Hana Juali

Hana Juali

1605238507

Getting Started with the Serverless Framework

Getting started with the Serverless framework

#serverless #programming #developer

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Getting Started with the Serverless Framework
Shubham Ankit

Shubham Ankit

1657081614

How to Automate Excel with Python | Python Excel Tutorial (OpenPyXL)

How to Automate Excel with Python

In this article, We will show how we can use python to automate Excel . A useful Python library is Openpyxl which we will learn to do Excel Automation

What is OPENPYXL

Openpyxl is a Python library that is used to read from an Excel file or write to an Excel file. Data scientists use Openpyxl for data analysis, data copying, data mining, drawing charts, styling sheets, adding formulas, and more.

Workbook: A spreadsheet is represented as a workbook in openpyxl. A workbook consists of one or more sheets.

Sheet: A sheet is a single page composed of cells for organizing data.

Cell: The intersection of a row and a column is called a cell. Usually represented by A1, B5, etc.

Row: A row is a horizontal line represented by a number (1,2, etc.).

Column: A column is a vertical line represented by a capital letter (A, B, etc.).

Openpyxl can be installed using the pip command and it is recommended to install it in a virtual environment.

pip install openpyxl

CREATE A NEW WORKBOOK

We start by creating a new spreadsheet, which is called a workbook in Openpyxl. We import the workbook module from Openpyxl and use the function Workbook() which creates a new workbook.

from openpyxl
import Workbook
#creates a new workbook
wb = Workbook()
#Gets the first active worksheet
ws = wb.active
#creating new worksheets by using the create_sheet method

ws1 = wb.create_sheet("sheet1", 0) #inserts at first position
ws2 = wb.create_sheet("sheet2") #inserts at last position
ws3 = wb.create_sheet("sheet3", -1) #inserts at penultimate position

#Renaming the sheet
ws.title = "Example"

#save the workbook
wb.save(filename = "example.xlsx")

READING DATA FROM WORKBOOK

We load the file using the function load_Workbook() which takes the filename as an argument. The file must be saved in the same working directory.

#loading a workbook
wb = openpyxl.load_workbook("example.xlsx")

 

GETTING SHEETS FROM THE LOADED WORKBOOK

 

#getting sheet names
wb.sheetnames
result = ['sheet1', 'Sheet', 'sheet3', 'sheet2']

#getting a particular sheet
sheet1 = wb["sheet2"]

#getting sheet title
sheet1.title
result = 'sheet2'

#Getting the active sheet
sheetactive = wb.active
result = 'sheet1'

 

ACCESSING CELLS AND CELL VALUES

 

#get a cell from the sheet
sheet1["A1"] <
  Cell 'Sheet1'.A1 >

  #get the cell value
ws["A1"].value 'Segment'

#accessing cell using row and column and assigning a value
d = ws.cell(row = 4, column = 2, value = 10)
d.value
10

 

ITERATING THROUGH ROWS AND COLUMNS

 

#looping through each row and column
for x in range(1, 5):
  for y in range(1, 5):
  print(x, y, ws.cell(row = x, column = y)
    .value)

#getting the highest row number
ws.max_row
701

#getting the highest column number
ws.max_column
19

There are two functions for iterating through rows and columns.

Iter_rows() => returns the rows
Iter_cols() => returns the columns {
  min_row = 4, max_row = 5, min_col = 2, max_col = 5
} => This can be used to set the boundaries
for any iteration.

Example:

#iterating rows
for row in ws.iter_rows(min_row = 2, max_col = 3, max_row = 3):
  for cell in row:
  print(cell) <
  Cell 'Sheet1'.A2 >
  <
  Cell 'Sheet1'.B2 >
  <
  Cell 'Sheet1'.C2 >
  <
  Cell 'Sheet1'.A3 >
  <
  Cell 'Sheet1'.B3 >
  <
  Cell 'Sheet1'.C3 >

  #iterating columns
for col in ws.iter_cols(min_row = 2, max_col = 3, max_row = 3):
  for cell in col:
  print(cell) <
  Cell 'Sheet1'.A2 >
  <
  Cell 'Sheet1'.A3 >
  <
  Cell 'Sheet1'.B2 >
  <
  Cell 'Sheet1'.B3 >
  <
  Cell 'Sheet1'.C2 >
  <
  Cell 'Sheet1'.C3 >

To get all the rows of the worksheet we use the method worksheet.rows and to get all the columns of the worksheet we use the method worksheet.columns. Similarly, to iterate only through the values we use the method worksheet.values.


Example:

for row in ws.values:
  for value in row:
  print(value)

 

WRITING DATA TO AN EXCEL FILE

Writing to a workbook can be done in many ways such as adding a formula, adding charts, images, updating cell values, inserting rows and columns, etc… We will discuss each of these with an example.

 

CREATING AND SAVING A NEW WORKBOOK

 

#creates a new workbook
wb = openpyxl.Workbook()

#saving the workbook
wb.save("new.xlsx")

 

ADDING AND REMOVING SHEETS

 

#creating a new sheet
ws1 = wb.create_sheet(title = "sheet 2")

#creating a new sheet at index 0
ws2 = wb.create_sheet(index = 0, title = "sheet 0")

#checking the sheet names
wb.sheetnames['sheet 0', 'Sheet', 'sheet 2']

#deleting a sheet
del wb['sheet 0']

#checking sheetnames
wb.sheetnames['Sheet', 'sheet 2']

 

ADDING CELL VALUES

 

#checking the sheet value
ws['B2'].value
null

#adding value to cell
ws['B2'] = 367

#checking value
ws['B2'].value
367

 

ADDING FORMULAS

 

We often require formulas to be included in our Excel datasheet. We can easily add formulas using the Openpyxl module just like you add values to a cell.
 

For example:

import openpyxl
from openpyxl
import Workbook

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']

ws['A9'] = '=SUM(A2:A8)'

wb.save("new2.xlsx")

The above program will add the formula (=SUM(A2:A8)) in cell A9. The result will be as below.

image

 

MERGE/UNMERGE CELLS

Two or more cells can be merged to a rectangular area using the method merge_cells(), and similarly, they can be unmerged using the method unmerge_cells().

For example:
Merge cells

#merge cells B2 to C9
ws.merge_cells('B2:C9')
ws['B2'] = "Merged cells"

Adding the above code to the previous example will merge cells as below.

image

UNMERGE CELLS

 

#unmerge cells B2 to C9
ws.unmerge_cells('B2:C9')

The above code will unmerge cells from B2 to C9.

INSERTING AN IMAGE

To insert an image we import the image function from the module openpyxl.drawing.image. We then load our image and add it to the cell as shown in the below example.

Example:

import openpyxl
from openpyxl
import Workbook
from openpyxl.drawing.image
import Image

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']
#loading the image(should be in same folder)
img = Image('logo.png')
ws['A1'] = "Adding image"
#adjusting size
img.height = 130
img.width = 200
#adding img to cell A3

ws.add_image(img, 'A3')

wb.save("new2.xlsx")

Result:

image

CREATING CHARTS

Charts are essential to show a visualization of data. We can create charts from Excel data using the Openpyxl module chart. Different forms of charts such as line charts, bar charts, 3D line charts, etc., can be created. We need to create a reference that contains the data to be used for the chart, which is nothing but a selection of cells (rows and columns). I am using sample data to create a 3D bar chart in the below example:

Example

import openpyxl
from openpyxl
import Workbook
from openpyxl.chart
import BarChart3D, Reference, series

wb = openpyxl.load_workbook("example.xlsx")
ws = wb.active

values = Reference(ws, min_col = 3, min_row = 2, max_col = 3, max_row = 40)
chart = BarChart3D()
chart.add_data(values)
ws.add_chart(chart, "E3")
wb.save("MyChart.xlsx")

Result
image


How to Automate Excel with Python with Video Tutorial

Welcome to another video! In this video, We will cover how we can use python to automate Excel. I'll be going over everything from creating workbooks to accessing individual cells and stylizing cells. There is a ton of things that you can do with Excel but I'll just be covering the core/base things in OpenPyXl.

⭐️ Timestamps ⭐️
00:00 | Introduction
02:14 | Installing openpyxl
03:19 | Testing Installation
04:25 | Loading an Existing Workbook
06:46 | Accessing Worksheets
07:37 | Accessing Cell Values
08:58 | Saving Workbooks
09:52 | Creating, Listing and Changing Sheets
11:50 | Creating a New Workbook
12:39 | Adding/Appending Rows
14:26 | Accessing Multiple Cells
20:46 | Merging Cells
22:27 | Inserting and Deleting Rows
23:35 | Inserting and Deleting Columns
24:48 | Copying and Moving Cells
26:06 | Practical Example, Formulas & Cell Styling

📄 Resources 📄
OpenPyXL Docs: https://openpyxl.readthedocs.io/en/stable/ 
Code Written in This Tutorial: https://github.com/techwithtim/ExcelPythonTutorial 
Subscribe: https://www.youtube.com/c/TechWithTim/featured 

#python 

Monty  Boehm

Monty Boehm

1659453850

Twitter.jl: Julia Package to Access Twitter API

Twitter.jl

A Julia package for interacting with the Twitter API.

Twitter.jl is a Julia package to work with the Twitter API v1.1. Currently, only the REST API methods are supported; streaming API endpoints aren't implemented at this time.

All functions have required arguments for those parameters required by Twitter and an options keyword argument to provide a Dict{String, String} of optional parameters Twitter API documentation. Most function calls will return either a Dict or an Array <: TwitterType. Bad requests will return the response code from the API (403, 404, etc).

DataFrame methods are defined for functions returning composite types: Tweets, Places, Lists, and Users.

Authentication

Before one can make use of this package, you must create an application on the Twitter's Developer Platform.

Once your application is approved, you can access your dashboard/portal to grab your authentication credentials from the "Details" tab of the application.

Note that you will also want to ensure that your App has Read / Write OAuth access in order to post tweets. You can find out more about this on Stack Overflow.

Installation

To install this package, enter ] on the REPL to bring up Julia's package manager. Then add the package:

julia> ]
(v1.7) pkg> add Twitter

Tip: Press Ctrl+C to return to the julia> prompt.

Usage

To run Twitter.jl, enter the following command in your Julia REPL

julia> using Twitter

Then the a global variable has to be declared with the twitterauth function. This function holds the consumer_key(API Key), consumer_secret(API Key Secret), oauth_token(Access Token), and oauth_secret(Access Token Secret) respectively.

twitterauth("6nOtpXmf...", # API Key
            "sES5Zlj096S...", # API Key Secret
            "98689850-Hj...", # Access Token
            "UroqCVpWKIt...") # Access Token Secret
  • Ensure you put your credentials in an env file to avoid pushing your secrets to the public 🙀.

Note: This package does not currently support OAuth authentication.

Code examples

See runtests.jl for example function calls.

using Twitter, Test
using JSON, OAuth

# set debugging
ENV["JULIA_DEBUG"]=Twitter

twitterauth(ENV["CONSUMER_KEY"], ENV["CONSUMER_SECRET"], ENV["ACCESS_TOKEN"], ENV["ACCESS_TOKEN_SECRET"])

#get_mentions_timeline
mentions_timeline_default = get_mentions_timeline()
tw = mentions_timeline_default[1]
tw_df = DataFrame(mentions_timeline_default)
@test 0 <= length(mentions_timeline_default) <= 20
@test typeof(mentions_timeline_default) == Vector{Tweets}
@test typeof(tw) == Tweets
@test size(tw_df)[2] == 30

#get_user_timeline
user_timeline_default = get_user_timeline(screen_name = "randyzwitch")
@test typeof(user_timeline_default) == Vector{Tweets}

#get_home_timeline
home_timeline_default = get_home_timeline()
@test typeof(home_timeline_default) == Vector{Tweets}

#get_single_tweet_id
get_tweet_by_id = get_single_tweet_id(id = "434685122671939584")
@test typeof(get_tweet_by_id) == Tweets

#get_search_tweets
duke_tweets = get_search_tweets(q = "#Duke", count = 200)
@test typeof(duke_tweets) <: Dict

#test sending/deleting direct messages
#commenting out because Twitter API changed. Come back to fix
# send_dm = post_direct_messages_send(text = "Testing from Julia, this might disappear later $(time())", screen_name = "randyzwitch")
# get_single_dm = get_direct_messages_show(id = send_dm.id)
# destroy = post_direct_messages_destroy(id = send_dm.id)
# @test typeof(send_dm) == Tweets
# @test typeof(get_single_dm) == Tweets
# @test typeof(destroy) == Tweets

#creating/destroying friendships
add_friend = post_friendships_create(screen_name = "kyrieirving")

unfollow = post_friendships_destroy(screen_name = "kyrieirving")
unfollow_df = DataFrame(unfollow)
@test typeof(add_friend) == Users
@test typeof(unfollow) == Users
@test size(unfollow_df)[2] == 40

# create a cursor for follower ids
follow_cursor_test = get_followers_ids(screen_name = "twitter", count = 10_000)
@test length(follow_cursor_test["ids"]) == 10_000

# create a cursor for friend ids - use barackobama because he follows a lot of accounts!
friend_cursor_test = get_friends_ids(screen_name = "BarackObama", count = 10_000)
@test length(friend_cursor_test["ids"]) == 10_000

# create a test for home timelines
home_t = get_home_timeline(count = 2)
@test length(home_t) > 1

# TEST of cursoring functionality on user timelines
user_t = get_user_timeline(screen_name = "stefanjwojcik", count = 400)
@test length(user_t) == 400
# get the minimum ID of the tweets returned (the earliest)
minid = minimum(x.id for x in user_t);

# now iterate until you hit that tweet: should return 399
# WARNING: current versions of julia cannot use keywords in macros? read here: https://github.com/JuliaLang/julia/pull/29261
# eventually replace since_id = minid
tweets_since = get_user_timeline(screen_name = "stefanjwojcik", count = 400, since_id = 1001808621053898752, include_rts=1)

@test length(tweets_since)>=399

# testing get_mentions_timeline
mentions = get_mentions_timeline(screen_name = "stefanjwojcik", count = 300) 
@test length(mentions) >= 50 #sometimes API doesn't return number requested (twitter API specifies count is the max returned, may be much lower)
@test Tweets<:typeof(mentions[1])

# testing retweets_of_me
my_rts = get_retweets_of_me(count = 300)
@test Tweets<:typeof(my_rts[1])

Want to contribute?

Contributions are welcome! Kindly refer to the contribution guidelines.

Linux: Build Status 

CodeCov: codecov

Author: Randyzwitch
Source Code: https://github.com/randyzwitch/Twitter.jl 
License: View license

#julia #api #twitter 

Hana Juali

Hana Juali

1602223649

Getting Started with Serverless Framework

What is Serverless? If you are really new to Serverless, here’s my previous article about  Serverless Architecture. Today we are going to create a simple CRUD application using Serverless framework with NodeJS and AWS.

Environment Setup

First of all, let’s install Serverless. To install run the command below.

**npm install -g serverless**

Now we have to configure our credentials. We can do it by executing the following command.

**serverless config credentials --provider aws --key <aws access key> --secret <aws secret key> --profile osusara**

So I’m going to use AWS as my provider. When we creating an IAM user in AWS, we can get the Access Key and Secret Key. We have to include them here as well. Then the--profile part is not a must. We can give a profile name to our configuration. If we didn’t put a profile, it will be the default profile.

For more details, here’s the Serverless  Getting Started guide and  AWS Config Credentials reference.

#dynamodb #aws #nodejs #serverless #serverless-framework

Hermann  Frami

Hermann Frami

1655426640

Serverless Plugin for Microservice Code Management and Deployment

Serverless M

Serverless M (or Serverless Modular) is a plugin for the serverless framework. This plugins helps you in managing multiple serverless projects with a single serverless.yml file. This plugin gives you a super charged CLI options that you can use to create new features, build them in a single file and deploy them all in parallel

splash.gif

Currently this plugin is tested for the below stack only

  • AWS
  • NodeJS λ
  • Rest API (You can use other events as well)

Prerequisites

Make sure you have the serverless CLI installed

# Install serverless globally
$ npm install serverless -g

Getting Started

To start the serverless modular project locally you can either start with es5 or es6 templates or add it as a plugin

ES6 Template install

# Step 1. Download the template
$ sls create --template-url https://github.com/aa2kb/serverless-modular/tree/master/template/modular-es6 --path myModularService

# Step 2. Change directory
$ cd myModularService

# Step 3. Create a package.json file
$ npm init

# Step 3. Install dependencies
$ npm i serverless-modular serverless-webpack webpack --save-dev

ES5 Template install

# Step 1. Download the template
$ sls create --template-url https://github.com/aa2kb/serverless-modular/tree/master/template/modular-es5 --path myModularService

# Step 2. Change directory
$ cd myModularService

# Step 3. Create a package.json file
$ npm init

# Step 3. Install dependencies
$ npm i serverless-modular --save-dev

If you dont want to use the templates above you can just add in your existing project

Adding it as plugin

plugins:
  - serverless-modular

Now you are all done to start building your serverless modular functions

API Reference

The serverless CLI can be accessed by

# Serverless Modular CLI
$ serverless modular

# shorthand
$ sls m

Serverless Modular CLI is based on 4 main commands

  • sls m init
  • sls m feature
  • sls m function
  • sls m build
  • sls m deploy

init command

sls m init

The serverless init command helps in creating a basic .gitignore that is useful for serverless modular.

The basic .gitignore for serverless modular looks like this

#node_modules
node_modules

#sm main functions
sm.functions.yml

#serverless file generated by build
src/**/serverless.yml

#main serverless directories generated for sls deploy
.serverless

#feature serverless directories generated sls deploy
src/**/.serverless

#serverless logs file generated for main sls deploy
.sm.log

#serverless logs file generated for feature sls deploy
src/**/.sm.log

#Webpack config copied in each feature
src/**/webpack.config.js

feature command

The feature command helps in building new features for your project

options (feature Command)

This command comes with three options

--name: Specify the name you want for your feature

--remove: set value to true if you want to remove the feature

--basePath: Specify the basepath you want for your feature, this base path should be unique for all features. helps in running offline with offline plugin and for API Gateway

optionsshortcutrequiredvaluesdefault value
--name-nstringN/A
--remove-rtrue, falsefalse
--basePath-pstringsame as name

Examples (feature Command)

Creating a basic feature

# Creating a jedi feature
$ sls m feature -n jedi

Creating a feature with different base path

# A feature with different base path
$ sls m feature -n jedi -p tatooine

Deleting a feature

# Anakin is going to delete the jedi feature
$ sls m feature -n jedi -r true

function command

The function command helps in adding new function to a feature

options (function Command)

This command comes with four options

--name: Specify the name you want for your function

--feature: Specify the name of the existing feature

--path: Specify the path for HTTP endpoint helps in running offline with offline plugin and for API Gateway

--method: Specify the path for HTTP method helps in running offline with offline plugin and for API Gateway

optionsshortcutrequiredvaluesdefault value
--name-nstringN/A
--feature-fstringN/A
--path-pstringsame as name
--method-mstring'GET'

Examples (function Command)

Creating a basic function

# Creating a cloak function for jedi feature
$ sls m function -n cloak -f jedi

Creating a basic function with different path and method

# Creating a cloak function for jedi feature with custom path and HTTP method
$ sls m function -n cloak -f jedi -p powers -m POST

build command

The build command helps in building the project for local or global scope

options (build Command)

This command comes with four options

--scope: Specify the scope of the build, use this with "--feature" tag

--feature: Specify the name of the existing feature you want to build

optionsshortcutrequiredvaluesdefault value
--scope-sstringlocal
--feature-fstringN/A

Saving build Config in serverless.yml

You can also save config in serverless.yml file

custom:
  smConfig:
    build:
      scope: local

Examples (build Command)

all feature build (local scope)

# Building all local features
$ sls m build

Single feature build (local scope)

# Building a single feature
$ sls m build -f jedi -s local

All features build global scope

# Building all features with global scope
$ sls m build -s global

deploy command

The deploy command helps in deploying serverless projects to AWS (it uses sls deploy command)

options (deploy Command)

This command comes with four options

--sm-parallel: Specify if you want to deploy parallel (will only run in parallel when doing multiple deployments)

--sm-scope: Specify if you want to deploy local features or global

--sm-features: Specify the local features you want to deploy (comma separated if multiple)

optionsshortcutrequiredvaluesdefault value
--sm-paralleltrue, falsetrue
--sm-scopelocal, globallocal
--sm-featuresstringN/A
--sm-ignore-buildstringfalse

Saving deploy Config in serverless.yml

You can also save config in serverless.yml file

custom:
  smConfig:
    deploy:
      scope: local
      parallel: true
      ignoreBuild: true

Examples (deploy Command)

Deploy all features locally

# deploy all local features
$ sls m deploy

Deploy all features globally

# deploy all global features
$ sls m deploy --sm-scope global

Deploy single feature

# deploy all global features
$ sls m deploy --sm-features jedi

Deploy Multiple features

# deploy all global features
$ sls m deploy --sm-features jedi,sith,dark_side

Deploy Multiple features in sequence

# deploy all global features
$ sls m deploy  --sm-features jedi,sith,dark_side --sm-parallel false

Author: aa2kb
Source Code: https://github.com/aa2kb/serverless-modular 
License: MIT license

#serverless #aws #node #lambda 

Serverless Applications - Pros and Cons to Help Businesses Decide - Prismetric

In the past few years, especially after Amazon Web Services (AWS) introduced its Lambda platform, serverless architecture became the business realm’s buzzword. The increasing popularity of serverless applications saw market leaders like Netflix, Airbnb, Nike, etc., adopting the serverless architecture to handle their backend functions better. Moreover, serverless architecture’s market size is expected to reach a whopping $9.17 billion by the year 2023.

Global_Serverless_Architecture_Market_2019-2023

Why use serverless computing?
As a business it is best to approach a professional mobile app development company to build apps that are deployed on various servers; nevertheless, businesses should understand that the benefits of the serverless applications lie in the possibility it promises ideal business implementations and not in the hype created by cloud vendors. With the serverless architecture, the developers can easily code arbitrary codes on-demand without worrying about the underlying hardware.

But as is the case with all game-changing trends, many businesses opt for serverless applications just for the sake of being up-to-date with their peers without thinking about the actual need of their business.

The serverless applications work well with stateless use cases, the cases which execute cleanly and give the next operation in a sequence. On the other hand, the serverless architecture is not fit for predictable applications where there is a lot of reading and writing in the backend system.

Another benefit of working with the serverless software architecture is that the third-party service provider will charge based on the total number of requests. As the number of requests increases, the charge is bound to increase, but then it will cost significantly less than a dedicated IT infrastructure.

Defining serverless software architecture
In serverless software architecture, the application logic is implemented in an environment where operating systems, servers, or virtual machines are not visible. Although where the application logic is executed is running on any operating system which uses physical servers. But the difference here is that managing the infrastructure is the soul of the service provider and the mobile app developer focuses only on writing the codes.

There are two different approaches when it comes to serverless applications. They are

Backend as a service (BaaS)
Function as a service (FaaS)

  1. Backend as a service (BaaS)
    The basic required functionality of the growing number of third party services is to provide server-side logic and maintain their internal state. This requirement has led to applications that do not have server-side logic or any application-specific logic. Thus they depend on third-party services for everything.

Moreover, other examples of third-party services are Autho, AWS Cognito (authentication as a service), Amazon Kinesis, Keen IO (analytics as a service), and many more.

  1. Function as a Service (FaaS)
    FaaS is the modern alternative to traditional architecture when the application still requires server-side logic. With Function as a Service, the developer can focus on implementing stateless functions triggered by events and can communicate efficiently with the external world.

FaaS serverless architecture is majorly used with microservices architecture as it renders everything to the organization. AWS Lambda, Google Cloud functions, etc., are some of the examples of FaaS implementation.

Pros of Serverless applications
There are specific ways in which serverless applications can redefine the way business is done in the modern age and has some distinct advantages over the traditional could platforms. Here are a few –

🔹 Highly Scalable
The flexible nature of the serverless architecture makes it ideal for scaling the applications. The serverless application’s benefit is that it allows the vendor to run each of the functions in separate containers, allowing optimizing them automatically and effectively. Moreover, unlike in the traditional cloud, one doesn’t need to purchase a certain number of resources in serverless applications and can be as flexible as possible.

🔹 Cost-Effective
As the organizations don’t need to spend hundreds and thousands of dollars on hardware, they don’t need to pay anything to the engineers to maintain the hardware. The serverless application’s pricing model is execution based as the organization is charged according to the executions they have made.

The company that uses the serverless applications is allotted a specific amount of time, and the pricing of the execution depends on the memory required. Different types of costs like presence detection, access authorization, image processing, etc., associated with a physical or virtual server is completely eliminated with the serverless applications.

🔹 Focuses on user experience
As the companies don’t always think about maintaining the servers, it allows them to focus on more productive things like developing and improving customer service features. A recent survey says that about 56% of the users are either using or planning to use the serverless applications in the coming six months.

Moreover, as the companies would save money with serverless apps as they don’t have to maintain any hardware system, it can be then utilized to enhance the level of customer service and features of the apps.

🔹 Ease of migration
It is easy to get started with serverless applications by porting individual features and operate them as on-demand events. For example, in a CMS, a video plugin requires transcoding video for different formats and bitrates. If the organization wished to do this with a WordPress server, it might not be a good fit as it would require resources dedicated to serving pages rather than encoding the video.

Moreover, the benefits of serverless applications can be used optimally to handle metadata encoding and creation. Similarly, serverless apps can be used in other plugins that are often prone to critical vulnerabilities.

Cons of serverless applications
Despite having some clear benefits, serverless applications are not specific for every single use case. We have listed the top things that an organization should keep in mind while opting for serverless applications.

🔹 Complete dependence on third-party vendor
In the realm of serverless applications, the third-party vendor is the king, and the organizations have no options but to play according to their rules. For example, if an application is set in Lambda, it is not easy to port it into Azure. The same is the case for coding languages. In present times, only Python developers and Node.js developers have the luxury to choose between existing serverless options.

Therefore, if you are planning to consider serverless applications for your next project, make sure that your vendor has everything needed to complete the project.

🔹 Challenges in debugging with traditional tools
It isn’t easy to perform debugging, especially for large enterprise applications that include various individual functions. Serverless applications use traditional tools and thus provide no option to attach a debugger in the public cloud. The organization can either do the debugging process locally or use logging for the same purpose. In addition to this, the DevOps tools in the serverless application do not support the idea of quickly deploying small bits of codes into running applications.

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