Top 7 Database You Must Know For Software Development Projects

A database is just like a room in an office where all the files and important information can be stored related to a project. Every company needs a database to store and organize the information. The information that we store can be very sensitive so we always have to be careful while accessing or manipulating the information in the database. Choosing the right database is completely dependent on the purpose of the project and over the years programmers and industry specialists have shown their love for databases that fulfilled their requirements.

Now if you wonder which databases are most popular in the world then according to the recent ranking shown by the DB Engines below is the list…

Image Source: DB-Engine

1. Oracle

Oracle is the most popular RDBMS written in assembly language C, C++, and Java. The current version of the Oracle Database is 19c. However, a lot of organizations are currently using 11g and 12c. It’s a very powerful secure database that has a well-written document. It takes less space and quickly processes data also you can find some new good features like JSON from SQL as well. Some of the other features are given below…

• Oracle provides functionality for Cloud, Document Store, Key-value storage, Graph DBMS, PDF Storages, and BLOG.
• It fulfills the requirements in the areas of performance, scalability, high availability, security, data warehousing, etc.
• It supports multiple Windows, UNIX, and Linux versions.

2. MySQL

MySQL is a very popular open-source RDBMS which is used by most of the major tech companies
such as Facebook, Google, Twitter and Adobe. It was acquired by Oracle as a part of Sun Microsystems’ acquisition in 2009. It uses structured query language and it is written in C and C++. The latest version of MySQL is 8.0 which has a better recovery option. MySQL has different features for different editions (Enterprise Edition, Standard Edition, and Classic Edition). Some of the good features of MYSQL are given below…

• It is widely used in web development because it gives high performance, it is secure, flexible and reliable.
• It supports C, C++, Java, Perl, PHP, Python, and Tcl for client programming.
• It support Unicode, Replication, Transactions, full-text search, triggers, and stored procedures.
• MySQL supports large databases, up to 50 million rows or more in a table.
• MySQL can run on Linux, Windows, OSX and FreeBSD and Solaris.

3. Microsoft SQL Server

This database was launched in 1989 and it is also one of the most popular relational database management systems (RDBMS) in the world. It is written in C and C++ and supports structured query language. The latest version of SQL Server is SQL Server 2019. It works well with Microsoft products and it is available on both Windows and Linux platforms. There are so many editions of this database such as Azure SQL Database (cloud-based version), Compact edition, enterprise edition (preferred by most of the companies) and Developer edition. Some of the main features are given below…

• It is platform depdendent, high performance database.
• It uses data compression technique so you need to worry less about storage or space.
• It can be integrated with non-relational sources like Hadoop.
• For security-related concern it uses row-level security, dynamic data masking, transparent data encryption, and robust auditing.
• It comes with custom-built graphical integration that saves a lot of time of users.
• Object Explorer feature allows users to view the creation of the tables.
• Creates various designs, tables, and view data without syntax.
• Efficient management of workload and allows multiple users to use the same database.

4. PostgreSQL

This database is also an open-source Object-Relational DBMS but users are free to create NoSQL databases. It is written in C and the popularity of this database is increasing day by day. It is ideal for companies that frequently deal with large volumes of data. A lot of gaming apps, database automation tools, and domain registries use this database. Companies such as Apple (macOS Server operating system uses this database), Cisco, Fujitsu, Skype, and IMDb, etc use this database. PostgreSQL runs on many operating systems, including Windows, Linux, Solaris and now Mac OS X. The database is good for single-machine applications, a large internet-facing application, and for all applications in between. It is also good for building fault-tolerant environments, managing the data and protecting data integrity. Let’s discuss some more features…

• High scalability, predefined functions, support for JSON, easy data portability, multiple interfaces.
• It provides support for tablespaces, as well as for stored procedures, joins, views, triggers, etc.
• Security and disaster recovery features.
• Extensibility through stored functions and procedures, procedural languages, and foreign data wrappers.
• Allows you to create custom data types and query methods.
• Robust, secure and fast.

5. MongoDB

MongoDB is a cross-platform NoSQL database. It is written in C++, C and JavaScript programming languages. You can use this database for mobile apps, real-time analytics, IoT, and can provide a real-time view for all your data. MongoDB is a high-speed database and the data is stored in the form of JSON style documents. MongoDB uses internal memory so the data is easily accessible. You can process a large amount of data simultaneously.

• Fast, easy to use, auto-sharding, deployment flexibility, high performance, high availability and easy scalability.
• Supports JSON and the schema can be written without downtime.
• Easy to administer in the case of failures.
• For data migrations, it provides complete deployment flexibility.
• Queries can be easily optimized for output.

6. IBM DB2

The latest release of DB2 is 11.5 which runs queries faster. This database supports the relational model but in recent years products have been extended to support object-relational features and non-relational structures like JSON and XML. The database offers AI-dedicated capabilities that are designed to manage and structure complex data. Some of its good features are given below.

• It supports private as well as cloud environments.
• It supports most of the data science languages to handle simple or complex frameworks.
• It supports multiple Windows, UNIX, and Linux versions.
• Easy to install and easily accessible.
• DB2 has different server editions which are designed for specific tasks.

7. Elasticsearch

ElasticSearch is a search engine based on the Lucene library. It is a distributed and open-source full-text search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. A lot of big organizations like Wikipedia, The Guardian, StackOverflow, GitHub, etc. Some of the key features are given below…

• It allows you to create replicas of their indexes and shards.
• It is scalable up to petabytes of structured and unstructured data.
• Multi tenancy and extremely fast search.
• Java-based and designed to operate in real-time.
• Document oriented with a higher performance result.

 

Top 10 Python Applications in Real World

We are living in a digital world that is completely driven by chunks of code. Every industry depends on software for its proper functioning be it healthcare, military, banking, research, and the list goes on. We have a huge list of programming languages that facilitate the software development process. One of these is Python which has emerged as the most lucrative and exciting programming language. As per a survey it is observed that python is the main coding language for more than 80% of developers. The main reason behind this is its extensive libraries and frameworks that fuel up the process. 

Python has been at the forefront of Machine learning, data science, and artificial intelligence innovation. Further, it provides ease in building a plethora of applications, web development processes, and a lot more. In this blog, we will discuss the top 10 Python applications in the real world in a detailed manner. So let’s get started:

1. Web Development

It is one of the most astonishing applications of Python. This is because Python comes up with a wide range of frameworks like Django, Flask, Bottle, and a lot more that provide ease to developers. Furthermore, Python has inbuilt libraries and tools which make the web development process completely effortless. Use of Python for web development also offers:

• Amazing visualization
• Convenience in development
• Enhanced security
• Fast development process

2. Machine Learning and Artificial Intelligence

Machine Learning and Artificial Intelligence are the hottest subjects right now. Python along with its inbuilt libraries and tools facilitate the development of AI and ML algorithms. Further, it offers simple, concise, and readable code which makes it easier for developers to write complex algorithms and provide a versatile flow. Some of the inbuilt libraries and tools that enhance AI and ML processes are:

• Numpy for complex data analysis
• Keras for Machine learning
• SciPy for technical computing
• Seaborn for data visualization

3. Data Science

Data science involves data collection, data sorting, data analysis, and data visualization. Python provides amazing functionality to tackle statistics and complex mathematical calculations. The presence of in-built libraries provides convenience to data science professionals. Some of the popular libraries that provide ease in the data science process are TensorFlow, Pandas, and Socket learning. These libraries provide an ecosystem for fine-tuning data models, data preprocessing, and performing complex data analysis.

4. Game Development

With the rapidly growing gaming industry Python has proved to be an exceptional option for game development. Popular games like Pirates of the Caribbean, Bridge commander, and Battlefield 2 use Python programming for a wide range of functionalities and addons. The presence of popular 2D and 3D gaming libraries like pygame, panda3D, and Cocos2D make the game development process completely effortless.

5. Audio and Visual Applications

Audio and video applications are undoubtedly the most amazing feature of Python. Python is equipped with a lot of tools and libraries to accomplish your task flawlessly. Applications that are coded in Python include popular ones like Netflix, Spotify, and YouTube. This can be handled by libraries like  

• Dejavu
• Pyo
• Mingus
• SciPy
• OpenCV

6. Software Development

Python is just the perfect option for software development. Popular applications like Google, Netflix, and Reddit all use Python. This language offers amazing features like:

• Platform independence  
• Inbuilt libraries and frameworks to provide ease of development.
• Enhanced code reusability and readability
• High compatibility

Apart from these Python offers enhanced features to work with rapidly growing technologies like Machine learning and Artificial intelligence. All these embedded features make it a popular choice for software development.

7. CAD Applications

CAD refers to computer-aided design; it is the process of creating 3D and 2D models digitally. This application has replaced manual drift and is used by architects, product designers, and construction managers to design products with extremely high consistency. Python is embedded with amazing applications like Blender, FreeCAD, open cascade, and a lot more to efficiently design products. These provide enhanced features like technical drawing, dynamic system development, recordings, file export, and import.

8. Business Applications

Python offers excellent security and scalability features that make it perfect for delivering high-performance business applications. It has inbuilt libraries and tools like:

• Odoo is business management software that provides you with an automated solution for your business process.
• Tryton is easy-to-use open-source business software. It has fully integrated features like financial accounting, sales, CRM, purchasing, shipping, and the list goes on.

All these distinguishing features make it fit for creating business applications.

9. Desktop GUI

Python is an interactive programming language that helps developers to create GUIs easily and efficiently. It has a huge list of inbuilt tools like PyQT, kivy, wxWidgets, and many other libraries like them to build a fully functional GUI in an extremely secure and efficient manner.  

10. Web Scraping Application

Web scraping is an automated process used to extract information from websites in an easier and faster way. The information is used by researchers, organizations, and analysts for a wide variety of tasks. Python has a wide range of features that make it suitable for web scraping some of them are:

• A concise syntax that enhances the readability and saves your time.
• A wide range of libraries and tools like pandas, matplotlib, and Selenium makes the web scraping process easy and efficient.
• Easy to use and understand

Some other real-world applications of Python are:

• Robotics and automation by the use of inbuilt libraries and tools like PyDy, Dart, PyRobot, and pyro.  
• Image processing: some of the amazing libraries and tools for image processing are Blender, OpenCV, Houdini, and PIL.
• Scientific applications are facilitated by popular libraries like Pandas, Matplotlib, SciPy, and many more

Conclusion: Python is a concise and extremely powerful language that is rapidly gaining popularity. It has been the epicenter of most amazing technologies like AI, automation, and machine learning. Further, it is used to facilitate hot subjects like data analysis and data visualization. In this blog, we have tried to give you a basic idea about the top 10 python applications in the real world. We hope that you found this helpful!

11 pip Commands For Python Developers

 Python has been preferred over all programming languages for technological advancement. It is one of the most lucrative programming languages that is used as the main programming language by more than 80%of developers. It is used for web development, mobile applications, hardware programming, and many more. 

Pip commands cannot be ignored when we talk about Python. When you get your task done within a second, it feels great, Right! Here comes the role of pip commands in Python which allow users to perform certain operations in a second. Pip is a package management system that is used to manage software packages, it also comes pre-installed with Python. There are certain commands which you should be handy and keep in practice whether you build a small or big application. Let’s discuss those.

Here, we present 11 Pip commands for Python Developers:

1. Install and Uninstall Package

To install and uninstall a certain package as per your requirement, this command plays a major role. You can directly install or uninstall using this command in your command prompt by going into the Python folder. 

pip install package_name

pip uninstall package_name

Here, package_name can be any package, whether it is Pandas, NumPy, etc. 

For Example, pip install pandas: installs pandas and 

                      pip  uninstall pandas: uninstalls pandas.

2. Update Pip

You can also update the pip if it’s not in its updated version. You just need to type pip –version to check whether it has an updated version or not, if not, just update it using the below-given command. Do check this command should be written under the Python folder. 

pip install –upgrade pip

For Example, pip install –upgrade pip updates pip with its newest version. 

3. Upgrade Package

Once, you see the installed packages are outdated, upgrade them using the command pip install package_name –upgrade. Also, keep in mind that this might even have compatibility issues with other packages. 

pip install package_name –upgrade

For Example, pip install pandas –upgrade installs pandas with its updated version containing all functionalities, and libraries. 

4. Update All Packages

To update all packages at once, you need to initially generate a requirements.txt file using the command pip freeze > requirements.txt, this file contains all the packages, and to update all packages you just need to update the requirements.txt file. 

In the list of packages in the requirements.txt file, packages with updated versions will be ignored rest will be updated.

pip freeze > requirements.txt

pip install -r requirements.txt –upgrade

For Example, pip freeze requirements.txt creates a temporary file called requirements.txt which could contain all the packages needed to be installed, then the command pip install -r requirements.txt –upgrade updates all the packages contained in requirements.txt at a time. 

5. Info About an Installed Package

To know about the package’s detail like its version, and features, one must type the command pip show package_name, where package_name could be anything like Pandas, matplotlib, etc. 

Note: In case you don’t have a package installed in your system and you try to update it, it shows an error as “Package Not Found”.

pip show package_name

For Example, pip show pandas display all the details about pandas like their version, features, etc. 

6. Install Editable Mode

Once you install a package and you want to develop it locally, you can install it in its editable mode. It links the package to the specified location which allows you to make changes that will be reflected directly.

This command pip install -e gives you the package in develop mode/ editable mode.

pip install -e

For Example, pip install -e this command opens the package(whichever is installed previously) in an editable form to make changes locally if required. 

7. Generate Requirements.txt File

To merge all your packages in a single file, we generate a requirements.txt file. It helps all the developers to use the package at a time. Using the command, pip freeze > requirements.txt, a requirements.txt file is generated which stores all the required packages.

pip freeze > requirements.txt

For Example, pip freeze>requirements.txt creates a temporary file called requirements.txt to store all the packages collectively in a single place. 

8. List All Installed Packages

This command helps you to display all the packages installed in your system. It shows a list of packages along with their version and path.

Note: Only the packages installed in your system will be displayed here, not those which are installed remotely. 

pip list

For Example, pip list, this command lists all the packages listed in your system which could be anything installed like Pandas, Matplotlib, NumPy.

9. Remove All Packages Installed By Pip

Instead of removing packages one by one, you can directly remove all packages installed by pip to save time using the command pip freeze > requirements.txt && pip uninstall -e requirements.txt -y. Here, it creates a requirements.txt file that contains a list of packages and then uninstalls all of them. 

pip freeze > requirements.txt && pip uninstall -e requirements.txt -y

For Example, pip freeze > requirements.txt && pip uninstall -e requirements.txt -y this command checks the requirements.txt file to check if there’s any package installed, if installed removes it by uninstalling it. 

10. Verify That Installed Packages Have Compatible Dependencies

This command checks whether all installed packages in the requirements.txt file have compatible dependencies. It checks all three conditions i.e., whether dependencies are compatible, whether the package has an updated version or not, and whether a package is missing. 

pip check

For Example, the pip checks this command checks whether all the installed packages contain compatible dependencies according to the package.

11. Install All Dependencies From the requirements.txt File

All the dependencies which are required for the project implementation can be installed using this command, pip install -r requirements.txt. Dependencies contain all the information about the package. 

pip install -r requirements.txt

For Example, pip install -r requirements.txt is needed when you have to install all the dependencies from requirements.txt (which contains all the installed packages).

 

10 Python In-Built Functions You Should Know

Python is one of the most lucrative programming languages. According to research, there were approximately 10 million Python developers in 2020 worldwide and the count is increasing day by day. It provides ease in building a plethora of applications, web development processes, and a lot more. When it comes to making a program short and clear, we use in-built functions which are a set of statements collectively performing a task. Using in-built functions in a program makes it beneficial in many ways such as:

• Makes it less complex.
• Enhances readability.
• Reduces coding time and debugging time.
• Allows re-usage of code.

Hence, it plays a major role in the development of an application. In python 3, we have 68 in-built functions, some of them are listed below:

1) append()

This method adds an item at the end of the existing list, tuple, or any other set. Then, the length of the list gets increased by one. We can append an item to the list and also list to a list. It adds any data type which is to be added at the end of the list. It has time complexity: O(1).

Syntax: 

append(item) 

where item refers to the item needed to be appended with the existing element.

For Example:

a=[“apple”,” banana”,” mango”,” grapes”]

a.append(“orange”)

print(a)

Output:

[“apple”,” banana”,” mango”,” grapes”,” orange”]

2) reduce()

The reduce() function applies a function of two arguments collectively on a list of objects in succession from left to right to reduce it to one value. It is defined in a functools library. This works better than for loop. 

Syntax: reduce(function, iterable) 

where, function refers to the function which will be used in a program, and iterable refers to the value that will be iterated in the program. 

For Example:  

From functools import reduce

Def sum(a, b):

res=return (sum, [1,2,4,5])

print res

Output: 

12

3) slice()

This function returns the sliced object from a given set of elements. It allows you to access any set of sequences whether it is ta tuple, list, or set. Time complexity of slice() is O(n).

Syntax: 

slice(start, stop, step) 

where start refers to the start index from where you have to copy, stop refers to the index till where you want to slice, and step refers to the count by which you want to skip.

For Example:

a=”Hello World”

y=slice(2,4,1)

print(y)

Output:

 lo

4) sorted()

This function sorts the given element in specified (ascending or descending) order. The set of elements could be a list, tuple, and dictionary. The time complexity of the sorted function is O(n.logn).

Syntax: 

sorted(set of elements) 

where a set of elements refers to the elements which need to be sorted.

For Example:

a=[1,7,3,8]

y=sorted(a)

print(y)

Output: 

[1,3,7,8]

5) split()

This method breaks up the string into a list of substrings, based on the specified separator. It returns strings as a list. By default, whitespace is the separator. Time complexity of split() is O(n).

Syntax: 

split(separator)

where separator refers to the value which is to be split from the given sequence.

For Example: 

a=”HelloWorld”

y=a.split(‘l’)

print(y)

Output:

 ['He','oWor','d']

6) eval()

The eval() function evaluates the given expression whether it is a mathematical or logical expression. If a string is passed through it, it parses the function, compiles it to bytecode, and then returns the output. Since operators have no time complexity therefore eval doesn’t have one. 

Syntax:

 eval(expression)

where the expression could be any operation such as mathematical or logical.

For Example: 

x=6

y=eval(‘x*8’)

print(y)

Output:

 48

7) bin()

This function converts an integer to a binary string that has the prefix 0b. Also, the integer passed could be negative or positive. Its time complexity for a number n is O(log(n))

Syntax:

 bin(integer)

where the integer is any value passed to receive its binary form. 

For Example:

print(bin(8))

Output:  

0b1000

8) map()

This function returns a map object(which is an iterator) of the results after applying the given function to each item of a given iterable (list, tuple, etc.). It applies a function to all objects in a list. The time of complexity of the map() function is O(n).

Syntax: 

map(function, iterable)

where function refers to the function which will be used in a program, iterable refers to the value that will be iterated in the program. 

For Example:

def add(x):

   return x+2

x = map(add, (3, 5, 7, 11, 13))

print (x)

Output:

(2,7,9,13,15)

9) filter()

This function creates a new iterator from an existing one (such as a list, tuple, or dictionary) that filters elements. It checks whether the given condition is available in the sequence or not and then prints the output. The time complexity of the filter function is O(n).

Syntax: 

filter(function, iterable)

where function refers to the function which will be used in a program, iterable refers to the value that will be iterated in the program. 

For Example:

c = [‘Ant’,’Lizard’,’Mosquito’,’Snake’]      

def vowels(x):

return x[0].lower() in ‘aeiou’

items = filter(vowels, c)

print(list(items))

Output:

 ['Ant']

10) exec()

This function executes the given condition and prints the output in python expression. It executes the program dynamically Python exec() function executes the dynamically created program, which is either a string or a code object. If it is a string, then it is parsed as a Python statement and then executed; else, a syntax error occurs.

Syntax: 

exec(object[, globals[, locals]])

where the object can be a string or object code, globals can be a dictionary and the parameter is optional, and locals can be a mapping object and are also optional.

For Example:

exec(print(sum(2,8)))

Output:

10

So till now you must have got the information about 10 Python in-built functions. With these in-built functions, you can make complex applications very easy. Use it whenever you’re working on any Python application to be handy.