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Choosing between MySQL vs PostgreSQL vs SQL Server

by Vitaliy Ilyukha

The choice between SQL and non-SQL databases usually boils down to differences in the structure. However, when we are looking into several SQL solutions, the criteria are a lot more distorted. Now will consider the aspects more precisely and analyze the underlying functionality. We’ll be taking a look at the three most popular relational databases: MySQL vs Postgresql vs SQL server.

Name of video

To help you, we have collected advice from our database developers, re-went through manuals, and even looked up official in-depth guides. We do tend to have our personal preferences, but in this guide, we will put them aside in favor of objective comparison.

stackoverflow questions


MySQL happens to be one of the most popular databases, according to DB Engines Ranking. It’s a definite leader among SQL solutions, used by Google, LinkedIn, Amazon, Netflix, Twitter, and others. MySQL popularity has been growing a lot because teams increasingly prefer open-source solutions instead of commercial ones.

Price: the database solution is developed by Oracle and has additional paid tools; the core functionality can be accessed for free.

Language: MySQL is written in C++; database management is done with Structured Query Language.



Read our comparison of MongoDB vs MySQL to make the right choice of a database solution.


A tried-and-proven relational database that is known for supporting a lot of data types, intuitive storage of schemaless data, and rich functionality. Some developers go even as far as to claim that it’s the most advanced open-source database on the market. We wouldn’t go that far, but it’s definitely a highly universal solution.

Price: open-source

Language: C

SQL Server

Unlike Postgresql vs MySQL, SQL Server is a commercial solution. It’s preferred by companies who are dealing with large traffic workloads on a regular basis. It’s also considered to be one of the most compatible systems with Windows services.

The SQL Server infrastructure includes a lot of additional tools, like reporting services, integration systems, and analytics. For companies that manage multiple teams, these tools make a big difference in day-to-day work.

Price: the database has a free edition for developers and small businesses but only supports 1 processor, 1GB of maximum memory used by the database engine and 10GB maximum database size.

. For a server, users need to pay $931.

Side-by-side Comparison of SQL Tools

In this comparison, we’ll take a look at the functionality of the three most popular SQL databases, examine their use cases, respective advantages, and disadvantages. Firstly, we’ll start by exploring the in-depth functionality.

Data Changes

Here we evaluate the ease that the data can be modified with and the database defragmented. The key priority is the systems’ flexibility, security, and usability.

Row updates

This criterion refers to the algorithms that a database uses to update its contents, speed, and efficiency.

In the MySQL case, a solution updates data automatically to the rollback storage. If something goes wrong, developers can always go back to the previous version.

PostgreSQL: developers insert a new column and row in order to update the database. All updated rows have unique IDs. This multiplies the number of columns and rows and increases the size of the database, but in turn, developers benefit from higher readability.

SQL Server: the database has three engines that are responsible for row updates. The ROW Store handles the information on all previous row updates, IDs, and modified content. The in-memory engine allows analyzing the quality of an updated database with a garbage collector. The column-store database lets store updates in columns, like in column-driven databases.

sql server

Among these three, SQL Server offers perhaps the most flexibility and efficiency, because it allows monitoring updated rows and columns, collecting errors, and automating the process. The difference between SQL Server and MySQL and Postgresql lies mainly in customizing the positions – SQL Server offers a lot more than others.


When developers update different parts of an SQL database, the changes occur at different points of the systems and can be hard to read, track, and manage. Therefore, maintenance should include defragmentation – the process of unifying the updated database by assigning indexes, revisiting the structure, and creating new pages. The database frees up the disk space that is not used properly so that a database can run faster.

MySQL offers several approaches to defragmentation – during backup, index creation, and with an OPTIMIZE Table command. Without going into much detail, we’ll just say that having that many options for table maintenance is convenient for developers, and it surely saves a lot of time.

PostgreSQL allows scanning the entire tables of a data layer to find empty rows and delete the unnecessary elements. By doing so, the system frees up the disk space. However, the method requires a lot of CPU and can affect the application’s performance.


SQL Server offers an efficient garbage collector that doesn’t create more than 15-20% of overhead. Technically, developers can even run garbage collector on a continuous basis, because it’s that efficient.

Overall, MySQL and SQL Server offer more of defragmentation methods that Postgresql does. They consume less CPU and provide more flexible settings.

Data Queries

Here, we take a look at how the systems cache and process user requests, what approaches they take in storing data, and how developers can manage it.

Buffer Pool

Some systems call a buffer to pull cache, but regardless of terminology, our goal is to summarize the algorithms that systems use to process user queries and maintain connections.

MySQL offers a scalable buffer pool – developers can set up the size of the cache according to the workload. If the goal is to save CPU and storage space, developers can put strict benchmarks on their buffer pool. Moreover, MySQL allows dividing cache by segments to store different data types and maximize isolation.

PostgreSQL isolates processes even further than MySQL by treating them as a separate OS process. Each database has a separate memory and runs its own process. On the one hand, management and monitoring become a lot easier, but on the other, scaling multiple databases takes a lot of time and computing resources.

SQL Server also uses a buffer pool, and just like in MySQL, it can be limited or increased according to processing needs. All the work is done in a single pool, with no multiple pages, like in Postgresql.

If your priority is to save computing resources and storage, choose flexible solutions: the choice will be between MySQL vs SQL Server. However, if you prefer clear organization and long-term order, Postgre, with its isolated approach, might be a better fit.

Temporary Tables

Temporary tables allow storing intermediate results from complex procedures and branched business logic. If you need some information only to power the next process, it doesn’t make sense to store it in a regular table. Temporary tables improve database performance and organization by separating intermediary data from the essential information.


MySQL offers limited functionality for temporary tables. Developers cannot set variables or create global templates. The software even limits the number of times that a temporary table can be referred to – not more than once.

Postgresql offers a lot more functionality when it comes to temporary content. You divide temporary tables into local and global and configure them with flexible variables.

SQL Server also offers rich functionality for temporary table management. You can create local and global temporary tables, as well as oversee and create variables.

Temporary tables are essential for applications with complicated business logic. If your software runs a lot of complex processes, you will need to store multiple intermediary results. Having rich customization functionality will often be necessary throughout the development process.


The way a database handles indexes is essential because they are used to locate data without searching for a particular row. Indexes can refer to multiple rows and columns. You can assign the same index to files, located in the different places in the database, and collect all these pieces with a single search.

tables indexes

In this comparison, we evaluated the way indexes are created in every solution, the support of multiple-index searches, and multi-column indexes, as well as partial ones.

MySQL organized indexes in tables and clusters. Developers can automatically locate and update indexes in their databases. The search isn’t highly flexible – you can’t search for multiple indexes in a single query. MySQL supports multi-column indexes, allowing adding up to 16 columns.

Postgresql also supports index-based table organization, but the early versions don’t include automated index updates (which appear only after the 11th edition release). The solution also allows looking up many indexes in a single search, which means that you can find a lot of information. The multi-column settings are also more flexible than in MySQL – developers can include up to 32 columns.

SQL Server offers rich automated functionality for index management. They can organize in clusters and maintain the correct row order without manual involvement. The solution also supports multiple-index searches and partial indexes.

Having flexible index settings allows looking up information faster and organizing multiple data simultaneously.

Memory-Optimized Tables

Memory-optimized tables are mainly known as a SQL Server concept, but they also exist in other database management solutions. Such a table is stored in active memory and on the disk space in a simplified way. To increase the transaction speed, the application can simply access data directly on the disk, without blocking concurrent transactions. For processes that happen on a regular basis and usually require a lot of time, a memory-optimized table can be a solution to improve database performance.

Memory-optimized tables

MySQL supports the memory-stored table, but it can’t participate in transactions, and its security is highly vulnerable. Such tables are used only for reading purposes and can simplify exclusively primitive operations. For now, MySQL doesn’t come close to making the most out of memory-optimized tables.

PostgreSQL doesn’t support in-memory database creation.

SQL Server uses an optimistic strategy to handle memory-optimized tables, which means they can participate in transactions along with ordinary tables. Memory-based transactions are faster than regular ones, and this allows a drastic increase in application speed.

As expected, memory-optimized tables are best set up in MySQL – it’s basically their native approach. It’s not an essential database feature, but still, a good way to improve performance.

JSON Support

The use of JSON files allows developers to store non-numeric data and achieve faster performance. JSON documents don’t have to be parsed, which contributes to much higher processing speed. They are easily readable and accessible, which is why JSON support simplifies maintenance. JSON files are mostly used in non-relational databases, but lately, SQL solutions have supported this format as well.

MySQL supports JSON files but doesn’t allow indexing them. Overall, the functionality for JSON files in MySQL is very limited, and developers mostly prefer using classical strings. Similarly to non-relational databases, MySQL also allows working with geospatial data, although handling it isn’t quite as intuitive.

Postgresql supports JSON files, as well as their indexing and partial updates. The database supports even more additional data than MySQL. Users can upload user-defined types, geospatial data, create multi-dimensional arrays, and a lot more.

SQL Server also provides full support of JSON documents, their updates, functionality, and maintenance. It has a lot of additional features for GPS data, user-defined types, hierarchical information, etc.

Overall, all three solutions are pretty universal and offer a lot of functionality for non-standard data types. MySQL, however, puts multiple limitations for JSON files, but other than that, it’s highly compatible with advanced data.

Replication and Sharding

When the application grows, a single server can no longer accommodate all the workload. Navigating single storage becomes complicated, and developers prefer to migrate to different ones or, at least, create partitions. The process of partitioning is the creation of many compartments for data in the single process.



Replacing is easier in NoSQL databases because they support horizontal scaling rather than vertical – increasing the number of locations rather than the size of a single one. Still, it’s possible to distribute data among different compartments even in SQL solutions, even if it’s slightly less efficient.

MySQL allows partitioning databases with hashing functions in order to distribute data among several nodes. Developers can generate a specific partition key that will define the data location. Hashing permits avoiding bottlenecks and simplifying maintenance.

Postgresql allows making LIST and RANGE partitions where the index of a partition is created manually. Developers need to identify children and parent column before assigning a partition for them.

SQL Server also provides access to RANGE partitioning, where the partition is assigned to all values that fall into a particular range. If the data lies within the threshold, it will be moved to the partition.


The database ecosystem is important because it defines the frequency of updates, the availability of learning resources, the demand on the market, and the tool’s long-term legacy.

MySQL Ecosystem

MySQL Ecosystem

MySQL is a part of the Oracle ecosystem. It’s the biggest SQL database on the market with a large open-source community. Developers can either purchase commercial add-ons, developed by the Oracle team or use freeware installations. You will easily find tools for database management, monitoring, optimization, and learning. The database itself is easy to install – all you have to do is pretty much download the installer.

MySQL has been a reliable database solution for 25 years, and statistics don’t pinpoint at any sights of its decline. It looks like MySQL will keep holding a leading position not only among SQL tools but also among all the databases in general.

Postgresql Ecosystem

Postgresql Ecosystem

The Postgresql community offers a lot of tools for software scaling and optimization. You can find add-ons by your industry – take a look at the full list on the official page. The integrations allow developers to perform clustering, integrating AI, collaborating, tracking issues, improving object mapping, and cover many other essential features.

Some developers point out that Postgresql’s installation process is slightly complicated – you can take a look at its official tutorial. Unlike MySQL, which can run right away, Postgresql requires additional installations.

SQL Server Ecosystem

SQL Server Ecosystem

SQL Server is highly compatible with Windows and all Microsoft OS and tools. If you are working with Windows, SQL server is definitely the best option on the market. Users of the database receive access to many additional instruments that cover server monitoring (Navicat Monitor), data analysis, parsing (SQL Parser), and safety management software (DBHawk).

SQL Server ecosystem is oriented towards large infrastructures. It’s more expensive than open-source competitors, but at the end of the day, users get access to frequently updated official ecosystem and active customer support.

What is the difference between SQL and MySQL? MySQL is an open-source database, whereas SQL Server is a commercial one. MySQL is more popular, but SQL Server comes close.


For a start, we analyzed the DB Engines ratings of every compared engine. The leader is MySQL, with second place as the most popular database and second most popular relational solution. SQL Server takes third place, while PostgreSQL is ranked fourth.

The statistics by Statista shows the same tendency. MySQL is ranked second, leaving the leading position to Oracle, the most popular DBMS today. SQL Server follows with a slim difference, whereas Postgresql, which comes right after, is a lot less recognized.

databases popularity

MySQL, therefore, is the most demanded database on the market, which means finding competent teams, learning resources, reusable libraries, and ready add-ons will be easy. So, if you are choosing between SQL Server vs MySQL in terms of market trends, the latter is a better choice.

Companies using MySQL

  • Google
  • Udemy
  • Netflix
  • Airbnb
  • Amazon
  • Pinterest

MySQL is used widely by big corporations and governmental organizations. Over the last 25 years, the solution has built a reputation of a reliable database management solution, and as time shows, it’s indeed capable of supporting long-running projects.

Companies that use PostgreSQL

  • Apple
  • Skype
  • Cisco
  • Etsy

Postgre is known for its intuitive functionality and versatile security settings. This is why its main use cases are governmental platforms, messenger applications, video chats, and e-commerce platforms.

Companies using SQL Server

  • JPMorganChase
  • Bank of America
  • UPS
  • Houston Methodist

SQL Server is a go-to choice for large enterprises that have vast business logic and handle multiple applications simultaneously. Teams that prioritize efficiency and reliability over scalability and costs typically choose this database. It’s a common option for “traditional” industries – finances, security, manufacturing, and others.

MySQL vs PostgreSQL vs SQL Server infographic


The choice between the three most popular databases ultimately boils down to the comparison of the functionality, use cases, and ecosystems. Companies that prioritize flexibility, cost-efficiency, and innovation usually choose open-source solutions. They can be integrated with multiple free add-ons, have active user communities, and are continuously updated.

For corporations that prefer traditional commercial solutions, software like SQL Server backed up by a big corporation and compatible with an extensive infrastructure, is a better bet. They have access to constant technical support, personalized assistance, and professional management tools.

If you are considering a database for your project, getting a team of experts who will help you define the criteria and narrow down the options is probably the best idea. You can always get in touch with our database developers – we will create a tech stack for your product and share our development experience.

10 Top Data Analysis Tools for 2021

While data is a necessary resource in business today, it’s also virtually useless in its raw form. There are zettabytes of data being generated every year now. Most of that data will be useful, but only through the work professionals with proper data analytics skills can do.

Companies that aren’t making business decisions based on insight gathered from data are already losing out to the competition in today’s commercial world.

It takes a qualified data analyst proficient in the latest data analysis tools to take that massive amount of data, glean what’s essential, prepare it in a form that’s easy for others to understand, and then create a plan of action.

But just like the data itself, there’s also a copious amount of data analytics tools at the analyst’s disposal. Depending on your needs, goals, and what types of data analysis you need to do, it’s worth learning about the various options you have.

Here is the list of top 10 data analysis tools that we will discuss in detail:

  1. Sequentum Enterprise
  2. Datapine
  3. Looker
  4. KNIME
  5. Lexalytics
  6. SAS Forecasting
  7. RapidMiner
  8. OpenRefine
  9. Talend
  10. NodeXL


Top Data Analysis Tools to Learn

Data analytics tools are applications and software used by data analysts to develop and perform the necessary analytical processes that help companies make better, more informed business decisions while lowering costs and increasing profits.

1. Sequentum Enterprise

Suppose you need an advanced data extraction tool for web crawling. In that case, Sequentum Enterprise is an excellent tool that enables development, testing, and production, geared towards large-scale web data extractions. Enterprise was designed with corporations that depend heavily on structured web data and legal compliance. Users can control and debug the crawler with C# or VB.NET, or they can write scripts.

Sequentum Enterprise offers advanced features that aren’t typically available in other solutions, such as the ability to monitor data extraction success criteria, legal compliance, and production failover.

However, this data analysis tool may be a bit overwhelming for those who only know the fundamentals of data analysis and programming. If you’re a beginner, you may want to hold off on using Sequentum Enterprise until you gain more career experience.

2. Datapine

Datapine delivers simple yet powerful analysis features for both beginners and advanced users alike. This popular business intelligence tool boasts a drag-and-drop interface, powerful predictive analysis features, and interactive dashboards and charts. It also features an advanced SQL mode that helps advanced users build their own queries. Datapine’s defining characteristics are speed and simplicity.

3. Looker

Cloud-based Looker provides an intuitive drag-and-drop interface that’s easy to use. It offers data analytics and management, business intelligence, and advanced visualization capabilities. The tool’s multi-cloud strategy supports the use of various data sources and deployment methods. Looker also easily connects with an array of databases, including Snowflake and Amazon Redshift. It has a built-in code editor that allows data analysts to modify generated models.


This open-source data analysis tool enables users to leverage powerful scripting languages like R and Python to build data science apps. It provides in-memory processing, as well as multithreaded data processing. Its drag-and-drop GUI is simple to navigate and easy to learn for beginners and provides users with an excellent way to analyze and model data via visual programming.

5. Lexalytics

The Lexalytics Intelligence Platform is perfect for businesses that want to better understand their customers’ or employees’ experiences with their products and services by leveraging the power of text data. Lexalytics collects information from posts, tweets, and comments and helps analysts gain the best possible insights. The software helps identify attitudes and feelings through a combination of text analytics, machine learning, and natural language processing, and more. Professionals with can deploy Lexalytics in public, private, and hybrid cloud environments.

6. SAS Forecasting

Data analysts working on business solutions need to understand the potential variables involved and how events may unfold in the future. That’s where forecasting and data analytics tools come in. SAS Forecasting for Desktop offers a good selection of forecasting methods, including “what-if” analysis, event modeling, scenario planning, and hierarchical reconciliation. This powerful data analysis tool provides scalability and modeling, easy-to-use GUI, data preparation, and an event-modeling console.

7. RapidMiner

RapidMiner is a very popular data science platform used by over 40,000 organizations. It allows users to improve productivity through automated machine learning. It doesn’t require users to write code manually and provides built-in security controls. It also facilitates team collaboration and includes a visual workflow designer for Spark and Hadoop. It offers over 1500 algorithms and data functions, support for third-party machine learning libraries, integration with Python or R, and advanced analytics.

8. OpenRefine

If you’re looking for a free data cleaning and transformation tool, you can’t go wrong with OpenRefine. Formerly known as Google Refine, this open-source data analysis tool is highly secure. Once the data is cleaned, data analysts can extend the dataset to external web services. This software supports numerous file formats for importing and exporting purposes. You can import CSV, TSV, XML, RDF, JSON, Google Spreadsheets, and Google Fusion Tables, then export the data in TSV, CSV, HTML table — and Microsoft Excel.

If you’re searching through the top data analytics tools, check this out. OpenRefine is available in multiple languages and can be used easily by companies of all sizes.

9. Talend

ETL (short for extract, transform, load) is a popular data integration process, and Talend provides an excellent entry data analytics tool. This Java-based tool is used to collect and transform data through preparation, integration, and cloud pipeline design. Talend can efficiently process millions of data records and handle any size project. It features data preparation, big data integration, cloud pipeline designer, and Stitch Data Loader, covering an assortment of data management requirements for any sized organization.

10. NodeXL

Referred to as the “MSPaint of Networks,” this tool comes in two versions: NodeXL Basic and NodeXL Pro. The Basic version is a free, open-source tool that enables data scientists to visualize and analyze network graphs in Microsoft Excel. The Pro version offers additional features that extend to social media network data and AI-powered text and sentiment analysis capabilities. NodeXL is a good choice if you’re looking for data representation, data import, graph analysis, and graph visualization. It’s compatible with Microsoft Excel 2007, 2010, 2013, and 2016.

While this list is hardly exhaustive, it’s a good starting point. You may actually need to use a combination of different data analytics tools and techniques in some cases. One thing is clear, however: data science and data analysis are already essential practices for any business that wants to thrive in the modern world.

AngularJS Vs. Angular 2 Vs. Angular 4: Understanding the Differences

The technologies that enable the Internet tend to change, progress and evolve at rapid speeds, as requirements change and developers build better versions of the software. Angular is a case in point, with wide changes in just a few years. Google developed AngularJS in 2009 and version 1.0 was released in 2012. Angular has since dominated the world of open-source JavaScript frameworks, with the enthusiastic support and widespread adoption among both enterprises and individuals. As a result, Angular has evolved from the AngularJS version 1.0 to Angular version 2.0 and now the latest Angular version 4.0, all in just five years.

Despite the potential benefits of the upgrades, some in the Angular community have concerns about migrating to a newer version. Keep reading to find out what has changed in Angular and why migrating to the latest version is a good idea.

In this article we will cover the following topics that will give you clear understanding of the differences between AngularJS, Angular 2 and Angular 4 and more, including:

  • Types of Angular versions
  • Difference between AngularJs and Angular Versions
  • Advantages and disadvantages of AngularJs and Angular Versions

Angular Versions

Before we dive into the differences, let’s first clarify each angular version with a description:

    • AngularJS

      is an open-source, JavaScript-based, front-end web application framework for dynamic web app development. It utilizes HTML as a template language. By extending HTML attributes with directives and binding data to HTML with expressions, AngularJS creates an environment that is readable, extraordinarily expressive and quick to develop.

    • Angular 2

      is the blanket term used to refer to Angular 2, Angular 4 and all other versions that come after AngularJS. Both Angular 2 and 4 are open-source, TypeScript-based front-end web application platforms.

    • Angular 4

      is the latest version of Angular. Although Angular 2 was a complete rewrite of AngularJS, there are no major differences between Angular 2 and Angular 4. Angular 4 is only an improvement and is backward compatible with Angular 2.

Difference Between AngularJs and Angular Versions

Below is a comparison of AngularJS to Angular, because Angular includes both version 2 and version 4. We compare architecture, language, expression syntax, mobile support, and routing.

1. Architecture


The architecture of AngularJS is based on the model-view-controller (MVC) design. The model is the central component that expresses the application’s behavior and manages its data, logic, and rules. The view generates an output based on the information in the model. The controller accepts input, converts it into commands and sends the commands to the model and the view.

Angular 2 

In Angular 2, controllers and $scope were replaced by components and directives. Components are directives with a template. They deal with a view of the application and logic on the page. There are two kinds of directives in Angular 2. These are structural directives that alter the layout of the DOM by removing and replacing its elements, and attributive directives that change the behavior or appearance of a DOM element.

In Angular 4, the structural derivatives ngIf and ngFor have been improved, and you can use if/else design syntax in your templates.

2. Language


AngularJS is written in JavaScript.

Angular versions

Angular uses Microsoft’s TypeScript language, which is a superset of ECMAScript 6 (ES6). This has the combined advantages of the TypeScript features, like type declarations, and the benefits of ES6, like iterators and lambdas.

Angular 4 is compatible with the most recent versions of TypeScript that have powerful type checking and object-oriented features.

3. Expression Syntax


To bind an image/property or an event with AngularJS, you have to remember the right ng directive.

Angular versions

Angular focuses on “( )” for event binding and “[ ]” for property binding.

4. Mobile Support

AngularJS was not built with mobile support in mind, but Angular 2 and 4 both feature mobile support.

5. Routing

AngularJS uses $routeprovider.when() to configure routing while Angular uses @RouteConfig{(…)}.


AngularJS was originally developed for designers, not developers. Although there were a few evolutionary improvements in its design, they were not enough to fulfill developer requirements. The later versions, Angular 2 and Angular 4, have been upgraded to provide an overall improvement in performance, especially in speed and dependency injection.

1. Speed

By providing features like 2-way binding, AngularJS reduced the development effort and time. However, by creating more processing on the client-side, page load was taking considerable time. Angular2 provides a better structure to more easily create and maintain big applications and a better change detection mechanism. Angular 4 is the fastest version yet.

2. Dependency Injection

Angular implements unidirectional tree-based change detection and uses the Hierarchical Dependency Injection system. This significantly boosts performance for the framework.

Advantages and Disadvantages Comparision

Because they are Google products, all Angular versions are trustworthy and enjoy great support from Google engineers and the large community of Angular users and developers. However, each angular version has its own advantages and disadvantages.

1. AngularJS


  • It is unit testing ready.
  • It has great MVC data binding that makes app development fast.
  • Using HTML as a declarative language makes it very intuitive.
  • It is a comprehensive solution for rapid front-end development since it does not need any other frameworks or plugins.
  • AngularJS apps can run on every significant program and advanced cells including iOS and Android-based phones and tablets.


  • It is big and complicated due to the multiple ways of doing the same thing.
  • Implementations scale poorly.
  • If a user of an AngularJS application disables JavaScript, nothing but the basic page is visible.
  • There’s a lagging UI if there are more than 200 watchers.

2. Angular 2


      • TypeScript allows code optimization using the OOPS concept.
      • It is mobile-oriented.
      • It has improved dependency injection and modularity.
      • It provides more choice for languages such as Dart, TypeScript, ES5, and ES6 for writing codes.
      • It offers simpler routing.


      • It is more complicated to set up compared to AngularJS.
      • It’s inefficient if you only need to create simple, small web apps.

3. Angular 4


      • It enables a fast development process.
      • It’s ideal for single-page web applications with an extended interface.
      • Full TypeScript support helps in building bulky applications.
      • Tests are easy to write.
      • An improved View Engine generates less code in AOT mode.
      • It has a modularized animation package.


      • It’s slow when displaying enormous amounts of data.


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