Advantages of Cloud Computing

We live in a modern, fast-paced world where people want to have all things to be instant. Through the internet, it makes our work faster and convenient. For example, you can have remote access to your files and applications using the internet and it frees you from using storage devices like flash drives. Whereas in the past, people would run applications or programs from software downloaded on a physical computer or server in their building. With cloud computing, it grants people to access to the same kinds of applications through the use of internet.


According to a set of reporters tackling cloud computing, cloud computing is a concept that works hand in hand with mobile computing in order to truly achieve computing on the go. This basically grants users or consumers to have remote access to data, applications, and storage for convenience and frees them from physical hardware needs.


In other words, it is a delivery system that delivers computing. Examples of cloud computing include most of Google’s services like Gmail, Google Calendar, Evernote, Google Maps and so on. For me, cloud computing is a big help, especially that I am a busy person and I can access my files anytime and anywhere. This is a great use either for business or personal means. Cloud Computing is mainly categorized into three services including, software as a service (SaaS), infrastructure as a service (IaaS) and platform as a service (PaaS). Key characteristics of Cloud Computing are on-demand network access, scalability, greater security, measured service, and flexibility. The reporters also shared their reasons why there are several people who move to cloud computing. They mentioned a lot of benefits including unlimited storage capacity, device independence, increased efficiency, increased data reliability, lower computer cost, easier collaboration, etc,. Despite a number of benefits, there are also its disadvantages including it requires a constant internet connection, does not work well with a low-speed internet connection and has possibilities of data being lost or not secured.

Cloud Computing is one of the most popular breakthroughs in IT today. The “cloud” represents one of the most significant shifts that computing has gone through. I think that although cloud computing is lacking a bit in some aspects and needs improvement it is a new model and architecture so it still has an opportunity for future research and expansion.

Nowadays, it is clear that cloud computing has revolutionized how technology is obtained, used and managed. And all of this has changed how organizations budget and pay for technology services.

Cloud Computing has given us the ability to reconfigure quickly our environments to be able to adapt them to changing business requirements. We can run cost-effective services that can scale up and down depending on usage or business demands and, all of this, using pay-per-use billing. Making unnecessary huge upfront infrastructure expenses for enterprises, and balancing the possibilities of being a big enterprise or a new one.



There are multiple and diverse advantages, and most of them depend on the enterprises, the business and the needs they have. But, there are six of them that tend to appear in every case:

Variable vs. Capital Expense

Instead of having to invest in data centers and servers before knowing how they are going to be used, they can be paid when you consume computing resources and paid only for how much you consume.

Economies of Scale

By, using cloud computing, we can achieve a lower variable cost that we would get on our own. Cloud providers like AWS can aggregate hundreds of thousands of customers in the cloud, achieving higher economies of scale, which translates in lower prices.

Stop Guessing Capacity

When we make a capacity decision prior to deploying an application, we usually end up either setting expensive idle resources or dealing with limited capacity. With cloud computing, there is no more need for guessing. The cloud allows us to access as much or as little as we need to cover our business needs and to scale up or down as required without advanced planning, within minutes.

Increase Speed and Agility

Deploy new resources to cover new business cases or implementation of prototypes and POCs to experiment now can be achieved with a few clicks provisioning new resources in a simple, easy and fast way usually reducing costs and time and allowing companies to adapt or explore.

Focus on Business Differentiators

Cloud computing allows enterprises to focus on their business priorities, instead of on the heavy lifting of racking, stacking and powering servers. This allows enterprises to focus on projects that differentiate their businesses.

Go Global in Minutes

With cloud computing enterprises can easily deploy their applications to multiple locations around the world. This allows providing redundancy and lower latencies. This is not any more reserved just for the largest enterprises. Cloud computing has democratized this ability


gRPC Practical Tutorial – Magic That Generates Code

This is a really nice article I wanted to share that helped me understand the gRPC protocol a little bit deeper. It also offers a nice hands-on experience for the active developer!

What is gRPC?

gRPC is a language-neutral, platform-neutral framework that allows you to write applications where independent services can work with each other as if they were native. If you write a REST API in, say, Python, you can have Java clients work with it as if it were a local package (with local objects).

This figure should clarify it even further.

As in many RPC systems, gRPC is based around the idea of defining a service, specifying the methods that can be called remotely with their parameters and return types. On the server side, the server implements this interface and runs a gRPC server to handle client calls. On the client side, the client has a stub that provides exactly the same methods as the server.


Why is it ?

  • gRPC is supported in tons of languages. That means, you write your service in, say, Python, and get FREE!!! native support in 10 languages.
    • C++
    • Java
    • Go
    • Python
    • Ruby
    • Node.js
    • Android Java
    • C#
    • Objective-C
    • PHP
  • gRPC is based on the brand new shiny HTTP/2 standard which offers a bunch of cool stuff over HTTP/1. My favorite HTTP/2 feature is bidirectional streaming.
  • gRPC uses Protocol Buffers (protobufs) to define the service and messages. Protobuf is a thing for serializing structured data that Google made for the impatient world (meaning it’s fast and efficient).
  • As I mentioned it, gRPC allows bi-directional streaming out of the box. No more long polling and blocking HTTP calls. This is valuable for a lot of services (any real-time service for example).

Why it could fail.

  • gRPC is Alpha Software™. That means it comes with no guarantees. It can (and will) break, docs are not yet comprehensive, support could be lacking. Expect tears and blood if you use it in production.
  • It has no browser support, yet. (Pure JS implementation of protobufs is in alpha, so this point will likely be moot in a few months).
  • No word from other browser vendors on standardization (which is why Dart didn’t catch on).

What are we making?

I was thinking really hard what I should make that is simple enough for most people to follow, but also practical so you can actually use it in your projects.

I use Twitter a lot, and have worked on a lot of project using the Twitter API. Almost every project requires parsing the tweet text to extract tagged users, hashtags, URLs etc. I always use twitter-text-python library. I think it will be great to write a server wrapping this package in Python, and then generating stubs (native client libraries) in Python and Ruby.

All code is here:

What you need

  • protoc – install
  • grpc-python – pip install grpcio
  • grpc-ruby – gem install grpc

Installation of these should be easy.


The proto file is where we define our service, and the messages that compose the service. For this particular project, this is the proto file we are using.

I have commented out the file so it should be pretty straightforward.

// We're using proto3 syntax
syntax = "proto3";

package twittertext;

// This is the service for our API
service TwitterText {
  // This is where we define the methods in this service

  // We have a method called `Parse` which takes
  // parameter called `TweetRequest` and returns
  // the message `ParsedResponse`
  rpc Parse(TweetRequest) returns (ParsedResponse) {}

// The request message has the tweet text to be parsed
message TweetRequest {
  // The field `text` is of type `string`
  string text = 1;

// The request message has the tweet text to be parsed
message ParsedResponse {
  // `repeated` is used for a list
  repeated string users = 1;
  repeated string tags = 2;
  repeated string urls = 3;

Full proto3 syntax guide can be found here.

Generate gRPC code

Now comes the fun part. We are going to use gRPC to generate libraries for Python and Ruby.

# Python client
protoc  -I protos/ --python_out=. --grpc_out=. --plugin=protoc-gen-grpc=`which grpc_python_plugin` protos/parser.proto

# Ruby
protoc -I protos/ --ruby_out=lib --grpc_out=lib --plugin=protoc-gen-grpc=`which grpc_ruby_plugin` protos/parser.proto

What has happened is that based on the proto file we defined earlier, gRPC has made native libraries for us.

The first command will generate The latter will generate lib/parser.rb and lib/parser_service.rb. All three files are small and easy to understand.

A python client can now just import parser_pb2 and start using the service as if it were a native package. Same for ruby.


I decided to make my server in Python, but I could have used Ruby as well.

import time

from ttp import ttp

// Bring in the package for our service
import parser_pb2

_ONE_DAY_IN_SECONDS = 60 * 60 * 24

// This is the parser from the third-party package,
// NOT from gRPC
p = ttp.Parser()

class Parser(parser_pb2.BetaTwitterTextServicer):
    def Parse(self, request, context):
        print 'Received message: %s' % request
        result = p.parse(request.text)
        return parser_pb2.ParsedResponse(users=result.users,

def serve():
    server = parser_pb2.beta_create_TwitterText_server(Parser())
        while True:
    except KeyboardInterrupt:

if __name__ == '__main__':

At this point, it is helpful to have open. What we are doing in class Parser is implementing the interface parser_pb2.BetaTwitterTextServicer that was generated, and implementing the Parse method.

In Parse, we receive the request (which is a TweetRequest object), parse it using the third-party package, and respond with a parser_pb2.ParsedResponse object (structure defined in the proto file).

In serve(), we create our server, bind it to a port and start it. Simple. 🙂

To start the server, simply run python

Write the clients

from grpc.beta import implementations

import parser_pb2


text = ("@burnettedmond, you now support #IvoWertzel's tweet "

def run():
    channel = implementations.insecure_channel('localhost', 50051)
    stub = parser_pb2.beta_create_TwitterText_stub(channel)
    response = stub.Parse(parser_pb2.TweetRequest(text=text), _TIMEOUT_SECONDS)
    print 'Parser client received: %s' % response
    print 'response.users=%s' % response.users
    print 'response.tags=%s' % response.tags
    print 'response.urls=%s' % response.urls

if __name__ == '__main__':

The generated code also contains a helpful method for creating a client stub. We bind that to the same port as the server, and call our Parse method. Notice how we build the request object (parser_pb2.TweetRequest(text=text)) – it must be the same as defined in the proto file.

You can run this client using python and see this output:



this_dir = File.expand_path(File.dirname(__FILE__))
lib_dir = File.join(this_dir, 'lib')
$LOAD_PATH.unshift(lib_dir) unless $LOAD_PATH.include?(lib_dir)

require 'grpc'
require 'parser_services'

def main
  stub ='localhost:50051', :this_channel_is_insecure)
  response = stub.parse( "@burnettedmond, you now support #IvoWertzel's tweet parser!"))
  puts "#{response.inspect}"
  puts "response.users=#{response.users}"
  puts "response.tags=#{response.tags}"
  puts "response.urls=#{response.urls}"


Similarly, we build the client for Ruby, construct the Twittertext::TwitterText::Stub, pass in a Twittertext::TweetRequest, and receive a Twittertext::ParsedResponse back.

To run this client, use ruby You should expect the following output:

<Twittertext::ParsedResponse: users: ["burnettedmond"], tags: ["IvoWertzel"], urls: [""]>


Again, the full code is at

You can keep building clients the same way for 10+ languages. Write once, use everywhere (almost). We haven’t even touched the sweet parts of gRPC, especially streaming, but if you look at this guide, they cover it well. I myself am just beginning to explore gRPC, but so far it seems promising. I can’t wait to see what you make with it.

Additional resources:

#BigData innovation through #CloudComputing:


With the digitalization of almost everything in this world, the amount of data is increasing at an exponential rate. The IT experts soon realized that analysis of this data is not possible with the traditional data analysis tools. Considering this ever-expanding volume of useful data that could be used in a number of ways the IT experts came up with many solutions amongst which the two initiatives are amongst the top. These two are big data and cloud computing.

Big data analysis offers the promise of providing valuable insights of the data that can create competitive advantage, spark new innovations, and drive increased revenues. By carefully analyzing the data we can predict different things about the company. Cloud computing acts as a delivery model for IT services of any company and has the potential to enhance business agility and productivity while enabling greater efficiencies and reducing costs significantly. By storing the data on cloud servers instead of on site IT department you can not only save money but also make sure that your data is safe and secure as the security of these cloud servers is usually in the hands of top IT security companies.

Both technologies continue to thrive. Organizations are now moving beyond questions of what and how to store big data to addressing how to derive meaningful analytics that responds to real business needs. As cloud computing continues to mature, a growing number of enterprises are building efficient cloud environments, and cloud providers continue to expand services and service offerings.

Characteristics and Categories:

Databases for big data:

One of the most important and crucial task that any company has to do is to choose the correct data base for their big data. As the data is increasing more and more companies have emerged to provide data bases for this big. The databases that are designed to handle big data are usually referred to as NoSQL systems and they do not depend on SQL in contrast to the traditional SQL based data systems. The main working principle of all these companies is, however, the same that is to provide an efficient and effective storage to companies and give them ways to extract useful information from their big data. These companies truly help them to build and expand their business by giving them useful data analytics. The most reputed companies among hundreds of others are Cassandra, dynamob, and AWS. These companies not only give you the best data storage options they also make sure that your data is safe and secure and provide you with useful analytics about your data.

Machine Learning in the Cloud:

One of the most interesting features of cloud computing and big data analysis is the machine learning and its integration with AI. The machine learning cloud services make it easier to build sophisticated and large-scale models that can really increase the efficiency and enhance the overall data management of your company’s data. By injecting AI into your business, you can learn truly amazing things about the data analytics.

IoT platforms:

Internet of Things or IoT is also an interesting aspect of big data and cloud computing. Big data and IoT are essentially two sides of the same coin. Big data is more about data whereas IoT is more concerned with the flow of this data and connectivity of different data generating devices. IoT has created a big data flux that must be analyzed in order to get useful analytics from it.

Computation Engines:

Big data is not just about collecting and storing a large amount of data. This data is of no use to us until it gives us useful information and analytics. These computational engines provide excellent scalability to make your data storage more efficient. These engines use parallel and distributed algorithms to analyze the data. Map reduce is one of the best computations engines in the market at the moment.

Big Data on AWS:

Amazon’s AWS provides you one of the most complete and best big data platforms in the world. It provides you a wide variety of options and different services which can help you with your big data needs. With AWS, you get fast and flexible IT solutions and that too at a low cost. It has the ability to process and analyze any type of data regardless of the volume, velocity, and variety of data. The best thing about AWS is that it offers you more than 50 services and hundreds of features are added in these services every year constantly increasing the efficacy of the system. Two of the most famous services offered by AWS is redshift and kinesis.

AWS Redshift:

Amazon Redshift is a fast, efficient and fully managed data warehouse that makes it extremely simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. By allowing you to run complex analytic queries against petabytes of structured data and using sophisticated query optimization on high-performance local disks most results come back in seconds. It is also extremely cost efficient where you can start from as small as $0.25 per hour with no commitments and then gradually increase to petabytes of data for $1,000 per terabyte per year.

The service also includes Redshift Spectrum, which allows you to directly run SQL queries against exabytes of unstructured big data in Amazon S3. You don’t need to load or transform the data, and you can use open data formats which may include CSV, TSV, Parquet, Sequence, and RCFile. The best thing is that Redshift Spectrum automatically scales query and computes capacity based on the data being retrieved, so queries against Amazon S3 run fast and do not depend on data set size.

AWS Kinesis:

Amazon Kinesis Analytics is another great service by Amazon and is one of the easiest ways to process streaming data in real time with standard SQL. The best thing about this service is that you don’t have to learn any new programming languages or processing frameworks. This service allows you to query streaming data or build entire streaming applications using SQL. This makes sure that you can gain actionable insights and respond to your business and more importantly customer needs promptly.

Amazon Kinesis Analytics is a complete service that takes care of everything required to run your queries continuously and the best part is that it scales automatically to match the volume and throughput rate of your incoming data. With Amazon Kinesis Analytics, you only pay for the resources your queries consume which makes it extremely budget friendly and cost efficient. There is no minimum fee or setup cost.



Scaling #Python on Heroku: Deployment, part 1

It’s always good for a developer to have a couple of different deployment options under their belt. Why not try deploying your site to Heroku, as well as PythonAnywhere?

Heroku is also free for small applications that don’t have too many visitors, but it’s a bit more tricky to get deployed.

We will be following this tutorial:, but we pasted it here so it’s easier for you.

The requirements.txt file

If you didn’t create one before, we need to create a requirements.txt file to tell Heroku what Python packages need to be installed on our server.

But first, Heroku needs us to install a few new packages. Go to your console with virtualenv activated and type this:

(myvenv) $ pip install dj-database-url gunicorn whitenoise

After the installation is finished, go to the apilama directory and run this command:

(myvenv) $ pip freeze > requirements.txt

This will create a file called requirements.txt with a list of your installed packages (i.e. Python libraries that you are using, for example Django :)).

: pip freeze outputs a list of all the Python libraries installed in your virtualenv, and the> takes the output of pip freeze and puts it into a file. Try running pip freeze without the > requirements.txt to see what happens!

Open this file and add the following line at the bottom:


This line is needed for your application to work on Heroku.


Another thing Heroku wants is a Procfile. This tells Heroku which commands to run in order to start our website. Open up your code editor, create a file called Procfile in apilama directory and add this line:

web: gunicorn mysite.wsgi

This line means that we’re going to be deploying a web application, and we’ll do that by running the command gunicorn mysite.wsgi (gunicorn is a program that’s like a more powerful version of Django’s runserver command).

Then save it. Done!

The runtime.txt file

We also need to tell Heroku which Python version we want to use. This is done by creating aruntime.txt in the apilama directory using your editor’s “new file” command, and putting the following text (and nothing else!) inside:



Because it’s more restrictive than PythonAnywhere, Heroku wants to use different settings from the ones we use on our locally (on our computer). Heroku wants to use Postgres while we use SQLite for example. That’s why we need to create a separate file for settings that will only be available for our local environment.

Go ahead and create mysite/ file. It should contain your DATABASE setup from yourmysite/ file. Just like that:

import os
BASE_DIR = os.path.dirname(os.path.dirname(__file__))

    'default': {
        'ENGINE': 'django.db.backends.sqlite3',
        'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),

DEBUG = True

Then just save it! 🙂


Another thing we need to do is modify our website’s file. Open mysite/ in your editor and add the following lines at the end of the file:

import dj_database_url
DATABASES['default'] = dj_database_url.config()



DEBUG = False

    from .local_settings import *
except ImportError:

It’ll do necessary configuration for Heroku and also it’ll import all of your local settings ifmysite/ exists.

Then save the file.


Open the mysite/ file and add these lines at the end:

from whitenoise.django import DjangoWhiteNoise
application = DjangoWhiteNoise(application)

All right!

Heroku account

You need to install your Heroku toolbelt which you can find here (you can skip the installation if you’ve already installed it during setup):

When running the Heroku toolbelt installation program on Windows make sure to choose “Custom Installation” when being asked which components to install. In the list of components that shows up after that please additionally check the checkbox in front of “Git and SSH”.

On Windows you also must run the following command to add Git and SSH to your command prompt’s PATH: setx PATH "%PATH%;C:\Program Files\Git\bin". Restart the command prompt program afterwards to enable the change.

After restarting your command prompt, don’t forget to go to your apilama folder again and activate your virtualenv! (Hint: Check the Django installation chapter)

Please also create a free Heroku account here:

Then authenticate your Heroku account on your computer by running this command:

$ heroku login

In case you don’t have an SSH key this command will automatically create one. SSH keys are required to push code to the Heroku.

Git commit

Heroku uses git for its deployments. Unlike PythonAnywhere, you can push to Heroku directly, without going via Github. But we need to tweak a couple of things first.

Open the file named .gitignore in your apilama directory and add to it. We want git to ignore local_settings, so it stays on our local computer and doesn’t end up on Heroku.


And we commit our changes

$ git status
$ git add -A .
$ git commit -m "additional files and changes for Heroku"

Pick an application name

We’ll be making your blog available on the Web at [your blog's name], so we need to choose a name that nobody else has taken. This name doesn’t need to be related to the Django blogapp or to mysite or anything we’ve created so far. The name can be anything you want, but Heroku is quite strict as to what characters you can use: you’re only allowed to use simple lowercase letters (no capital letters or accents), numbers, and dashes (-).

Once you’ve thought of a name (maybe something with your name or nickname in it), run this command, replacing apilamablog with your own application name:

$ heroku create apilamablog

: Remember to replace apilamablog with the name of your application on Heroku.

If you can’t think of a name, you can instead run

$ heroku create

and Heroku will pick an unused name for you (probably something like enigmatic-cove-2527).

If you ever feel like changing the name of your Heroku application, you can do so at any time with this command (replace the-new-name with the new name you want to use):

$ heroku apps:rename the-new-name

: Remember that after you change your application’s name, you’ll need to visit [the-new-name] to see your site.

Deploy to Heroku!

That was a lot of configuration and installing, right? But you only need to do that once! Now you can deploy!

When you ran heroku create, it automatically added the Heroku remote for our app to our repository. Now we can do a simple git push to deploy our application:

$ git push heroku master

: This will probably produce a lot of output the first time you run it, as Heroku compiles and installs psycopg. You’ll know it’s succeeded if you see something like deployed to Heroku near the end of the output.

Visit your application

You’ve deployed your code to Heroku, and specified the process types in a Procfile (we chose a webprocess type earlier). We can now tell Heroku to start this web process.

To do that, run the following command:

$ heroku ps:scale web=1

This tells Heroku to run just one instance of our web process. Since our blog application is quite simple, we don’t need too much power and so it’s fine to run just one process. It’s possible to ask Heroku to run more processes (by the way, Heroku calls these processes “Dynos” so don’t be surprised if you see this term) but it will no longer be free.

We can now visit the app in our browser with heroku open.

$ heroku open

: you will see an error page! We’ll talk about that in a minute.

This will open a url like in your browser, and at the moment you will probably see an error page.

The error you saw was because we when we deployed to Heroku, we created a new database and it’s empty. We need to run the migrate and createsuperuser commands, just like we did on PythonAnywhere. This time, they come via a special command-line on our own computer, heroku run:

$ heroku run python migrate

$ heroku run python createsuperuser

The command prompt will ask you to choose a username and a password again. These will be your login details on your live website’s admin page.

Refresh it in your browser, and there you go! You now know how to deploy to two different hosting platforms. Pick your favourite 🙂


APIs for Authentication: A journey

Application Program Interface (API) key authentication is a technique that overcomes the hurdles of using shared credentials by using a unique key for each user. The key is usually in the form of a long series of letters and numbers that are different from the account login password. The owner provides the client with the key that helps the client access a website. When a client provides the said API key, the server allows the client to access data. The server has the power to limit administrative functions to any client for example in changing passwords, or deletion of accounts. API keys are sometimes used so that account passwords do not have to be given again and again. The APIs offer flexibility to limit control while also protecting user passwords.

API keys work a lot of different ways as they were conceived by multiple companies and they all have a different way of authentication. There are some API keys like Basic Auth that uses an established standard along with some strict rules. However, over time some familiar approaches are being used. These include putting the key in the Authorization header accompanying the username and password, another one just demands to add the key onto the URL. Sometimes keys are buried in the request body together with the data. Wherever the key is added the outcome is the same, the server provides access to the user.

There are different security protocols being used like OAuth1.0a, Basic API authentication with TLS and OAuth2.0. Basic Auth is the simplest because it only uses the standard framework or language library. Because it is the simplest hence, it offers the least security and provides no advanced options, you are simply providing a username and password that is Base64 encoded.

OAuth1.0a has, on the other hand, the most secure security protocol as it uses a cryptographic signature, combined with a token secret, none and other request based information. As the token is never directly passed across the wire so there is no possibility of anyone seeing a password in transit, this provides an edge to OAuth1.0a. On the other hand, this level of security comes with a lot of complexity. You have to use hashing algorithms with strict steps, but now this problem has been overcome as every other programming language can do it for you.

Repose is another API authentication platform that provides open source API validation, HTTP request logging, rate limiting and much more. It employs a RESTful middleware platform that is easily scalable and extensible. OAuth2.0 and Auth0 are both open sources API authenticators. Both have a completely different approach from OAuth1.0a. The encryptions are handled by TLS (previously called SSL) rather than using cryptographic algorithms. There are not that many OAuth2.0 libraries so this provides a disadvantage to users. OAuth2.0 is used by big names like Google and Twitter.

Auth0 is a platform that allows authentication of apps and supports just about all identity providers on any device or cloud. It uses a secure HTTPs API key to integrating with other tools giving it a seamless experience. It provides the clients with the ability to authenticate with credentials that they are comfortable with.

Many management platforms for API are available, each platform bringing something unique on the table. Kong is an API manager that offers a range of plugins to improve security, better authentication services and management of inbound and outbound traffic. Kong acts as a gateway between the client and the API, providing different layers of rate limiting, logging, and authentication.

3Scale is another manager that separates traffic control and management layers, as a result it produces superior and unsurpassed scalability. It integrates many gateway deployments with Amazon, Heroku, and Red HatOpenshift, which are free to use. Additionally, plugins can also be added to libraries built in several different languages and they design custom API management protocols for organizations as well. Microsoft Azure also provides a host of options for users so that little effort is done on the client’s part and most of the work is accomplished by managers. Azure uses a professional front end and developer portal that make it more user-friendly. It offers the greatest number of options for APIs and thus attracts more clients.

Del Boomi can be thought of as a cloud middleware, plumbing between applications that reside on cloud or premise. They can efficiently manage data for social networks and other uses. Boomi communicates with data across different or common domains, giving it an added advantage. MuleSoft is another API manager that makes use of Anypoint platform, thus it re-architects the SOA infrastructure covering legacy systems, proprietary platforms, and custom integration. This results in a strong and agile business solution for their clients.

AWS cognito is another management system offered by Amazon web services. They offer an adaptive multi-layer design that includes products which ensure availability and resilience. AWS cognito is built with security as its key feature. It can be easily deployed on any platform, using lock library or custom build implementation that can be chosen from more than 50 integrations. It enables clients to authorize users through an external identity provider that assigns temporary security credentials for users to access your website/app. It employs external identity providers that support OpenID, SAML, and the option to integrate your own identity provider.

Recently, API has found its applications in health-related fields. A vast majority of healthcare providers and other companies in the healthcare industry are making use of the web and mobile services. They provide vital information to patients and help them share information with other prescribers. Medical APIs will also help with the integration between partner providers, patient support services, insurance companies and government agencies. But are these API’s HIPPA compliant is a question many users have. Yes, there are many providers that meet the challenge of conforming to client demands while also ensuring the security of medical data.

Apigee Edge, another platform enhances digital value chain from the back end for customers who engage an app. It is HIPPA (Health Insurance Portability and Accountability Act) and PCI compliant. Apigee maintains management compliance by a number of features that include, encrypting and masking information, protecting traffic and managing and securing all data.

For healthcare providers, there are other API managers that provide HIPPA compliance like TrueVault. TrueVault acts as an interface between internal data and external applications. For instance, if a diagnostic laboratory wants to provide online viewing of test results, by making use of TrueVault they can allow approved third parties to access that information without the use of custom APIs or hooks. Hence, it provides a secure service that not only saves time but delivers information to the patients via mobile and tablet interfaces.

Still, there are many challenges that API managers face in making optimized solutions for the healthcare sector. Lack of access to effective tools required for testing and monitoring these interfaces are a serious obstacle for the developers. Furthermore, the developers lack insight and feedbacks in medical APIs which is a critical factor in developing elaborate and engaging APIs that will be widely adopted by the medical field.

Related Links:

  1. Apigee management compliance.

  1. MuleSoft API manager

  1. TrueVault Systems

  1. Microsoft Azure

  1. Del Boomi

  1. Kong API manager

  1. 3Scale management

  1. Akana API management solutions

  1. Auth0

  1. Repose API manager

  1. OAuth2.0

  1. OAuth1.0a


Build Your Own Udemy

Today we all are living in technological driven world where online learning has become an important and totally worthwhile way of learning on-the-go. Now our future of higher education lies in the hand of the online learning system. Nowadays college and university students find themselves burdened with Jobs and family commitments and having an option of studying at their own time has become a critically important part of their life, as its very convenient and less expensive for most of the students moreover, You can work on any course just about anywhere you have computer access.

Because of the expanding trend of online learning platforms like Udemy, khan academy, now the question arises is that how can we make our own online learning platform, what are the core technologies involved in developing such systems, the answer to that is Application programming interface (APIs). APIs are sets of instructions or requirements that govern how one application can communicate with another.

The function of an API is usually fairly straightforward and simple, the process of choosing which type of API to build, understanding why that type of API is appropriate for your application, and then designing it to work effectively is the key to giving your API a fairly long life and making sure that it’s used by developers.

There are many types of APIs available. For example, you may have heard of Java APIs or interfaces within different classes that let objects interact with each other in the Java programming language. Along with program-centric APIs, we also have Web APIs like the Simple Object Access Protocol (SOAP), Remote Procedure Call (RPC), and the most popular at least in name, Representational State Transfer (REST).

There are more than one alternatives

If you’re looking for building your own platform for e-learning like Udemy, it’s important to decide which type of method you have in mind for the delivering lectures of courses that are offered, it can be audio, video or simple text. Video lectures are more in trend these days so now it’s important to know  how to make your own live streaming videos for course lectures, there are a lot of APIs that can offer to make an application that is user friendly and fast but for specific live video streaming Castasy is a good way of doing so as it’s a  cost efficient solution that has arrived in the form of a software  This new live streaming software comes with compatible versions for both iOS and Android devices and also comes in a desktop version. The software basically allows the user to have an application and website that could stream live videos with their own live streaming software. The user is capable of allowing access or denying access to any follower. Each video gets a separate URL and posting that specific URL in their browser, users can view the video at their desktops with the website version of that software. With different URLs users have the facility to view a number of videos available in the website version of the software The live streaming software also withholds a chat feature facilitating viewers to chat on videos as they are streamed so they can discuss relevant topics related to that video it’s a very good feature for e-academies as it helps the students to discuss different queries through chat.

Now if we talk about the most popular, known and very efficient API developer Citrix, Gotowebinar, and Udemy usually comes into the person’s mind now let’s look at them one by one and in detail.

What Citrix basically do is that these applications are streamed from a centralized specific location into an isolated environment where they are executed on different target devices. Application configuration, settings, and relevant files are copied to the client device. When you start the session virtualization, applications are delivered from hosting servers in the data center with the help of application streaming. The user is then connected to the server to which that specific application was delivered. The application is then executed on the server, and the server power is maximized. While the server receives mouse clicks and random keyboard strokes, it sends all the screen updates to the end user device.

GoToWebinar is a purpose-built for do-it-yourself Webinars, making it easy for multinational organizations to deliver their message to thousands of people at the same time, eliminating costly travel or expensive marketing promotions. Innovative in-session features and reports help businesses evaluate the success of their Webinars and to judge whether it was successful or not .it’s actually a Citrix production but it’s usually considered as a different API.

If we look at Udemy as an API we see that Depending on our intended use case, we may be interested in creating our own courses, basically our own platforms for e-learning, it helps us in developing that certain stage, we can consume premium courses, or develop our own through Udemy it’s an easy way to provide services online and earn a little bit of fortune.

API’s pricing benefits Availability
Gotowebinar For starters, it costs $89.00 and can provide services for up to 100 participants

For Pro it costs

$199.00/mo and can entertain up to

500 Participants

For plus it costs $429.00 and can provide services for

2000 Participants

·      Reliable

·      Ease of use

·      Cost efficient

·      Saves time and money that is otherwise consumed on marketing

Easily available in the US and outside of US
Citrix It ranges between 80$ to 500$ ·      standardized, common setup.

·      compress the data

·      it’ll encrypt the data for security

·      the performance is faster

·      centralized management

Easily available all around the globe
Udemy ·        list prices of Udemy range between $20 – $200.

·       Larger discounts are offered.

·       We can run promotions if different courses in 10 to 15$



  • The ability to create your own courses
  • The easiest opportunity to centralize your training materials
  • Easy Management of users and courses


Available all around the world


It is not as hard as you may think

Every API technologies have a lot of benefits and mostly are available all around the globe if we want to build our own e-learning platform it’s easier to utilize these APIs rather than developing our own, as its cost efficient and gives us all the desirable features whether it’s online streaming of lectures or publicity of a certain seminar they provide us with every feature necessary to develop our own Udemy .


Cloud Computing is every #Startup’s #CTO best friend

The needs of a startup:

Chief technology officers play a major role in managing the technical aspects of a company, especially for startups. The requirements of a company in the early stages differ considerably from its requirements in the later stage. For most startups, the initial period is turbulent; the market waters harsh and finding loyal partnerships cumbersome. For CTOs, this period can be exceedingly stressful, they have to manage and ensure the entire operation of the company runs smoothly at every point. As the world advances into digital zones, the burden on CTOs has increased. Initially, the company may hire a lot of IT professionals to take care of technology needs, however, as time goes on, these professionals would be cut down and some advance and take on more responsibility. The later stages of a startup are more secure and stable, by this point CTOs already have their strategy in motion, they have hired professions to handle technology work and their major role lies in super vision. However, during the middle region, CTOs can face numerous challenges. From finding the right balance in the company, managing resources, storing data, keeping the company wired, operational and connected to the market, can be a hurdle. However, diligent CTOs manage the company needs, keeping their eye on the end price.

The role of a CTO

Chief Technology officers are required to maintain the smooth functionality of technology, while reducing expenses of the company. Micro-level events are exceptionally useful for CTOs and they are always on the look at for changes that might occur at this level. For example, ways in which digital technology can be improved. Since data is the basic tool of most companies, CTOs often look for ways to improve high data throughput. The technology market and all its innovations are always under the radar of Chief technology officers. These people do not invest impulsively; rather make calculated decisions to ensure that every investment results in incremental growth and money savings for the company. CTOs look at market trends and environments, the evolutions that take place and the competition they face in the market. Moreover, these officers pay diligent attention to customer preferences and buying habits. These two aspects show the company how to market products so they become more appealing to customers. These customer needs are evaluated on a five year basis, as customer preferences change only slightly during this time frame. However, if certain technological advances make big waves in the market environment, then CTO’s are required to change their strategies accordingly.  While these are the basic requirements and credentials of CTO’s, hiring equally qualified tech experts also falls under their domain. CTO’s are also required to manage their team, and ensure every department and their technology needs are fulfilled, and run smoothly at all times.



What is Cloud Computing?

Cloud computing or internet based computing is on demand access to a number of configurable computer resources. These resources can include computer networks, data, storage, servers, applications and other services. The services can be dispatched with minimum management, and are normally safer, and more reliable for data needs. Cloud storage and computing give customers and companies the platform to safely store their data, privately and even remotely. In some cases outsourced companies may be involved in providing the services, however, other cloud based computing are very personalized.

Cloud computing and services can really reduce the cost of the technology infrastructure of a company. For startup companies, the costs are already high and initial revenue low, hence for such companies, cloud computing provides and easy, accessible and cheap option, as they do not need to buy separate servers. By taking care of the IT needs of organizations, it gives companies the leverage to focus on central issues and core business goals. Moreover, it allows CTOs to manage the technology needs faster, more professionally, and in a systematic manner. When such professionals have to take care of big data and services on a daily basis, they rarely find the time to focus on more important issues at hand, managing the technology resources. Moreover, since these servers are outsourced, maintenance costs are negligible for the company. In addition, it also reduces the personnel need of a company, and hence cuts costs considerably.

While cloud computing can offer a range of benefits to companies, there are some draw backs as well. Public cloud computing options are very risky, and in the past, there have been countless breaches that have resulted in loss of personal information from companies. This information can include sensitive credit card information, employee details or any company data. Usually hackers release such information on social media outlets, and this can cause the public image of a company to be in jeopardy. There have been numerous documented cases of theft and cyber hacking on public cloud computing, however, it is uncommon in Private cloud computing. None the less, the risks associated are very high, and due to the remote nature of the vice, the criminal can be very hard to track down.

Cloud Computing for CTOs: Design solutions in Cloud

Cloud Computing can offer a lot to companies, especially CTOs. Not only are there many cost saving benefits of employing such a service, but, most technology aspects of the company get assisted by the service. Cloud computing solutions are cheaper for companies, and by outsourcing data and IT needs, CTOs can focus on what truly matters, designing solutions to run the company seamlessly. The data becomes much easier to manage for officers, becomes more transparent and storage issues rarely arise.

Amazon’s CTO, Werner Vogel has already spoken about the benefits he has reaped from cloud computing in his company. Vogel advocated the services in a conference, stating, “the cloud has nothing to do with technology, the cloud is defined by all its benefits”.

While apps and gadgets can take care of data storage needs, for companies and startups the cost of download could be great, by investing in cloud services, this downtime can be prevented.  According to Vogel, if Cloud services lower their costs and make tackle privacy issues, companies would advance at an alarming rate.