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Understand what are the characteristics of key technologies for backend developers
In software development, the back-end area is responsible for managing and applying all the business rules that a given system may have. It works like a human brain: it coordinates all actions and decides what kind of behavior will be applied in each situation.
The backend area is also responsible for storing and retrieving all the information needed for development, doing this with the help of a database.
In the midst of such a wide variety of technologies, tools and frameworks available on the market, finding the ideal solution for the work of the professional who works in this area is a task that requires a lot of attention and research.
This is because, for the planning and construction of the back-end, it is necessary to consider the problem domain and the market segment in which it operates, as well as the available tools and technologies, comparing the advantages and disadvantages of each one of them, in addition to the technical capacity of the developer team.
Here are nine recommended technologies for working on the backend.
1.GraphQL
GraphQL is a query language developed by Facebook in 2012 and made open source in 2015. The tool provides a solution to optimize queries by capturing only what is needed from the database.
It generates very significant benefits when it comes to projects that need to process data quickly or that have high data throughput.
The technology can reduce data transfer costs, both on the server side (transfer) and on the client side (4G band), and guarantee faster response times, even on slow connections.
However, the disadvantages of using GraphQL are the learning curve of the professional who will work with the language and the greater complexity in making use of caching.
2. Elasticsearch
Elasticsearch is a search data analysis engine built on Apache Lucene, developed in Java and distributed in open source. The technology allows store and search large volumes of data quickly (near real-time, with millisecond responses).
For that, use a document-based structure instead of tables and schemes, in addition to using extensive REST APIs to store and search the data. The tool is used by Wikipedia and Github, supporting several languages: Java, Python, PHP, JavaScript, Node.js and Ruby.
However, Elasticsearch needs extra queries to gather the datawhich consumes more search time and decreases solution performance.
3. Redis
Redis is a remote dictionary server designed to high speed data Storage As a database management system (DBMS), Redis stores information in memory and has a key-value store, which is both a cache and a memory.
Used by Twitter and Snapchat, the tool offers response times of less than 1 millisecond, being useful for real-time applications such as gaming, financial services and the internet of things (IoT).
However, the solution works only with commands, not with query language. This means that all accesses must be anticipated and designed in advance by the backend developer.
4. Apache Kafka
Apache Kafka is a data distribution platform which can be used as message system
The technology allows applications to manage the streaming billions of simultaneous eventsovercoming the limitations of periodic batch streaming that prevent real-time communication.
The solution, adopted by Netflix and Airbnb, is open source and enables scaling data horizontallywith processing split between physical or virtual machines, increasing the reliability of data transmission.
However, the solution is difficult to implante Once the on-premises infrastructure is in place to run the tool, the backend developer needs to provision the machines, design the cluster, and perform routine maintenance to replace the machines that fail.
5. Java
O Java is one of the oldest and most popular programming languages. Developed by Sun Microsystems in 1995, the solution is designed to have fewer implementation dependencies. Java software can run on laptops and smartphones as well as scientific supercomputers.
Cross-platform, object-oriented and network-centric, the language is also a great choice for those who want to develop android appssince, together with Kotlin, it is the main language of the platform.
6. C#
Microsoft developed the C Sharp (C#) as a programming language to be used on your .NET platform. The solution was based on C++ and has influences from Object Pascal and especially Java.
Like Java, C# is an object-oriented and cross-platform language, in addition to being strongly typed. It can be used in several scenarios, such as in the development of: desktop applications, on the web and even in games (Unity uses C# as the development language for its engine, for example).
7. PHP
Hypertext Preprocessor (PHP) is a scripting language that runs applications on web servers independently of the online browser With a syntax similar to the C language, PHP has been adopted by social networks for content management systems, website development and e-commerce, among others.
PHP is open source and can be easily connected to a database In addition, it is simple to use and learn, and can be combined with other programming languages, making the software package more effective.
However, the technology not suitable for working with large content and uses more framework resources and tools, harming the performance of online applications. The solution also has few debug solutions compared to other languages, being more error prone.
8. JavaScript
JavaScript (JS) is a popular programming language for the web, both among professional developers and enthusiasts.
Embedded in all web browsers and mobile devices, the language has a rich ecosystem that makes it one of the most powerful tools available for deployment in the technology industry.
It is one of the hottest languages on the market today because it is quite dynamic and has a constantly growing community, being used both on the front-end, in frameworks such as React.js and Vue.js, and on the back-end, with the already famous Node.js (JavaScript execution environment).
9. Python
Python has many features that facilitate use and learningallowing the developer to focus on solutions rather than trying to understand the programming language.
In addition, the tool has a vast number of librariesincluding related to machine learning, artificial intelligence (AI) modeling and game development.
Free and open source, Python works on different platforms simply and without the need for compilation. Despite this practicality, the language has speed limitationsdoes not work well on mobile platforms, consumes a lot of memory and is not easy to test.
Source: XP Educação, Back4app, Digital House, Radixweb, AWS, Redhat, AWS, Redis, AWS, Towards Data Science, Google Cloud, Microsoft, PHP, Mozilla,
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