around H1B visas and other immigration issues. The Indian IT services industry has traditionally depended mainly on the US market, with nearly 60% of the revenues of the top Indian IT services firms being contributed by this market. The Biden administration is expected to be less hostile and more liberal with immigrant policies, which, if it happens, will be a great boon for Indian tech firms.

What does Joe Biden’s win mean for the Indian IT Industry?

Will Biden reverse Trump’s immigration policies?

India has been an essential contributor of high-skilled talent—especially STEM (science, technology, engineering and mathematics)—for the global market. Trump administration’s strict visa regime led to a backlash across the globe as it further worsened the skill shortage challenge in the US. It was not just the Indian IT firms that expressed concern over this move. Many US-based tech giants –including Amazon, Microsoft, Netflix, Facebook and Twitter—said that Trump’s move could damage IT firms and impact the delivery of new products/services. It indeed questioned the Indian tech industry’s decade-old ‘talent model’.

All of that is expected to change now—sooner or later. Indian firms and industry experts have been quite vocal about their optimism and how the leadership change in the US would be a positive development for the industry.

“NASSCOM congratulates US #PresidentElect @JoeBiden on his win. We look forward to working with him and his administration in pivoting technology, skills and digital transformation for the United States,” tweeted the apex body right after Biden was announced as the president-elect.

Silicon Valley firms are equally hopeful that Biden’s relaxed policies will help them effectively address the skills gap—a growing concern for all big tech companies.

More than the policy change, Biden’s victory has brought a much-needed morale booster for the global tech industry. We are going through a time when Covid-19 has wholly reshuffled the deck, and technology has been considered the biggest shot in the arm. Liberal policies that promote unrestrained talent flow and knowledge sharing beyond geographical boundaries have never been more critical.

Tech in the Biden Era – Biden’s Presidency and its Impact on Global Tech

Biden administration’s policies and approach are expected to bring many positive changes for the global tech industry broadly. These include improved collaboration at the global level to address challenges and opportunities around cyber security, AI, Robotics and tech-enabled climate change solutions.

Biden’s stand towards trade tariffs is another area the global tech community eagerly watches. While Biden may not wholly reverse Trump’s Trade War against China, it has been said that Biden will likely have a more lenient approach. Trump’s Huawei ban, for example, has had a ripple effect in the global tech industry, with sales of US suppliers like Qualcomm, Broadcom, Microsoft, and Google predicted to take a hit.

Why Biden’s Victory Bodes Well For The Tech World?

The tech world is waiting to see the new administration’s decisions around antitrust and net neutrality. At the same time, the Biden-Harris administration will likely take a pro-tech approach and pave the way for better opportunities for startups and more mature discussions around data protection/privacy and a brighter roadmap for emerging technologies like AI.

The next four years of the Biden era will likely present new windows of opportunity to the tech world.

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From the shift in IT spending policies of the companies to the increasing dominance of public cloud, hybrid cloud, containers, and instances, to the implementation of AI in cloud-based IT analytics solutions, this post outlines 15 cloud computing predictions you need to watch in 2018.

15 Cloud Computing Predictions to Watch in 2018

Prediction 1 – Companies Will Keep Shifting Their IT Spending Policies 

In recent years, a large number of companies have shifted their IT spending policies to increase their investment in the cloud. According to a recent Gartner Research Report, cloud spending will remain a major cause of companies’ shift in IT spending policies in 2018. So, we will see more and more companies deploying their new IT projects in the cloud in 2018.

Prediction 2 – Public Cloud Adoption Rate of Companies Will Increase

Public cloud leaders have achieved tremendous growth in the last few years. Considering the large number of companies adopting public cloud platforms (such as AWS, Google Cloud, and Microsoft Azure) in recent years, the cloud solution providers have realized the value of the channel and included a balanced portfolio of all public cloud leaders in their services. The tremendous growth and continuous innovation in the public cloud will force more companies to adopt the public cloud in 2018, leading to a significant increase in the public cloud adoption rate of companies in 2018.

A recent Forrester report states that the total global public cloud market will be $178B in 2018, up from $146B in 2017, and will continue to grow at a 22% compound annual growth rate (CAGR). The leading American market research firm has also predicted that the public cloud platforms, the fastest-growing segment, will generate $44 billion in 2018. Also, AWS, Google, and Microsoft will capture 76% of all cloud platform revenue in 2018; 80% by 2020.

Prediction 3 – The Usage of Hybrid Cloud & Multiple Cloud Will Increase

A hybrid cloud allows companies to run their applications in a mix of on-premises, private, and public clouds with common orchestration and management tools. The introduction of Azure Stack by Microsoft and teaming up of VMware and AWS and Cisco, and Google to create a hybrid cloud that helps companies improve operational agility, efficiency, and scale will be the main reason behind the companies’ increased usage of hybrid cloud in 2018. The multiple clouds will also become dominant in 2018, with different workloads running in different clouds and being managed separately.

Prediction 4 – Customized Cloud Compute Instances Will Emerge

As the cloud adoption by the companies increases, the cloud leaders will further segment and optimize their compute instances for improved performance and new use cases. This will lead to various application-specific instance types within the clouds in 2018. The cloud leaders will introduce customized instances optimized for AI, Big Data, High Network Performance, and Large Memory of applications.

Prediction 5 – Cloud Containers Will Gain More Popularity

Cloud containers are a hot topic in the IT world, especially regarding security. The isolation boundary provided by the container at the application level (instead of the server level) ensures that if anything goes wrong in a container (e.g., excessive consumption of resources by a process), it only affects that individual container and not the whole VM or whole service. Today, almost all big or small companies use containers, and all the cloud leaders, including AWS, Microsoft Azure, and Google Cloud, provide containers to their customers. The recent launch of Amazon Elastic Container Service for Kubernetes (EKS) by AWS is a testimony to the increasing popularity of cloud containers, which will continue to rise in 2018 as well.

Prediction 6 -Serverless Computing Will Gain Momentum   

Serverless computing is far more efficient than traditional virtualized servers and today’s application containers. Earlier, a unit of an additional computing resource was an instance and VM, for which you were required to pay – whether you consumed them or not. The most significant benefit of Serverless computing is that it allows you to spin up the additional compute resources and pay for them on a consumption model. Serverless computing is likely to will gain more momentum in 2018 as the companies that strive for maximum efficiency and cloud cost savings will rely heavily on Serverless computing.

Prediction 7 – Cloud-Based Big Data Mining Solutions Will Emerge

Today, many companies are launching cloud-based IoT applications in the market. While these companies rely heavily on the big data generated by their IoT applications to make better business decisions, they don’t have any suitable method to mine the big data generated by their IoT applications. So, in 2018, we expect cloud leaders to provide cloud-based big data mining solutions to these customers to help them utilize their valuable IoT app data.

Prediction 8 – AI Upgrade Will Make Cloud Analytics More Proactive

Today, AI is used in our daily lives. Digital assistants like Siri of Apple, Cortana of Microsoft, and Google Assistant use AI to provide information and execute tasks on our voice commands. In 2018, cloud leaders were likely to upgrade AI more tightly into their analytics systems to make them more proactive (instead of reactive) to automate their response besides giving us actionable information and recommendations.

In 2018, the AI upgrade of cloud analytics systems is likely to give them more insight into the behavior of the cloud infrastructure, applications, and clients. Also, they are likely to be capable of recognizing unusual performance and security issues and the chances of an application or server failure. After recognizing such behavior, the cloud analytics systems are likely to perform an automated task to immediately fix the issue and improve the application’s performance by running another server or load-balancing the application. So, in 2018, we can expect the cloud analytics system to send us notifications like “Hey Mark, run another instance immediately.”

Prediction 9 – Cloud Migration Service Providers Will Increase

Given the large number of companies migrating their legacy IT systems and applications to the cloud platforms like AWS, Microsoft Azure, and Google Cloud – 2018 will witness a significant increase in the number of cloud migration service providers in the market to provide the companies the IT expertise required for the cloud migration. So, the cloud migration service is a great opportunity for IT solution providers, especially for those who provide cloud consulting services. Customers seeking cloud migration services are also likely to hire the same solution provider to manage their cloud infrastructure and optimize the performance and costs of their cloud assets.

Prediction 10 – Shortage of Cloud Technology Experts Will Continue

The biggest reason behind companies’ ignorance or postponement of cloud technology adoption is the lack of skills in cloud technology. As the cloud is still an emerging technology, the shortage of cloud technology experts will continue in 2018, and companies will have to look for an external cloud professional to help them in cloud migration and cloud management.

Prediction 11 – Cloud Cost Management Solutions Will Emerge

Predicting, optimizing, and managing the monthly cloud costs are still the biggest problem faced by companies using cloud technology. And the problem is going to become worse as the companies start using multiple cloud platforms to host their applications in the future. Since these companies badly need a transparent method to predict, optimize and manage their monthly cloud costs, we can expect the cloud leaders and third parties to develop transparent cloud cost management solutions in 2018 to help them predict, optimize and manage their monthly cloud costs or bills.

Prediction 12 – Non-US Cloud Providers Will Get More Traction

While AWS, Google Cloud, and Microsoft Azure are global cloud leaders and the first choice of government enterprises and Global 200 companies, the new small and medium businesses will look towards non-US cloud providers like Alibaba Cloud with interest due to their low costs. So, the non-US cloud providers are likely to get more traction in 2018. You must be aware that Alibaba Cloud has already surpassed Google in IaaS revenue.

Prediction 13 – Companies Will Consolidate Their Market Position

The cloud market has changed dramatically over the last decade as the more prominent companies are consolidating their market positions by acquiring smaller companies. We have seen telecoms entering the cloud market through acquisitions. Verizon acquired Terremark (but later sold it to IBM) to enter the cloud market. CenturyLink also acquired Orchestrate and ElasticBox. This trend is limited to the telecoms and the bigger cloud providers like Peak 10 \’s and Green Cloud Technologies, who acquired tier-two cloud companies like ViaWest and Cirrity, respectively, to consolidate their position in the cloud market. The trend of acquisitions will continue in 2018.

Prediction 14 – Cloud Misconfigurations Will Lead to More Data Breaches

Due to common cloud misconfigurations, we have seen many infamous data breach incidents in government offices and private companies. But the bigger cloud data breach incidents are yet to come in 2018. As the cloud gains popularity, we should be ready to witness bigger incidents of cloud data breaches just because someone from the IT team didn’t configure their cloud settings correctly, and the hackers took advantage of their mistake.

Prediction 15 – Cloud Security Will Remain the Top Concern

Since the cloud is still an emerging technology, it urgently requires a different security approach and measures than the traditional on-premise-based IT infrastructure. In 2018, cloud security will remain the top concern of companies using cloud infrastructure, and it opens an excellent opportunity for cloud solution providers to come up with robust cloud security solutions for customers. In other words, cloud security solutions will be the most sought-after cloud service after cloud consulting and migration in 2018.

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Machine Learning (ML) – An overview

Machine learning is one of the most disruptive technologies that we have encountered in our generation. It has the great potential to transform businesses for the better. From being a niche technology, ML now sees increased adoption among organizations in various sectors. ML emerged as a critical priority area for technology leaders in 2020 as they aim to achieve revenue growth while reducing costs. In 2021, enterprises are exploring more matured use cases of the technology as they navigate an environment of flux. Disruptive organizations have been at the forefront of adopting this technology for process automation, customer experience, security, etc. In 2021, here are the top five industries that will adopt ML to change how they work forever.

Role of Machine Learning (ML) in Healthcare

The global pandemic has underscored the importance of investing in and optimizing our healthcare systems. ML is the most promising technology that allows healthcare providers to churn massive volumes of data and derive valuable clinical insights. ML offers tremendous progress in drug discovery, cutting down the long discovery & development pipeline and reducing costs. It can also significantly improve healthcare delivery systems and, in turn, lift the overall quality of healthcare while keeping costs under control. In the coming days, ML is also predicted to have critical applications in clinical trials.

Experts emphasize that ML will significantly impact almost all branches of healthcare, including pharma and biotech.

Role of Machine Learning (ML) in Banking & Finance

The banking sector has already seen many matured use cases of ML, especially in fraud detection and automating processes. Machine Learning use cases will be actively explored across areas such as trading, investment modelling, risk prevention and customer sentiment analysis. As digital transactions continue to grow, ML combined with predictive analytics will play a big role in helping financial institutions to improve transaction efficiencies throughout the transaction lifecycle. Banks and financial institutions will also use this technology to customize their products and offerings to stay more relevant in a competitive environment.

Role of Machine Learning (ML) in Media & Entertainment

Companies like Amazon and Netflix have recently popularized data-driven content consumption models. As the global pandemic further drives up the demand for new consumption models, firms will effectively leverage AI and ML to create value for customers and present the most relevant content to them in real time. Whether developing better recommendation engines or delivering hyper-targeted services, ML will be critical for the media and entertainment industry to address the drastically changing customer expectations. Predictive modelling will be vital in responding to customers in real time, anticipating future demand and making investments wisely.

Role of Machine Learning (ML) in Retail & E-Commerce

No other industry has better understood the need to be prepared for the unexpected. The global pandemic has disrupted the retail sector in several ways, and ML has been considered a critical enabler for the industry to address change effectively. Whether it is the traditional brick-and-mortar stores or the ecommerce companies, the sector is on a path to reinvention with technologies such as ML. From supply chain and inventory management to personalized product recommendations through chatbots, the retail and ecommerce sector looks at several ML use cases. It is also being used extensively for predicting user behaviour and analyzing the trend effectively to be better prepared. Dynamic pricing is emerging as a critical ML use case to help retailers thrive in a competitive market landscape.

Role of Machine Learning (ML) in Manufacturing & Industry 4.0

With the massive adoption of IoT devices set to further increase in the manufacturing sector, ML will be the most critical technology bridge that analyses the vast volumes of data generated. ML is the powerful building block of Industry 4.0, along with automation and data connectivity. While predictive maintenance is the most explored use case so far, manufacturers will look at more matured use cases of ML, such as real-time error detection, supply chain visibility, warehousing efficiency & cost reduction, and asset tracking. As traditional factories transform into smart factories, ML will fuel more incredible innovation and efficiency in the future.

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On March 08, 2018, Rapyder Cloud Solutions Pvt. Ltd. achieved another milestone by receiving the ISO/IEC 27001:2013 certification for establishing an Information Security Management System (ISMS).

At Rapyder, we adhere to the highest standards of information security, client confidentiality, and trust. We have received this certification because we acknowledge the fact that your manuscript is your most important asset and always treat it with the greatest integrity. That explains why leading Financial Services companies, Financial Technology companies, and E-Commerce startups trust Rapyder with their mission-critical Apps.

At Rapyder, we are passionate about Data and Information Security and always ensure:

• Your confidential information is secure
• Provide customers and stakeholders with confidence in managing risk
• Allows for secure exchange of information
• Helps you to comply with other regulations (e.g. SOX)
• Provide you with a competitive advantage
• Consistency in the delivery of your service or product

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A Gartner CFO survey revealed that 74% of CFOs polled will move at least 5% of their previously on-site workforce to permanently remote positions post-COVID-19. Many employees expect to continue to have the flexibility and safety of the work-from-home (WFH) model in the post-Covid-19 world.

While WFH has become a win-win for employers and employees in many aspects, it has opened a can of worms for enterprise security experts.

Millions around the globe shifted to a remote working model in a few days. As enterprise perimeters further blurred, many security leaders are forced to throw their previous strategies out of the window.

Unforeseen Cybersecurity risks

Adopting the new work model has been easier for modern tech firms as they already have the right policies and infrastructure. But remote working was nearly an alien concept for many businesses. A sudden transition to the WFH model was a massive hurdle for such organizations. Enabling thousands of employees to work remotely required them to go against the established practices.

Many were caught unaware of the new requirements to ensure business continuity. They scrambled to enable their corporate networks and provide employees with new devices and remote collaboration tools. Most networks were not equipped to handle the scale. Many employees did not own corporate laptops and access corporate data from their devices (most unprotected and unpatched) through their home networks (probably unprotected).

Once this transition happened—effectively or ineffectively—firms realized that their exposure to cyber threats was at its peak.
CISOs understood that they have plenty of gaps to deal with, such as:

•   Weak endpoint security coupled with direct internet access without VPN
•   Surge in cloud-based tools & data transfer to cloud without proper visibility
•   Unintentional data leakage
•   Increased phishing attacks that use Covid as a bait

Some new lessons

According to some recent reports, close to 50 percent of employees are less likely to follow cybersecurity practices while working from home. Many admit to bypassing security policies that are perceived to impede productivity. Legacy, on-site approach to security wouldn’t work in the new world of work.

Security practitioners need to focus on following some of the best practices suited for the Covid-induced remote working ecosystem, including:

1. Improving visibility of all remote endpoints: It’s essential to clearly understand the company’s digital footprint, which is now spread across numerous locations. Security teams must also ensure that all devices are patched and protected to minimize the attack surface. Employees and security teams should audit their home environment for potential vulnerabilities, such as those arising from IoT devices.
2. Reviewing access control policies: Organizations are now forced to reconsider their policies. Access and identity management can’t be static and needs to be reviewed depending on whether the employees are on-site or off-site using corporate-owned or personal devices. Multifactor authentication is increasingly considered an effective mechanism to ensure authorized access to data.
3. Awareness programs to deal with phishing attacks: Security awareness training for employees is critical to fight against the rising phishing attacks. Many organizations conduct phishing simulations to educate employees to recognize and report phishing attacks and social engineering threats.
4. Protecting cloud-based tools: Zoom-bombing has taught us valuable lessons on returning to the basics of security and privacy. The sudden upsurge in cloud-based collaboration and video conferencing tools led to significant shadow IT within organizations. Security teams must ensure employees adhere to basic security policies and hygiene. Also, security has to be the paramount criterion when choosing any cloud-based tool.

Employees are equally, if not more, responsible for ensuring corporate data and device security in this context. They are expected to be in charge of security instead of leaving everything in their systems admin’s hands.

Here are some Security tips to follow while working from home.

1. Install reliable antivirus solutions to all the devices that handle corporate data, even if it is your personal device. Lack of a security solution will not be considered a valid excuse in the event of data loss! Additionally, update your operating systems and software regularly to ensure your systems are patched.
2. Protect your Wi-Fi network with strong passwords. Some of the encryption standards are already outdated. So choose, WPA2 is a widely accepted method to prevent unauthorized network access. You must change the default login credential that comes with your router.
3. Always use corporate email accounts and company-recommended messaging/collaboration tools to communicate—anything configured by your IT team.
4. If you use a public network to access your corporate data, ensure that you’re connecting through a virtual private network (VPN) so that all the data you transfer will be encrypted.

Summary: A highly volatile crisis like Covid-19 calls for a highly adaptable security framework that constantly responds to emerging risks. Security leaders and their teams must work closely with key business functions and include cybersecurity in crisis management procedures. An effective cyber risk mitigation measure is critical to strengthen enterprise resilience in these challenging times.

Recommended Reading:  Learn how a Leading BPO built a Secure Remote Working Environment using Amazon WorkSpaces

The post What ‘Remote Working’ Taught Us About Cybersecurity Defenses appeared first on Rapyder.

Considering these facts in combination with the growing adoption of cloud model becomes an even more complicated scenario. Security is the pillar of cloud architecture and is critical for the success of any cloud workload. As the cloud market leader, AWS has always been at the forefront of ensuring its customers meet the core security and compliance requirements. At the same time, security on cloud is one of the most misinterpreted concepts.

In the initial years, security stood in the way of organizations embracing cloud. But it lasted until IT pros figured out that cloud security is better. Nevertheless, some recent high-profile cloud breach incidents have brought cloud security back into focus and raised a very important question—who is responsible for cloud security?

That’s when we started hearing about “shared responsibility” models. It essentially talked about the fine line from where the responsibility of security shifts to the customer. AWS has been championing the shared responsibility model for quite some time now.

In simple terms, AWS defines it as ‘security of the cloud’ and ‘security in the cloud.’ AWS takes full responsibility for the security of the cloud— protecting the infrastructure that runs all of the services offered in the AWS Cloud. Security in the cloud is, however, the customer’s responsibility and will be determined by the cloud services that a customer selects. The diagram below brings more clarity:

Demystifying security on AWS Cloud : Image Source – AWS

The ‘Security OF The Cloud’ part is fairly straightforward, and AWS provides some of the best-in-class security features in the industry. But the customer will finally define the ‘security IN the cloud.’ How do you get that right?

AWS recommends approaching security from a data perspective rather than compartmentalizing it into on-premise and off-premise security. Organizations need to focus on three broad categories, and the cloud provider advises to achieve this. This includes:

Data classification and security-zone modeling: How you classify data can not only decide your security posture but can be critical in bringing the much-touted cloud benefits like agility and flexibility into the environment. It’s important to add the right level of fidelity to your data classification model, and the data security control models must be designed to match the varying degree of sensitivity the data comes with. The data classification model can then be combined with a ‘security zone,’ providing a well-specified network perimeter that protects all the critical assets.

Defense in depth: This model focuses on a layered security control environment to ensure one control works if the other fails. In today’s dynamic technology and business landscapes, it’s critical to have preventive and detective security measures, which AWS emphasizes in its Cloud Adoption Framework. Preventive control looks at aspects like IAM, Infrastructure security, and encryption/tokenization.
On the detective control side, organizations should prioritize detecting unauthorized traffic, configuration drift, and fine-grained audits.

Swim-lane isolation: Swim-lane isolation looks at security from a business domain-driven design and recommends approaching the security of the data stores attached to each microservices from the context of a business domain. This helps ensure that sensitive data from one microservice domain does not leak out through another.

Summary
Cloud service providers like AWS have been investing heavily in public cloud security. But ultimately, the customers are equally responsible for the security of their data. In today’s cloud-driven world, look beyond the traditional tools and methods of securing data, and reassess your security postures and strategies.

[Recommened Reading: AWS Security – What Makes Misconfiguration Critical?

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What are spot instances?

Introduced in late 2009, Spot instances are a new way for Amazon Web Services (AWS) customers to purchase its Elastic Compute Cloud (EC2) instance (a virtual machine) at lower costs for running their applications on the Amazon Web Services (AWS) infrastructure.

What are the benefits of spot instances?

The most significant benefit of spot instances is that they are often available at a discount compared to On-Demand instances. Thus, the spot instances can significantly reduce the cost of running your applications, grow your application’s compute capacity and throughput for the same budget, and enable new cloud computing applications.

How do Spot Instances work?

Amazon Web Services (AWS) sells its spared EC2 instances in a spot market, similar to derivative markets. The spare EC2 instances are bundled in spot pools based on the instance type, platform, and availability zone.

To buy AWS spot instances, you need to place a bid. If spare EC2 instances (spot instances) are available and your bid is above their current market price, then you will get to use them as long as they are available and your bid price remains above their market price.

Why is Spot Management important?

Since the spot instances come cheaper than on-demand instances, they reap significant savings, but some risks are involved. For example, if there are no spare EC2 instances (spot instances) available, you have to wait a long time until they availability in the spot market. Also, if the market price of the spot instances rises above your bid price, they will be terminated abruptly after a two-minute warning. And when this happens, all the processes running on-the-spot instances are dead, leaving you in a terrible situation, especially if you are running critical and customer-facing services. Suppose you want to avoid such dire situations. In that case, you must have a proper spot management strategy to ensure that you always have sufficient spot instances to run your applications smoothly and your spot instances never get terminated like this.

The spot instance management not only saves you from going instance-less but also plays a vital role in your AWS Cost Optimization by allowing you to use the spot instances at the lowest price possible.

How to ensure AWS Cost optimization?

For AWS Cost Optimization, you need a proper spot management strategy. To execute the spot management strategy correctly, you must have a primary and working knowledge of AWS Spot Instances. Only then can you manage your spot instances efficiently.

[Read Next: How Can You Optimize Cost On AWS? ]

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