Find out Comparing Cloud Managed NoSQL Databases

Azure Cosmos DB is a globally distributed, multi-model database service provided by Microsoft Azure. It provides a highly available and scalable NoSQL database that can handle a variety of data types and workloads. Similarly, Google Cloud Platform (GCP) offers Cloud Firestore, Amazon Web Services (AWS) offers Amazon DynamoDB, and Oracle Cloud offers Oracle NoSQL Database as their equivalent managed NoSQL database services.

Similarities

All services are fully managed and provide automatic scaling, high availability, and global distribution.
All services support NoSQL data models and offer flexible data structures.
All services provide SDKs and APIs for various programming languages for easy integration and development.

Differences

Azure Cosmos DB supports multiple APIs (MongoDB, Cassandra, SQL, Gremlin, and Azure Table Storage), while GCP Cloud Firestore, AWS DynamoDB, and Oracle NoSQL DB have their own APIs.
Cosmos DB supports the global distribution of data to any Azure region, while AWS DynamoDB supports regional distribution with read replicas in other regions.
Cosmos DB offers tunable consistency levels, while GCP Firestore and AWS DynamoDB offer strong consistency by default.
Oracle NoSQL DB supports data encryption and Oracle Key Vault integration.

Real-time use cases

Microsoft uses Cosmos DB to support Azure services such as Azure IoT Hub, Azure Time Series Insights, and Azure Cognitive Services.
Snapchat uses Google Cloud Firestore to power its real-time chat feature.
Netflix uses AWS DynamoDB to store metadata for its streaming service.
AT&T uses Oracle NoSQL Database to store and manage large volumes of customer data.

Azure Cosmos DB, GCP Cloud Firestore, AWS DynamoDB, and Oracle NoSQL Database are all managed NoSQL database services that offer scalable, highly available, and globally distributed solutions for modern applications. While there are similarities between these services, such as flexible data models and support for various programming languages, there are differences in API support, consistency models, and distribution capabilities. Real-time use cases demonstrate the diverse industries and applications that utilize these services, including IoT, real-time chat, metadata storage, and customer data management. Ultimately, organizations should choose the managed NoSQL database service that best fits their needs based on their specific requirements and use cases.

Original article source at: https://www.c-sharpcorner.com

#nosql #databases #cloud

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Find out Comparing Cloud Managed NoSQL Databases

Adaline Kulas

1594162500

Multi-cloud Spending: 8 Tips To Lower Cost

A multi-cloud approach is nothing but leveraging two or more cloud platforms for meeting the various business requirements of an enterprise. The multi-cloud IT environment incorporates different clouds from multiple vendors and negates the dependence on a single public cloud service provider. Thus enterprises can choose specific services from multiple public clouds and reap the benefits of each.

Given its affordability and agility, most enterprises opt for a multi-cloud approach in cloud computing now. A 2018 survey on the public cloud services market points out that 81% of the respondents use services from two or more providers. Subsequently, the cloud computing services market has reported incredible growth in recent times. The worldwide public cloud services market is all set to reach $500 billion in the next four years, according to IDC.

By choosing multi-cloud solutions strategically, enterprises can optimize the benefits of cloud computing and aim for some key competitive advantages. They can avoid the lengthy and cumbersome processes involved in buying, installing and testing high-priced systems. The IaaS and PaaS solutions have become a windfall for the enterprise’s budget as it does not incur huge up-front capital expenditure.

However, cost optimization is still a challenge while facilitating a multi-cloud environment and a large number of enterprises end up overpaying with or without realizing it. The below-mentioned tips would help you ensure the money is spent wisely on cloud computing services.

Deactivate underused or unattached resources

Most organizations tend to get wrong with simple things which turn out to be the root cause for needless spending and resource wastage. The first step to cost optimization in your cloud strategy is to identify underutilized resources that you have been paying for.

Enterprises often continue to pay for resources that have been purchased earlier but are no longer useful. Identifying such unused and unattached resources and deactivating it on a regular basis brings you one step closer to cost optimization. If needed, you can deploy automated cloud management tools that are largely helpful in providing the analytics needed to optimize the cloud spending and cut costs on an ongoing basis.

Figure out idle instances

Another key cost optimization strategy is to identify the idle computing instances and consolidate them into fewer instances. An idle computing instance may require a CPU utilization level of 1-5%, but you may be billed by the service provider for 100% for the same instance.

Every enterprise will have such non-production instances that constitute unnecessary storage space and lead to overpaying. Re-evaluating your resource allocations regularly and removing unnecessary storage may help you save money significantly. Resource allocation is not only a matter of CPU and memory but also it is linked to the storage, network, and various other factors.

Deploy monitoring mechanisms

The key to efficient cost reduction in cloud computing technology lies in proactive monitoring. A comprehensive view of the cloud usage helps enterprises to monitor and minimize unnecessary spending. You can make use of various mechanisms for monitoring computing demand.

For instance, you can use a heatmap to understand the highs and lows in computing visually. This heat map indicates the start and stop times which in turn lead to reduced costs. You can also deploy automated tools that help organizations to schedule instances to start and stop. By following a heatmap, you can understand whether it is safe to shut down servers on holidays or weekends.

#cloud computing services #all #hybrid cloud #cloud #multi-cloud strategy #cloud spend #multi-cloud spending #multi cloud adoption #why multi cloud #multi cloud trends #multi cloud companies #multi cloud research #multi cloud market

Anissa Barrows

1669099573

What Is Face Recognition? Facial Recognition with Python and OpenCV

In this article, we will know what is face recognition and how is different from face detection. We will go briefly over the theory of face recognition and then jump on to the coding section. At the end of this article, you will be able to make a face recognition program for recognizing faces in images as well as on a live webcam feed.

What is Face Detection?

In computer vision, one essential problem we are trying to figure out is to automatically detect objects in an image without human intervention. Face detection can be thought of as such a problem where we detect human faces in an image. There may be slight differences in the faces of humans but overall, it is safe to say that there are certain features that are associated with all the human faces. There are various face detection algorithms but Viola-Jones Algorithm is one of the oldest methods that is also used today and we will use the same later in the article. You can go through the Viola-Jones Algorithm after completing this article as I’ll link it at the end of this article.

Face detection is usually the first step towards many face-related technologies, such as face recognition or verification. However, face detection can have very useful applications. The most successful application of face detection would probably be photo taking. When you take a photo of your friends, the face detection algorithm built into your digital camera detects where the faces are and adjusts the focus accordingly.

For a tutorial on Real-Time Face detection

What is Face Recognition?

face recognition

Now that we are successful in making such algorithms that can detect faces, can we also recognise whose faces are they?

Face recognition is a method of identifying or verifying the identity of an individual using their face. There are various algorithms that can do face recognition but their accuracy might vary. Here I am going to describe how we do face recognition using deep learning.

So now let us understand how we recognise faces using deep learning. We make use of face embedding in which each face is converted into a vector and this technique is called deep metric learning. Let me further divide this process into three simple steps for easy understanding:

Face Detection: The very first task we perform is detecting faces in the image or video stream. Now that we know the exact location/coordinates of face, we extract this face for further processing ahead.

Feature Extraction: Now that we have cropped the face out of the image, we extract features from it. Here we are going to use face embeddings to extract the features out of the face. A neural network takes an image of the person’s face as input and outputs a vector which represents the most important features of a face. In machine learning, this vector is called embedding and thus we call this vector as face embedding. Now how does this help in recognizing faces of different persons?

While training the neural network, the network learns to output similar vectors for faces that look similar. For example, if I have multiple images of faces within different timespan, of course, some of the features of my face might change but not up to much extent. So in this case the vectors associated with the faces are similar or in short, they are very close in the vector space. Take a look at the below diagram for a rough idea:

Now after training the network, the network learns to output vectors that are closer to each other(similar) for faces of the same person(looking similar). The above vectors now transform into:

We are not going to train such a network here as it takes a significant amount of data and computation power to train such networks. We will use a pre-trained network trained by Davis King on a dataset of ~3 million images. The network outputs a vector of 128 numbers which represent the most important features of a face.

Now that we know how this network works, let us see how we use this network on our own data. We pass all the images in our data to this pre-trained network to get the respective embeddings and save these embeddings in a file for the next step.

Comparing faces: Now that we have face embeddings for every face in our data saved in a file, the next step is to recognise a new t image that is not in our data. So the first step is to compute the face embedding for the image using the same network we used above and then compare this embedding with the rest of the embeddings we have. We recognise the face if the generated embedding is closer or similar to any other embedding as shown below:

So we passed two images, one of the images is of Vladimir Putin and other of George W. Bush. In our example above, we did not save the embeddings for Putin but we saved the embeddings of Bush. Thus when we compared the two new embeddings with the existing ones, the vector for Bush is closer to the other face embeddings of Bush whereas the face embeddings of Putin are not closer to any other embedding and thus the program cannot recognise him.

What is OpenCV

In the field of Artificial Intelligence, Computer Vision is one of the most interesting and Challenging tasks. Computer Vision acts like a bridge between Computer Software and visualizations around us. It allows computer software to understand and learn about the visualizations in the surroundings. For Example: Based on the color, shape and size determining the fruit. This task can be very easy for the human brain however in the Computer Vision pipeline, first we gather the data, then we perform the data processing activities and then we train and teach the model to understand how to distinguish between the fruits based on size, shape and color of fruit.

Currently, various packages are present to perform machine learning, deep learning and computer vision tasks. By far, computer vision is the best module for such complex activities. OpenCV is an open-source library. It is supported by various programming languages such as R, Python. It runs on most of the platforms such as Windows, Linux and MacOS.

To know more about how face recognition works on opencv, check out the free course on face recognition in opencv.

Advantages of OpenCV:

OpenCV is an open-source library and is free of cost.
As compared to other libraries, it is fast since it is written in C/C++.
It works better on System with lesser RAM
To supports most of the Operating Systems such as Windows, Linux and MacOS.

Installation:

Here we will be focusing on installing OpenCV for python only. We can install OpenCV using pip or conda(for anaconda environment).

Using pip:

Using pip, the installation process of openCV can be done by using the following command in the command prompt.

pip install opencv-python

Anaconda:

If you are using anaconda environment, either you can execute the above code in anaconda prompt or you can execute the following code in anaconda prompt.

conda install -c conda-forge opencv

Face Recognition using Python

In this section, we shall implement face recognition using OpenCV and Python. First, let us see the libraries we will need and how to install them:

OpenCV
dlib
Face_recognition

OpenCV is an image and video processing library and is used for image and video analysis, like facial detection, license plate reading, photo editing, advanced robotic vision, optical character recognition, and a whole lot more.

The dlib library, maintained by Davis King, contains our implementation of “deep metric learning” which is used to construct our face embeddings used for the actual recognition process.

The face_recognition library, created by Adam Geitgey, wraps around dlib’s facial recognition functionality, and this library is super easy to work with and we will be using this in our code. Remember to install dlib library first before you install face_recognition.

To install OpenCV, type in command prompt

pip install opencv-python

I have tried various ways to install dlib on Windows but the easiest of all of them is via Anaconda. First, install Anaconda (here is a guide to install it) and then use this command in your command prompt:

conda install -c conda-forge dlib

Next to install face_recognition, type in command prompt

pip install face_recognition

Now that we have all the dependencies installed, let us start coding. We will have to create three files, one will take our dataset and extract face embedding for each face using dlib. Next, we will save these embedding in a file.

In the next file we will compare the faces with the existing the recognise faces in images and next we will do the same but recognise faces in live webcam feed

Extracting features from Face

First, you need to get a dataset or even create one of you own. Just make sure to arrange all images in folders with each folder containing images of just one person.

Next, save the dataset in a folder the same as you are going to make the file. Now here is the code:

from imutils import paths

import face_recognition

import pickle

import cv2

import os

#get paths of each file in folder named Images

#Images here contains my data(folders of various persons)

imagePaths = list(paths.list_images('Images'))

knownEncodings = []

knownNames = []

# loop over the image paths

for (i, imagePath) in enumerate(imagePaths):

# extract the person name from the image path

name = imagePath.split(os.path.sep)[-2]

# load the input image and convert it from BGR (OpenCV ordering)

# to dlib ordering (RGB)

image = cv2.imread(imagePath)

rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

#Use Face_recognition to locate faces

boxes = face_recognition.face_locations(rgb,model='hog')

# compute the facial embedding for the face

encodings = face_recognition.face_encodings(rgb, boxes)

# loop over the encodings

for encoding in encodings:

knownEncodings.append(encoding)

knownNames.append(name)

#save emcodings along with their names in dictionary data

data = {"encodings": knownEncodings, "names": knownNames}

#use pickle to save data into a file for later use

f = open("face_enc", "wb")

f.write(pickle.dumps(data))

f.close()

Now that we have stored the embedding in a file named “face_enc”, we can use them to recognise faces in images or live video stream.

Face Recognition in Live webcam Feed

Here is the script to recognise faces on a live webcam feed:

import face_recognition

import imutils

import pickle

import time

import cv2

import os

#find path of xml file containing haarcascade file

cascPathface = os.path.dirname(

cv2.__file__) + "/data/haarcascade_frontalface_alt2.xml"

# load the harcaascade in the cascade classifier

faceCascade = cv2.CascadeClassifier(cascPathface)

# load the known faces and embeddings saved in last file

data = pickle.loads(open('face_enc', "rb").read())

print("Streaming started")

video_capture = cv2.VideoCapture(0)

# loop over frames from the video file stream

while True:

# grab the frame from the threaded video stream

ret, frame = video_capture.read()

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

faces = faceCascade.detectMultiScale(gray,

scaleFactor=1.1,

minNeighbors=5,

minSize=(60, 60),

flags=cv2.CASCADE_SCALE_IMAGE)

# convert the input frame from BGR to RGB

rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# the facial embeddings for face in input

encodings = face_recognition.face_encodings(rgb)

names = []

# loop over the facial embeddings incase

# we have multiple embeddings for multiple fcaes

for encoding in encodings:

#Compare encodings with encodings in data["encodings"]

#Matches contain array with boolean values and True for the embeddings it matches closely

#and False for rest

matches = face_recognition.compare_faces(data["encodings"],

encoding)

#set name =inknown if no encoding matches

name = "Unknown"

# check to see if we have found a match

if True in matches:

#Find positions at which we get True and store them

matchedIdxs = [i for (i, b) in enumerate(matches) if b]

counts = {}

# loop over the matched indexes and maintain a count for

# each recognized face face

for i in matchedIdxs:

#Check the names at respective indexes we stored in matchedIdxs

name = data["names"][i]

#increase count for the name we got

counts[name] = counts.get(name, 0) + 1

#set name which has highest count

name = max(counts, key=counts.get)

# update the list of names

names.append(name)

# loop over the recognized faces

for ((x, y, w, h), name) in zip(faces, names):

# rescale the face coordinates

# draw the predicted face name on the image

cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

cv2.putText(frame, name, (x, y), cv2.FONT_HERSHEY_SIMPLEX,

0.75, (0, 255, 0), 2)

cv2.imshow("Frame", frame)

if cv2.waitKey(1) & 0xFF == ord('q'):

break

video_capture.release()

cv2.destroyAllWindows()

https://www.youtube.com/watch?v=fLnGdkZxRkg

Although in the example above we have used haar cascade to detect faces, you can also use face_recognition.face_locations to detect a face as we did in the previous script

Face Recognition in Images

The script for detecting and recognising faces in images is almost similar to what you saw above. Try it yourself and if you can’t take a look at the code below:

import face_recognition

import imutils

import pickle

import time

import cv2

import os

#find path of xml file containing haarcascade file

cascPathface = os.path.dirname(

cv2.__file__) + "/data/haarcascade_frontalface_alt2.xml"

# load the harcaascade in the cascade classifier

faceCascade = cv2.CascadeClassifier(cascPathface)

# load the known faces and embeddings saved in last file

data = pickle.loads(open('face_enc', "rb").read())

#Find path to the image you want to detect face and pass it here

image = cv2.imread(Path-to-img)

rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

#convert image to Greyscale for haarcascade

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

faces = faceCascade.detectMultiScale(gray,

scaleFactor=1.1,

minNeighbors=5,

minSize=(60, 60),

flags=cv2.CASCADE_SCALE_IMAGE)

# the facial embeddings for face in input

encodings = face_recognition.face_encodings(rgb)

names = []

# loop over the facial embeddings incase

# we have multiple embeddings for multiple fcaes

for encoding in encodings:

#Compare encodings with encodings in data["encodings"]

#Matches contain array with boolean values and True for the embeddings it matches closely

#and False for rest

matches = face_recognition.compare_faces(data["encodings"],

encoding)

#set name =inknown if no encoding matches

name = "Unknown"

# check to see if we have found a match

if True in matches:

#Find positions at which we get True and store them

matchedIdxs = [i for (i, b) in enumerate(matches) if b]

counts = {}

# loop over the matched indexes and maintain a count for

# each recognized face face

for i in matchedIdxs:

#Check the names at respective indexes we stored in matchedIdxs

name = data["names"][i]

#increase count for the name we got

counts[name] = counts.get(name, 0) + 1

#set name which has highest count

name = max(counts, key=counts.get)

# update the list of names

names.append(name)

# loop over the recognized faces

for ((x, y, w, h), name) in zip(faces, names):

# rescale the face coordinates

# draw the predicted face name on the image

cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)

cv2.putText(image, name, (x, y), cv2.FONT_HERSHEY_SIMPLEX,

0.75, (0, 255, 0), 2)

cv2.imshow("Frame", image)

cv2.waitKey(0)

Output:

InputOutput

This brings us to the end of this article where we learned about face recognition.

You can also upskill with Great Learning’s PGP Artificial Intelligence and Machine Learning Course. The course offers mentorship from industry leaders, and you will also have the opportunity to work on real-time industry-relevant projects.

Original article source at: https://www.mygreatlearning.com

#python #opencv

Johathan McGlynn

1659283860

Activeinteraction: Manage Application Specific Business Logic Of Ruby

ActiveInteraction

ActiveInteraction manages application-specific business logic. It's an implementation of service objects designed to blend seamlessly into Rails.

ActiveInteraction gives you a place to put your business logic. It also helps you write safer code by validating that your inputs conform to your expectations. If ActiveModel deals with your nouns, then ActiveInteraction handles your verbs.

Installation
Basic usage
- Validations
Filters
- Basic Filters
- Advanced Filters
Rails
- Setup
- Controller
Advanced usage
Credits

API Documentation

Installation

Add it to your Gemfile:

gem 'active_interaction', '~> 5.1'

Or install it manually:

$ gem install active_interaction --version '~> 5.1'

This project uses Semantic Versioning. Check out GitHub releases for a detailed list of changes.

Basic usage

To define an interaction, create a subclass of ActiveInteraction::Base. Then you need to do two things:

Define your inputs. Use class filter methods to define what you expect your inputs to look like. For instance, if you need a boolean flag for pepperoni, use boolean :pepperoni. Check out the filters section for all the available options.

Define your business logic. Do this by implementing the #execute method. Each input you defined will be available as the type you specified. If any of the inputs are invalid, #execute won't be run. Filters are responsible for checking your inputs. Check out the validations section if you need more than that.

That covers the basics. Let's put it all together into a simple example that squares a number.

require 'active_interaction'

class Square < ActiveInteraction::Base
  float :x

  def execute
    x**2
  end
end

Call .run on your interaction to execute it. You must pass a single hash to .run. It will return an instance of your interaction. By convention, we call this an outcome. You can use the #valid? method to ask the outcome if it's valid. If it's invalid, take a look at its errors with #errors. In either case, the value returned from #execute will be stored in #result.

outcome = Square.run(x: 'two point one')
outcome.valid?
# => nil
outcome.errors.messages
# => {:x=>["is not a valid float"]}

outcome = Square.run(x: 2.1)
outcome.valid?
# => true
outcome.result
# => 4.41

You can also use .run! to execute interactions. It's like .run but more dangerous. It doesn't return an outcome. If the outcome would be invalid, it will instead raise an error. But if the outcome would be valid, it simply returns the result.

Square.run!(x: 'two point one')
# ActiveInteraction::InvalidInteractionError: X is not a valid float
Square.run!(x: 2.1)
# => 4.41

Validations

ActiveInteraction checks your inputs. Often you'll want more than that. For instance, you may want an input to be a string with at least one non-whitespace character. Instead of writing your own validation for that, you can use validations from ActiveModel.

These validations aren't provided by ActiveInteraction. They're from ActiveModel. You can also use any custom validations you wrote yourself in your interactions.

class SayHello < ActiveInteraction::Base
  string :name

  validates :name,
    presence: true

  def execute
    "Hello, #{name}!"
  end
end

When you run this interaction, two things will happen. First ActiveInteraction will check your inputs. Then ActiveModel will validate them. If both of those are happy, it will be executed.

SayHello.run!(name: nil)
# ActiveInteraction::InvalidInteractionError: Name is required

SayHello.run!(name: '')
# ActiveInteraction::InvalidInteractionError: Name can't be blank

SayHello.run!(name: 'Taylor')
# => "Hello, Taylor!"

Filters

You can define filters inside an interaction using the appropriate class method. Each method has the same signature:

Some symbolic names. These are the attributes to create.

An optional hash of options. Each filter supports at least these two options:

default is the fallback value to use if nil is given. To make a filter optional, set default: nil.

desc is a human-readable description of the input. This can be useful for generating documentation. For more information about this, read the descriptions section.

An optional block of sub-filters. Only array and hash filters support this. Other filters will ignore blocks when given to them.

Let's take a look at an example filter. It defines three inputs: x, y, and z. Those inputs are optional and they all share the same description ("an example filter").

array :x, :y, :z,
  default: nil,
  desc: 'an example filter' do
    # Some filters support sub-filters here.
  end

In general, filters accept values of the type they correspond to, plus a few alternatives that can be reasonably coerced. Typically the coercions come from Rails, so "1" can be interpreted as the boolean value true, the string "1", or the number 1.

Basic Filters

Array

In addition to accepting arrays, array inputs will convert ActiveRecord::Relations into arrays.

class ArrayInteraction < ActiveInteraction::Base
  array :toppings

  def execute
    toppings.size
  end
end

ArrayInteraction.run!(toppings: 'everything')
# ActiveInteraction::InvalidInteractionError: Toppings is not a valid array
ArrayInteraction.run!(toppings: [:cheese, 'pepperoni'])
# => 2

Use a block to constrain the types of elements an array can contain. Note that you can only have one filter inside an array block, and it must not have a name.

array :birthdays do
  date
end

For interface, object, and record filters, the name of the array filter will be singularized and used to determine the type of value passed. In the example below, the objects passed would need to be of type Cow.

array :cows do
  object
end

You can override this by passing the necessary information to the inner filter.

array :managers do
  object class: People
end

Errors that occur will be indexed based on the Rails configuration setting index_nested_attribute_errors. You can also manually override this setting with the :index_errors option. In this state is is possible to get multiple errors from a single filter.

class ArrayInteraction < ActiveInteraction::Base
  array :favorite_numbers, index_errors: true do
    integer
  end

  def execute
    favorite_numbers
  end
end

ArrayInteraction.run(favorite_numbers: [8, 'bazillion']).errors.details
=> {:"favorite_numbers[1]"=>[{:error=>:invalid_type, :type=>"array"}]}

With :index_errors set to false the error would have been:

{:favorite_numbers=>[{:error=>:invalid_type, :type=>"array"}]}

Boolean

Boolean filters convert the strings "1", "true", and "on" (case-insensitive) into true. They also convert "0", "false", and "off" into false. Blank strings will be treated as nil.

class BooleanInteraction < ActiveInteraction::Base
  boolean :kool_aid

  def execute
    'Oh yeah!' if kool_aid
  end
end

BooleanInteraction.run!(kool_aid: 1)
# ActiveInteraction::InvalidInteractionError: Kool aid is not a valid boolean
BooleanInteraction.run!(kool_aid: true)
# => "Oh yeah!"

File

File filters also accept TempFiles and anything that responds to #rewind. That means that you can pass the params from uploading files via forms in Rails.

class FileInteraction < ActiveInteraction::Base
  file :readme

  def execute
    readme.size
  end
end

FileInteraction.run!(readme: 'README.md')
# ActiveInteraction::InvalidInteractionError: Readme is not a valid file
FileInteraction.run!(readme: File.open('README.md'))
# => 21563

Hash

Hash filters accept hashes. The expected value types are given by passing a block and nesting other filters. You can have any number of filters inside a hash, including other hashes.

class HashInteraction < ActiveInteraction::Base
  hash :preferences do
    boolean :newsletter
    boolean :sweepstakes
  end

  def execute
    puts 'Thanks for joining the newsletter!' if preferences[:newsletter]
    puts 'Good luck in the sweepstakes!' if preferences[:sweepstakes]
  end
end

HashInteraction.run!(preferences: 'yes, no')
# ActiveInteraction::InvalidInteractionError: Preferences is not a valid hash
HashInteraction.run!(preferences: { newsletter: true, 'sweepstakes' => false })
# Thanks for joining the newsletter!
# => nil

Setting default hash values can be tricky. The default value has to be either nil or {}. Use nil to make the hash optional. Use {} if you want to set some defaults for values inside the hash.

hash :optional,
  default: nil
# => {:optional=>nil}

hash :with_defaults,
  default: {} do
    boolean :likes_cookies,
      default: true
  end
# => {:with_defaults=>{:likes_cookies=>true}}

By default, hashes remove any keys that aren't given as nested filters. To allow all hash keys, set strip: false. In general we don't recommend doing this, but it's sometimes necessary.

hash :stuff,
  strip: false

String

String filters define inputs that only accept strings.

class StringInteraction < ActiveInteraction::Base
  string :name

  def execute
    "Hello, #{name}!"
  end
end

StringInteraction.run!(name: 0xDEADBEEF)
# ActiveInteraction::InvalidInteractionError: Name is not a valid string
StringInteraction.run!(name: 'Taylor')
# => "Hello, Taylor!"

String filter strips leading and trailing whitespace by default. To disable it, set the strip option to false.

string :comment,
  strip: false

Symbol

Symbol filters define inputs that accept symbols. Strings will be converted into symbols.

class SymbolInteraction < ActiveInteraction::Base
  symbol :method

  def execute
    method.to_proc
  end
end

SymbolInteraction.run!(method: -> {})
# ActiveInteraction::InvalidInteractionError: Method is not a valid symbol
SymbolInteraction.run!(method: :object_id)
# => #<Proc:0x007fdc9ba94118>

Dates and times

Filters that work with dates and times behave similarly. By default, they all convert strings into their expected data types using .parse. Blank strings will be treated as nil. If you give the format option, they will instead convert strings using .strptime. Note that formats won't work with DateTime and Time filters if a time zone is set.

Date

class DateInteraction < ActiveInteraction::Base
  date :birthday

  def execute
    birthday + (18 * 365)
  end
end

DateInteraction.run!(birthday: 'yesterday')
# ActiveInteraction::InvalidInteractionError: Birthday is not a valid date
DateInteraction.run!(birthday: Date.new(1989, 9, 1))
# => #<Date: 2007-08-28 ((2454341j,0s,0n),+0s,2299161j)>

date :birthday,
  format: '%Y-%m-%d'

DateTime

class DateTimeInteraction < ActiveInteraction::Base
  date_time :now

  def execute
    now.iso8601
  end
end

DateTimeInteraction.run!(now: 'now')
# ActiveInteraction::InvalidInteractionError: Now is not a valid date time
DateTimeInteraction.run!(now: DateTime.now)
# => "2015-03-11T11:04:40-05:00"

date_time :start,
  format: '%Y-%m-%dT%H:%M:%S'

Time

In addition to converting strings with .parse (or .strptime), time filters convert numbers with .at.

class TimeInteraction < ActiveInteraction::Base
  time :epoch

  def execute
    Time.now - epoch
  end
end

TimeInteraction.run!(epoch: 'a long, long time ago')
# ActiveInteraction::InvalidInteractionError: Epoch is not a valid time
TimeInteraction.run!(epoch: Time.new(1970))
# => 1426068362.5136619

time :start,
  format: '%Y-%m-%dT%H:%M:%S'

Numbers

All numeric filters accept numeric input. They will also convert strings using the appropriate method from Kernel (like .Float). Blank strings will be treated as nil.

Decimal

class DecimalInteraction < ActiveInteraction::Base
  decimal :price

  def execute
    price * 1.0825
  end
end

DecimalInteraction.run!(price: 'one ninety-nine')
# ActiveInteraction::InvalidInteractionError: Price is not a valid decimal
DecimalInteraction.run!(price: BigDecimal(1.99, 2))
# => #<BigDecimal:7fe792a42028,'0.2165E1',18(45)>

To specify the number of significant digits, use the digits option.

decimal :dollars,
  digits: 2

Float

class FloatInteraction < ActiveInteraction::Base
  float :x

  def execute
    x**2
  end
end

FloatInteraction.run!(x: 'two point one')
# ActiveInteraction::InvalidInteractionError: X is not a valid float
FloatInteraction.run!(x: 2.1)
# => 4.41

Integer

class IntegerInteraction < ActiveInteraction::Base
  integer :limit

  def execute
    limit.downto(0).to_a
  end
end

IntegerInteraction.run!(limit: 'ten')
# ActiveInteraction::InvalidInteractionError: Limit is not a valid integer
IntegerInteraction.run!(limit: 10)
# => [10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]

When a String is passed into an integer input, the value will be coerced. A default base of 10 is used though it may be overridden with the base option. If a base of 0 is provided, the coercion will respect radix indicators present in the string.

class IntegerInteraction < ActiveInteraction::Base
  integer :limit1
  integer :limit2, base: 8
  integer :limit3, base: 0

  def execute
    [limit1, limit2, limit3]
  end
end

IntegerInteraction.run!(limit1: 71, limit2: 71, limit3: 71)
# => [71, 71, 71]
IntegerInteraction.run!(limit1: "071", limit2: "071", limit3: "0x71")
# => [71, 57, 113]
IntegerInteraction.run!(limit1: "08", limit2: "08", limit3: "08")
ActiveInteraction::InvalidInteractionError: Limit2 is not a valid integer, Limit3 is not a valid integer

Advanced Filters

Interface

Interface filters allow you to specify an interface that the passed value must meet in order to pass. The name of the interface is used to look for a constant inside the ancestor listing for the passed value. This allows for a variety of checks depending on what's passed. Class instances are checked for an included module or an inherited ancestor class. Classes are checked for an extended module or an inherited ancestor class. Modules are checked for an extended module.

class InterfaceInteraction < ActiveInteraction::Base
  interface :exception

  def execute
    exception
  end
end

InterfaceInteraction.run!(exception: Exception)
# ActiveInteraction::InvalidInteractionError: Exception is not a valid interface
InterfaceInteraction.run!(exception: NameError) # a subclass of Exception
# => NameError

You can use :from to specify a class or module. This would be the equivalent of what's above.

class InterfaceInteraction < ActiveInteraction::Base
  interface :error,
    from: Exception

  def execute
    error
  end
end

You can also create an anonymous interface on the fly by passing the methods option.

class InterfaceInteraction < ActiveInteraction::Base
  interface :serializer,
    methods: %i[dump load]

  def execute
    input = '{ "is_json" : true }'
    object = serializer.load(input)
    output = serializer.dump(object)

    output
  end
end

require 'json'

InterfaceInteraction.run!(serializer: Object.new)
# ActiveInteraction::InvalidInteractionError: Serializer is not a valid interface
InterfaceInteraction.run!(serializer: JSON)
# => "{\"is_json\":true}"

Object

Object filters allow you to require an instance of a particular class or one of its subclasses.

class Cow
  def moo
    'Moo!'
  end
end

class ObjectInteraction < ActiveInteraction::Base
  object :cow

  def execute
    cow.moo
  end
end

ObjectInteraction.run!(cow: Object.new)
# ActiveInteraction::InvalidInteractionError: Cow is not a valid object
ObjectInteraction.run!(cow: Cow.new)
# => "Moo!"

The class name is automatically determined by the filter name. If your filter name is different than your class name, use the class option. It can be either the class, a string, or a symbol.

object :dolly1,
  class: Sheep
object :dolly2,
  class: 'Sheep'
object :dolly3,
  class: :Sheep

If you have value objects or you would like to build one object from another, you can use the converter option. It is only called if the value provided is not an instance of the class or one of its subclasses. The converter option accepts a symbol that specifies a class method on the object class or a proc. Both will be passed the value and any errors thrown inside the converter will cause the value to be considered invalid. Any returned value that is not the correct class will also be treated as invalid. Any default that is not an instance of the class or subclass and is not nil will also be converted.

class ObjectInteraction < ActiveInteraction::Base
  object :ip_address,
    class: IPAddr,
    converter: :new

  def execute
    ip_address
  end
end

ObjectInteraction.run!(ip_address: '192.168.1.1')
# #<IPAddr: IPv4:192.168.1.1/255.255.255.255>

ObjectInteraction.run!(ip_address: 1)
# ActiveInteraction::InvalidInteractionError: Ip address is not a valid object

Record

Record filters allow you to require an instance of a particular class (or one of its subclasses) or a value that can be used to locate an instance of the object. If the value does not match, it will call find on the class of the record. This is particularly useful when working with ActiveRecord objects. Like an object filter, the class is derived from the name passed but can be specified with the class option. Any default that is not an instance of the class or subclass and is not nil will also be found. Blank strings passed in will be treated as nil.

class RecordInteraction < ActiveInteraction::Base
  record :encoding

  def execute
    encoding
  end
end

> RecordInteraction.run!(encoding: Encoding::US_ASCII)
=> #<Encoding:US-ASCII>

> RecordInteraction.run!(encoding: 'ascii')
=> #<Encoding:US-ASCII>

A different method can be specified by providing a symbol to the finder option.

Rails

ActiveInteraction plays nicely with Rails. You can use interactions to handle your business logic instead of models or controllers. To see how it all works, let's take a look at a complete example of a controller with the typical resourceful actions.

Setup

We recommend putting your interactions in app/interactions. It's also very helpful to group them by model. That way you can look in app/interactions/accounts for all the ways you can interact with accounts.

- app/
  - controllers/
    - accounts_controller.rb
  - interactions/
    - accounts/
      - create_account.rb
      - destroy_account.rb
      - find_account.rb
      - list_accounts.rb
      - update_account.rb
  - models/
    - account.rb
  - views/
    - account/
      - edit.html.erb
      - index.html.erb
      - new.html.erb
      - show.html.erb

Controller

Index

# GET /accounts
def index
  @accounts = ListAccounts.run!
end

Since we're not passing any inputs to ListAccounts, it makes sense to use .run! instead of .run. If it failed, that would mean we probably messed up writing the interaction.

class ListAccounts < ActiveInteraction::Base
  def execute
    Account.not_deleted.order(last_name: :asc, first_name: :asc)
  end
end

Show

Up next is the show action. For this one we'll define a helper method to handle raising the correct errors. We have to do this because calling .run! would raise an ActiveInteraction::InvalidInteractionError instead of an ActiveRecord::RecordNotFound. That means Rails would render a 500 instead of a 404.

# GET /accounts/:id
def show
  @account = find_account!
end

private

def find_account!
  outcome = FindAccount.run(params)

  if outcome.valid?
    outcome.result
  else
    fail ActiveRecord::RecordNotFound, outcome.errors.full_messages.to_sentence
  end
end

This probably looks a little different than you're used to. Rails commonly handles this with a before_filter that sets the @account instance variable. Why is all this interaction code better? Two reasons: One, you can reuse the FindAccount interaction in other places, like your API controller or a Resque task. And two, if you want to change how accounts are found, you only have to change one place.

Inside the interaction, we could use #find instead of #find_by_id. That way we wouldn't need the #find_account! helper method in the controller because the error would bubble all the way up. However, you should try to avoid raising errors from interactions. If you do, you'll have to deal with raised exceptions as well as the validity of the outcome.

class FindAccount < ActiveInteraction::Base
  integer :id

  def execute
    account = Account.not_deleted.find_by_id(id)

    if account
      account
    else
      errors.add(:id, 'does not exist')
    end
  end
end

Note that it's perfectly fine to add errors during execution. Not all errors have to come from checking or validation.

New

The new action will be a little different than the ones we've looked at so far. Instead of calling .run or .run!, it's going to initialize a new interaction. This is possible because interactions behave like ActiveModels.

# GET /accounts/new
def new
  @account = CreateAccount.new
end

Since interactions behave like ActiveModels, we can use ActiveModel validations with them. We'll use validations here to make sure that the first and last names are not blank. The validations section goes into more detail about this.

class CreateAccount < ActiveInteraction::Base
  string :first_name, :last_name

  validates :first_name, :last_name,
    presence: true

  def to_model
    Account.new
  end

  def execute
    account = Account.new(inputs)

    unless account.save
      errors.merge!(account.errors)
    end

    account
  end
end

We used a couple of advanced features here. The #to_model method helps determine the correct form to use in the view. Check out the section on forms for more about that. Inside #execute, we merge errors. This is a convenient way to move errors from one object to another. Read more about it in the errors section.

Create

The create action has a lot in common with the new action. Both of them use the CreateAccount interaction. And if creating the account fails, this action falls back to rendering the new action.

# POST /accounts
def create
  outcome = CreateAccount.run(params.fetch(:account, {}))

  if outcome.valid?
    redirect_to(outcome.result)
  else
    @account = outcome
    render(:new)
  end
end

Note that we have to pass a hash to .run. Passing nil is an error.

Since we're using an interaction, we don't need strong parameters. The interaction will ignore any inputs that weren't defined by filters. So you can forget about params.require and params.permit because interactions handle that for you.

Destroy

The destroy action will reuse the #find_account! helper method we wrote earlier.

# DELETE /accounts/:id
def destroy
  DestroyAccount.run!(account: find_account!)
  redirect_to(accounts_url)
end

In this simple example, the destroy interaction doesn't do much. It's not clear that you gain anything by putting it in an interaction. But in the future, when you need to do more than account.destroy, you'll only have to update one spot.

class DestroyAccount < ActiveInteraction::Base
  object :account

  def execute
    account.destroy
  end
end

Edit

Just like the destroy action, editing uses the #find_account! helper. Then it creates a new interaction instance to use as a form object.

# GET /accounts/:id/edit
def edit
  account = find_account!
  @account = UpdateAccount.new(
    account: account,
    first_name: account.first_name,
    last_name: account.last_name)
end

The interaction that updates accounts is more complicated than the others. It requires an account to update, but the other inputs are optional. If they're missing, it'll ignore those attributes. If they're present, it'll update them.

class UpdateAccount < ActiveInteraction::Base
  object :account

  string :first_name, :last_name,
    default: nil

  validates :first_name,
    presence: true,
    unless: -> { first_name.nil? }
  validates :last_name,
    presence: true,
    unless: -> { last_name.nil? }

  def execute
    account.first_name = first_name if first_name.present?
    account.last_name = last_name if last_name.present?

    unless account.save
      errors.merge!(account.errors)
    end

    account
  end
end

Update

Hopefully you've gotten the hang of this by now. We'll use #find_account! to get the account. Then we'll build up the inputs for UpdateAccount. Then we'll run the interaction and either redirect to the updated account or back to the edit page.

# PUT /accounts/:id
def update
  inputs = { account: find_account! }.reverse_merge(params[:account])
  outcome = UpdateAccount.run(inputs)

  if outcome.valid?
    redirect_to(outcome.result)
  else
    @account = outcome
    render(:edit)
  end
end

Advanced usage

Callbacks

ActiveSupport::Callbacks provides a powerful framework for defining callbacks. ActiveInteraction uses that framework to allow hooking into various parts of an interaction's lifecycle.

class Increment < ActiveInteraction::Base
  set_callback :filter, :before, -> { puts 'before filter' }

  integer :x

  set_callback :validate, :after, -> { puts 'after validate' }

  validates :x,
    numericality: { greater_than_or_equal_to: 0 }

  set_callback :execute, :around, lambda { |_interaction, block|
    puts '>>>'
    block.call
    puts '<<<'
  }

  def execute
    puts 'executing'
    x + 1
  end
end

Increment.run!(x: 1)
# before filter
# after validate
# >>>
# executing
# <<<
# => 2

In order, the available callbacks are filter, validate, and execute. You can set before, after, or around on any of them.

Composition

You can run interactions from within other interactions with #compose. If the interaction is successful, it'll return the result (just like if you had called it with .run!). If something went wrong, execution will halt immediately and the errors will be moved onto the caller.

class Add < ActiveInteraction::Base
  integer :x, :y

  def execute
    x + y
  end
end

class AddThree < ActiveInteraction::Base
  integer :x

  def execute
    compose(Add, x: x, y: 3)
  end
end

AddThree.run!(x: 5)
# => 8

To bring in filters from another interaction, use .import_filters. Combined with inputs, delegating to another interaction is a piece of cake.

class AddAndDouble < ActiveInteraction::Base
  import_filters Add

  def execute
    compose(Add, inputs) * 2
  end
end

Note that errors in composed interactions have a few tricky cases. See the errors section for more information about them.

Defaults

The default value for an input can take on many different forms. Setting the default to nil makes the input optional. Setting it to some value makes that the default value for that input. Setting it to a lambda will lazily set the default value for that input. That means the value will be computed when the interaction is run, as opposed to when it is defined.

Lambda defaults are evaluated in the context of the interaction, so you can use the values of other inputs in them.

# This input is optional.
time :a, default: nil
# This input defaults to `Time.at(123)`.
time :b, default: Time.at(123)
# This input lazily defaults to `Time.now`.
time :c, default: -> { Time.now }
# This input defaults to the value of `c` plus 10 seconds.
time :d, default: -> { c + 10 }

Descriptions

Use the desc option to provide human-readable descriptions of filters. You should prefer these to comments because they can be used to generate documentation. The interaction class has a .filters method that returns a hash of filters. Each filter has a #desc method that returns the description.

class Descriptive < ActiveInteraction::Base
  string :first_name,
    desc: 'your first name'
  string :last_name,
    desc: 'your last name'
end

Descriptive.filters.each do |name, filter|
  puts "#{name}: #{filter.desc}"
end
# first_name: your first name
# last_name: your last name

Errors

ActiveInteraction provides detailed errors for easier introspection and testing of errors. Detailed errors improve on regular errors by adding a symbol that represents the type of error that has occurred. Let's look at an example where an item is purchased using a credit card.

class BuyItem < ActiveInteraction::Base
  object :credit_card, :item
  hash :options do
    boolean :gift_wrapped
  end

  def execute
    order = credit_card.purchase(item)
    notify(credit_card.account)
    order
  end

  private def notify(account)
    # ...
  end
end

Having missing or invalid inputs causes the interaction to fail and return errors.

outcome = BuyItem.run(item: 'Thing', options: { gift_wrapped: 'yes' })
outcome.errors.messages
# => {:credit_card=>["is required"], :item=>["is not a valid object"], :"options.gift_wrapped"=>["is not a valid boolean"]}

Determining the type of error based on the string is difficult if not impossible. Calling #details instead of #messages on errors gives you the same list of errors with a testable label representing the error.

outcome.errors.details
# => {:credit_card=>[{:error=>:missing}], :item=>[{:error=>:invalid_type, :type=>"object"}], :"options.gift_wrapped"=>[{:error=>:invalid_type, :type=>"boolean"}]}

Detailed errors can also be manually added during the execute call by passing a symbol to #add instead of a string.

def execute
  errors.add(:monster, :no_passage)
end

ActiveInteraction also supports merging errors. This is useful if you want to delegate validation to some other object. For example, if you have an interaction that updates a record, you might want that record to validate itself. By using the #merge! helper on errors, you can do exactly that.

class UpdateThing < ActiveInteraction::Base
  object :thing

  def execute
    unless thing.save
      errors.merge!(thing.errors)
    end

    thing
  end
end

When a composed interaction fails, its errors are merged onto the caller. This generally produces good error messages, but there are a few cases to look out for.

class Inner < ActiveInteraction::Base
  boolean :x, :y
end

class Outer < ActiveInteraction::Base
  string :x
  boolean :z, default: nil

  def execute
    compose(Inner, x: x, y: z)
  end
end

outcome = Outer.run(x: 'yes')
outcome.errors.details
# => { :x    => [{ :error => :invalid_type, :type => "boolean" }],
#      :base => [{ :error => "Y is required" }] }
outcome.errors.full_messages.join(' and ')
# => "X is not a valid boolean and Y is required"

Since both interactions have an input called x, the inner error for that input is moved to the x error on the outer interaction. This results in a misleading error that claims the input x is not a valid boolean even though it's a string on the outer interaction.

Since only the inner interaction has an input called y, the inner error for that input is moved to the base error on the outer interaction. This results in a confusing error that claims the input y is required even though it's not present on the outer interaction.

Forms

The outcome returned by .run can be used in forms as though it were an ActiveModel object. You can also create a form object by calling .new on the interaction.

Given an application with an Account model we'll create a new Account using the CreateAccount interaction.

# GET /accounts/new
def new
  @account = CreateAccount.new
end

# POST /accounts
def create
  outcome = CreateAccount.run(params.fetch(:account, {}))

  if outcome.valid?
    redirect_to(outcome.result)
  else
    @account = outcome
    render(:new)
  end
end

The form used to create a new Account has slightly more information on the form_for call than you might expect.

<%= form_for @account, as: :account, url: accounts_path do |f| %>
  <%= f.text_field :first_name %>
  <%= f.text_field :last_name %>
  <%= f.submit 'Create' %>
<% end %>

This is necessary because we want the form to act like it is creating a new Account. Defining to_model on the CreateAccount interaction tells the form to treat our interaction like an Account.

class CreateAccount < ActiveInteraction::Base
  # ...

  def to_model
    Account.new
  end
end

Now our form_for call knows how to generate the correct URL and param name (i.e. params[:account]).

# app/views/accounts/new.html.erb
<%= form_for @account do |f| %>
  <%# ... %>
<% end %>

If you have an interaction that updates an Account, you can define to_model to return the object you're updating.

class UpdateAccount < ActiveInteraction::Base
  # ...

  object :account

  def to_model
    account
  end
end

ActiveInteraction also supports formtastic and simple_form. The filters used to define the inputs on your interaction will relay type information to these gems. As a result, form fields will automatically use the appropriate input type.

Shared input options

It can be convenient to apply the same options to a bunch of inputs. One common use case is making many inputs optional. Instead of setting default: nil on each one of them, you can use with_options to reduce duplication.

with_options default: nil do
  date :birthday
  string :name
  boolean :wants_cake
end

Optional inputs

Optional inputs can be defined by using the :default option as described in the filters section. Within the interaction, provided and default values are merged to create inputs. There are times where it is useful to know whether a value was passed to run or the result of a filter default. In particular, it is useful when nil is an acceptable value. For example, you may optionally track your users' birthdays. You can use the inputs.given? predicate to see if an input was even passed to run. With inputs.given? you can also check the input of a hash or array filter by passing a series of keys or indexes to check.

class UpdateUser < ActiveInteraction::Base
  object :user
  date :birthday,
    default: nil

  def execute
    user.birthday = birthday if inputs.given?(:birthday)
    errors.merge!(user.errors) unless user.save
    user
  end
end

Now you have a few options. If you don't want to update their birthday, leave it out of the hash. If you want to remove their birthday, set birthday: nil. And if you want to update it, pass in the new value as usual.

user = User.find(...)

# Don't update their birthday.
UpdateUser.run!(user: user)

# Remove their birthday.
UpdateUser.run!(user: user, birthday: nil)

# Update their birthday.
UpdateUser.run!(user: user, birthday: Date.new(2000, 1, 2))

Translations

ActiveInteraction is i18n aware out of the box! All you have to do is add translations to your project. In Rails, these typically go into config/locales. For example, let's say that for some reason you want to print everything out backwards. Simply add translations for ActiveInteraction to your hsilgne locale.

# config/locales/hsilgne.yml
hsilgne:
  active_interaction:
    types:
      array: yarra
      boolean: naeloob
      date: etad
      date_time: emit etad
      decimal: lamiced
      file: elif
      float: taolf
      hash: hsah
      integer: regetni
      interface: ecafretni
      object: tcejbo
      string: gnirts
      symbol: lobmys
      time: emit
    errors:
      messages:
        invalid: dilavni si
        invalid_type: '%{type} dilav a ton si'
        missing: deriuqer si

Then set your locale and run interactions like normal.

class I18nInteraction < ActiveInteraction::Base
  string :name
end

I18nInteraction.run(name: false).errors.messages[:name]
# => ["is not a valid string"]

I18n.locale = :hsilgne
I18nInteraction.run(name: false).errors.messages[:name]
# => ["gnirts dilav a ton si"]

Everything else works like an activerecord entry. For example, to rename an attribute you can use attributes.

Here we'll rename the num attribute on an interaction named product:

en:
  active_interaction:
    attributes:
      product:
        num: 'Number'

Credits

ActiveInteraction is brought to you by Aaron Lasseigne. Along with Aaron, Taylor Fausak helped create and maintain ActiveInteraction but has since moved on.

If you want to contribute to ActiveInteraction, please read our contribution guidelines. A complete list of contributors is available on GitHub.

ActiveInteraction is licensed under the MIT License.

Author: AaronLasseigne
Source code: https://github.com/AaronLasseigne/active_interaction
License: MIT license

#ruby

Adaline Kulas

1594166040

What are the benefits of cloud migration? Reasons you should migrate

The moving of applications, databases and other business elements from the local server to the cloud server called cloud migration. This article will deal with migration techniques, requirement and the benefits of cloud migration.

In simple terms, moving from local to the public cloud server is called cloud migration. Gartner says 17.5% revenue growth as promised in cloud migration and also has a forecast for 2022 as shown in the following image.

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Desmond Gerber

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