7 Data Analytics Trends for 2022 That You Need to Watch Out For
Infrastructure
27.05.2022
9 min read
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Over the past few years, data analytics has become a goldmine. The more data a company has, the more insight they have to make smarter decisions. With the data industry worth an estimated $274 billion in 2022, it’s no wonder big data is the driving force for the future.
The pandemic continues to drive business digital transformations. Applications have an improved capacity to interpret incoming data for its user. This means that the opportunities for business growth are endless.
Organizations embrace the cutting-edge data technology available to them. Data analytics continues to evolve, from big data to artificial intelligence (AI), data-driven models are in demand more than ever.
With the pandemic and its economic disruptions, businesses now realize they need to better use the data available to them. With data analytics traditionally used to assess ‘what happened?’ It is now used to predict ‘what will happen?’
With that in mind, let’s take a look at the top seven data analytics trends for 2022 and how they can benefit your business.
Smarter artificial intelligence
With the introduction of artificial intelligence (AI), organizations are experiencing a specialized change in business strategy. AI accelerates business decision making by automating processes that determine data analytics.
By developing training models and testing metrics in agile methodology, AI ensures rapid data insight without the need for data scientists. Organizations are utilizing AI algorithms to measure, predict, and interpret large amounts of data. Data refers to the market, customers, and online applications.
The pandemic and the rise of remote work have increased opportunities to track and measure data. Due to this rise in data availability, establishing a new data-driven culture was necessary. A culture that fuels investments via AI-based technologies produces intuitive data analytics.
AI systems work with both large and small data sets. The systems protect privacy, are adaptive, and provide a faster return on investment. The advancement of intuitive UIs in services such as metabase analytical services promotes accessibility and efficiency. Simply put, it’s never been easier to interpret data.
Edge computing
CTO of Kinetica, Nima Negahban, describes edge computing as ‘data analysis that takes place on a device in real-time.’ She goes on to say that ‘edge computing is about processing data locally, and cloud computing is about processing data in a data center or public cloud.’
Edge computing delivers data analytics technology closer to the physical asset. From wrist wearables to mobile traffic apps, worldwide spending on edge computing is expected to reach $176 billion in 2022.
To introduce edge computing into business systems, industries need to include IoT app development and other data transformation services.
By processing data storage closer to the devices that collect it, edge computing is more reliable. With real-time processing there is no issue of latency, it is also more cost-efficient than cloud-based storage.
Cloud-based data solutions
As data is being produced in large quantities, there will be an increasing shift towards cloud-based solutions. A database in the cloud involves labeling, cleaning, formatting, and collecting. It equates to a mammoth amount of storage for one location. This is where cloud-based platforms come into play.
The cloud opens the door for the next generation of data warehousing. With added accuracy and security in the form of an existing QA framework. New practices such as data mesh, data fabric, and Data Vault 2.0 have been intrinsically built via the cloud.
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Data mesh - a holistic approach that allows data products connectivity across many domains. Enabling an information exchange without the need for storage.
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Data fabric - architecture that enables data access in a distributed environment.
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Data Vault 2.0 - based on the potential of the cloud, Data Vault 2.0 provides greater productivity. Driven by metadata for collaborative configuration and management of testing models.
Whilst a private cloud for your business can be costly, a hybrid cloud provides both private and public agility. A hybrid cloud approach offers companies the opportunity to switch between multiple cloud platforms. This is a more cost-effective approach for your business.
Data fabric
Due to our booming digital age, our interconnected ecosystem has become more complex than ever. Finding a solution to connect devices, applications, and data infrastructure formats is a constant challenge. Data fabric emerges as the solution.
Data fabric is a new answer to an old dilemma. With 73% of analytical data going unused, it has become essential for missing data to become discoverable. Data fabrics fuse data from internal silos and external data sources. This leads to the discovery of effective networks and business applications.
More companies are using data fabric architecture to produce more discoverable, pervasive, and reusable data from all environments. This includes private, public, and mulitcloud cloud systems.
Data fabric connects data from contrasting applications to identify new data relationships. It’s a form of referral software that enables rapid decision-making and cost-efficiency. Accelerating hybrid data integration ensures digital security and greater business value.
Data fabric is a technology that will become commonplace over the next few years.
Augmented analytics
Augmented analytics is a leading analytics concept that uses natural language processing (NLP), machine learning (ML), and AI. What used to be handled by a data scientist is now automated to offer economical data sharing and insight discovery.
Augmented analytics produces data integration from internal and external enterprises. Due to the specialist applications, the outcomes are more precise. NLP, ML, and AI ensure in-depth reports and forecasts, data processing, analytics, and visualization.
- Natural language processing - NLP provides computers with the ability to understand text and spoken words. It’s included in identifying patterns and trends in corporate operations. NLP is essential for tracking Twitter Analytics, understanding customer satisfaction, and smart assistants.
Machine learning - there is a growing use of data analytics and ML to predict what will happen in digital analytics and the best response to counter. Using ML to target specific customer needs, such as analyzing social media activity to determine what product they might buy.
- AI - AI helps make predictions more accurate, efficient, and cost-effective without the need for human intervention. AI will continue to be implemented in many different industries due to its unique possibilities.
Machine learning - there is a growing use of data analytics and ML to predict what will happen in digital analytics and the best response to counter. Using ML to target specific customer needs, such as analyzing social media activity to determine what product they might buy.With the introduction of all three, it has never been easier to measure, interpret, and predict results. Targeting specific customer behaviors reduces problems such as customers leaving their digital shopping cart abandoned and inefficient resources.
NLP, MI, and AI have the capabilities to assist business intelligence and aid business users.
Businesses are focussing on operational agility and resilience to recover from difficult market situations.
Automated machine learning
Applying ML models to real-world situations is known as automated machine learning (AutoML). ML is more user friendly when it is automated. It can create and deploy systems that even non-experts can master.
AutoML enhances monotonous workloads. Leaving human interaction free for more complemented questions (think of automation in call centers).
Businesses are counting on AutoML to increase data insight as end-users have direct access to these applications. As more users utilize this model, generating insight becomes easier and easier.
With the continuous release of machine learning tools, technical aspects of data science will become automated. This accelerates decision making by automating processes that data scientists would generally perform.
Much like how a mobile application is a must for small business development, AutoML is essential for rapid data insight. The more platforms that use it, the faster it becomes.
XOps
XOps has become an important fixture in business enterprise. It encompasses DataOps, ModelOps, AIOps, and PlatformOps, enabling automation of technology and processes. Making it a dominant combination of IT disciplines and strategic decision making when it comes to AI and machine learning (ML).
XOps data professionals can process defined goals that align with their business priorities. Its main focus is to enhance business operations as well as customer experience. Enhancing operations means enhanced security, which awards applications and offers advanced protection from DDoS attacks.
XOps strives to reduce duplication, ensuring more efficient and reliable data outcomes. Enabling XOps data and analytics allows the user to begin automation from the beginning rather than as an afterthought. It allows you to orchestrate your automating software in a way that meets measurable goals. Allowing more efficient data collection.
To cut a long story short
As data science continues to take the spotlight. Breakthroughs in future analytics continue to progress. Presently, data has never been more accessible, with companies able to collect, manage, analyze, and leverage data for future business intelligence.
Data analytics has become an essential part of business functionality. AI trends and data analysis provide valuable insight that improves business automation, accessibility, and intuition.
Organizations that successfully impose the above trends will be able to harness data strategically and efficiently. Tools such as automated machine learning and edge computing can improve customer algorithms via results and feedback.
Ensuring the process of data accumulation and evaluation is accessible, business actions have never been more necessary. Constantly improving analytics means businesses are constantly preparing for the future.
Author: Emily Rollwitz - Content Marketing Executive, Global App Testing
Emily Rollwitz is a Content Marketing Executive at Global App Testing, a remote and on-demand API automation testing tools company helping top app teams deliver high-quality software, anywhere in the world. She has 5 years of experience as a marketer, spearheading lead generation campaigns and events that propel top-notch brand performance. Handling marketing of various brands, Emily has also developed a great pulse in creating fresh and engaging content. She’s written for great websites like Airdroid and Shift4Shop. You can find her on LinkedIn.
Infrastructure
27.05.2022
9 min read
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Infrastructure
PostgreSQL vs MySQL: Which Database Is Right for Your Business?
PostgreSQL and MySQL are among the most popular relational databases. In this article, we will examine the functional differences between them and compare their performance so that you can choose the database that is suitable for your business.
PostgreSQL vs MySQL
Despite the increasing similarity in features between PostgreSQL and MySQL, important differences remain. For example, PostgreSQL is better suited for managing large and complex databases, while MySQL is optimal for website and online-application databases because it is oriented toward speed. This follows from the internal structure of these relational database systems, which we will examine.
Data Storage in PostgreSQL and MySQL
Like any other relational databases, these systems store data in tables. However, MySQL uses several storage engines for this, while PostgreSQL uses only a single storage engine. On one hand, this makes PostgreSQL more convenient, because MySQL’s engines read and write data to disk differently. On the other hand, MySQL offers greater flexibility in choosing a data engine.
However, PostgreSQL has an advantage: its storage engine implements table inheritance, where tables are represented as objects. As a result, operations are performed using object-oriented functions.
Support
The SQL standard is over 35 years old, and only the developers of PostgreSQL aim to bring their product into full compliance with the standard. The developers of MySQL use a different approach: if a certain feature simplifies working with the system, it will be implemented even if it does not fully conform to the standard. This makes MySQL more user-friendly compared to PostgreSQL.
In terms of community support, the number of MySQL developers still exceeds those working with PostgreSQL, but you can receive qualified help in both communities. In addition, many free guides and even books have been written about PostgreSQL, containing answers to most questions. It is also worth noting that both platforms are free, but MySQL has several commercial editions, which can sometimes lead to additional expenses.
Programming Languages
Both systems support a wide range of programming languages. Among the popular ones are C++, Java, Python, lua, and PHP. Therefore, a company’s development team will not face difficulties implementing features in either system.
Operating Systems
MySQL is a more universal system that runs on Windows, Linux, macOS, and several other operating systems. PostgreSQL was originally designed for Linux, but with the REST API interface, it becomes an equally universal solution that operates on any OS.
Data Processing
PostgreSQL provides more capabilities for data processing. For example, a cursor is used for moving through table data, and responses are written to the memory of the database server rather than the client, as in MySQL. PostgreSQL also allows building indexes simultaneously for several columns. It supports different index types, allowing work with multiple data types. This database also supports regular expressions in queries. However, new fields in PostgreSQL can only be added at the end of a table.
Parallel data processing is better organized in PostgreSQL because the platform has a built-in implementation of MVCC (multiversion concurrency control). MVCC can also be supported in MySQL, but only if InnoDB is used. Concerning replication, PostgreSQL supports logical, streaming, and bidirectional replication, while MySQL supports circular replication as well as master-master and master-standby. Replication refers to copying data between databases located on different servers.
PostgreSQL and MySQL: Performance Comparison
Testing is fair only when comparing two clean, “out-of-the-box” systems. Indexed testing provides the following results:
Insertion: PostgreSQL is more than 2.7× faster, processing a 400,000-record database in 5.5 seconds versus 15 seconds for MySQL.
Inner join: PostgreSQL processes 400,000 records in 1.1 seconds, MySQL in 2.8 seconds: a gain of more than 2.5×.
Indexed sorting: PostgreSQL processes the same number of records in 0.9 seconds, MySQL in 1.5 seconds.
Grouping: For the same 400,000-record database, PostgreSQL achieves 0.35 seconds, MySQL 0.52 seconds.
Indexed selection: PostgreSQL is 2× faster: 0.6 seconds vs. 1.2 seconds.
When it comes to updating data, PostgreSQL’s update time increases gradually as the number of records grows, while MySQL processes them in roughly the same time, starting from 100,000 records. This is due to different data-storage implementations. Nevertheless, PostgreSQL holds a significant advantage over MySQL even with large data volumes: 3.5 seconds versus 9.5 seconds for 400,000 records—more than 2.7× faster.
Without indexes, PostgreSQL also shows surprisingly high performance, processing a 400,000-record database in 1.3, 0.7, and 2.2 seconds for inner join, selection, and update operations, respectively.
Thus, PostgreSQL delivers an average performance advantage of about 2× (2.06). Although MySQL was originally positioned as a high-performance platform, constant optimization by the PostgreSQL development team has resulted in greater efficiency.
Advantages for Developers
Here we consider only the unique features characteristic of each platform. Therefore, we will not discuss support for MVCC or ACID, as these features are present in both systems.
From a developer’s perspective, MySQL is advantageous because it:
Provides increased flexibility and is easily scalable, with more than ten storage engines based on different data-storage algorithms.
Handles small read-oriented databases more efficiently (i.e., without frequent writes).
Is easier to manage and maintain, because it requires less configuration and fewer preparatory steps before starting work.
From a developer’s perspective, PostgreSQL is advantageous because it:
Offers an object-oriented approach to data, enabling inheritance and allowing the creation of more complex table structures that do not fit the traditional relational model.
Handles write-oriented databases better, including validation of written data.
Supports object-oriented programming features, enabling work with NoSQL-style data, including XML and JSON formats.
Can support databases without limitations on data volume. Some companies use PostgreSQL to run databases as large as several petabytes.
PostgreSQL and MySQL Comparison
For clarity, the main features of both systems can be presented in a table:
PostgreSQL
MySQL
Supported OS
Solaris, Windows, Linux, OS X, Unix, HP-UX
Solaris, Windows, Linux, OS X, FreeBSD
Use cases
Large databases with complex queries (e.g., Big Data)
Lighter databases (e.g., websites and applications)
Data types
Supports advanced data types, including arrays and hstore
Supports standard SQL data types
Table inheritance
Yes
No
Triggers
Supports triggers for a wide range of commands
Limited trigger support
Storage engines
Single (Storage Engine)
Multiple
As we can see, several features are implemented only in PostgreSQL. Both systems support ODBC, JDBC, CTE (common table expressions), declarative partitioning, GIS, SRS, window functions, and many other features.
Conclusion
Each system has its strengths. MySQL handles horizontal scaling well and is easier to configure and manage. However, if you expect database expansion or plan to work with different data types, it is better to consider implementing PostgreSQL in advance. Moreover, PostgreSQL is a fully free solution, so companies with limited budgets can use it without fear of unnecessary costs.
24 November 2025 · 6 min to read
Infrastructure
How to Speed Up Development with Cloud Technologies
From the point of view of a software-development company, the computing infrastructure needed for developing and supporting software can be of two types:
Servers owned by the development company
The application developer physically hosts the server equipment directly in their office or data center, maintains it themselves, and ensures data security.
This approach has become less popular over the years. Developers prefer using a provider that offers computing power on its own equipment on a paid basis.
Advantages:
Full physical control over the servers
High data security and confidentiality
Disadvantages:
Financial costs for server hardware
Financial costs for maintenance (specialists, equipment)
Need for experience and expertise to support data centers
Servers managed by another organization
The application developer chooses a provider from whom they remotely rent computing resources. Today, this is the most optimal option for most companies.
Advantages:
No need for own equipment
The amount of computing resources used is flexibly adjustable
No need for experience or expertise in network communications
Disadvantages:
The provider is responsible for data security and confidentiality
No physical control over the servers
What Are Cloud Technologies?
Cloud technologies are technical solutions that provide users with computing power through a flexibly configurable online service.
The cloud belongs to the provider of cloud resources, who owns the physical servers.
Although clouds from different providers have their own unique features, they generally include the following basic components:
servers
storage
databases
network infrastructure
software
testing and analytics tools
Physical and Logical Layers
The main feature of the cloud is that, unlike the traditional server-rental model, it separates the physical and logical layers.
This means that data processing and network interactions exist on top of the physical base—the server machines located in data centers (DCs).
This is why cloud allows the flexible configuration of the resources a specific application requires.
This architecture can be represented hierarchically:
Logical layer: operating systems, virtualization systems, containers, server applications, application-level network protocols, business logic, client applications
Physical layer: server machines, motherboards, ASIC controllers, low-level network protocols
Cloud Models
There are several types of clouds that differ in the degree of abstraction of the software (logical) level.
IaaS (Infrastructure as a Service)
Only computing power is provided, with just the operating system and sometimes additional software.
Examples:
Amazon Web Services
Microsoft Azure
Google Cloud
Hostman Cloud Servers
PaaS (Platform as a Service)
Along with computing power, a set of tools for development and administration is provided.
Examples:
Red Hat OpenShift
AWS Elastic Beanstalk
Google App Engine
Hostman App Platform
SaaS (Software as a Service)
A client application with ready-to-use functionality that does not require specialized knowledge.
Examples:
Google Drive
Salesforce
Dropbox
Types of Clouds
Clouds differ in their degree of isolation, from fully public to fully private. There are also mixed implementations.
Public
Computing resources are located in the provider’s data center. All cloud provider resources are distributed among users depending on the chosen pricing plans.
Private
The cloud is completely isolated (sometimes even disconnected from the global Internet) and is physically located on the user's server equipment.
There are also cases when the cloud provider offers isolated server resources for a specific client, while other users use the public part of the cloud.
Hybrid
A combination of a private cloud and a public one. Usually, business logic containing sensitive data is placed in the private cloud, while “peripheral” computing is performed in the provider’s public cloud.
Advantages of Cloud Technologies
Simplified Development
Using any of the existing cloud models clearly simplifies the development process.
The closer a chosen model is to SaaS, the easier it is to interact with the cloud, but the more standardized and typical the solution becomes.
The closer a chosen model is to IaaS, the more precisely the cloud can be configured, but the more complex it becomes to maintain.
Therefore, the cloud model is selected individually for every case. A standardized solution may not fit someone if the product being developed requires a more unique technical implementation.
For example, using cloud databases (DBaaS) provided by Hostman removes many responsibilities from the developer regarding managing their own storage.
While deploying a database cluster can be done fairly quickly and easily, using it properly “in the long run” requires certain expertise.
Thus, to stay focused on development, it makes sense to entrust this function to an experienced provider with established methodologies, expertise, and the necessary technical solutions.
Cost Optimization
In the cloud, user expenses match the amount of resources used in their work tasks.
This payment model, "pay-as-you-go", is fundamental to cloud computing: you pay only for the resources you use.
If, at some point, more resources are needed, they are allocated quickly and increase costs. Conversely, extra resources can be “returned” to the provider, reducing expenses.
In other words, the user’s financial expenses correlate with the number of resources consumed. Allocation and release of resources happen instantly.
Additionally, delegating cloud-infrastructure management to the provider eliminates the developer’s costs associated with maintaining their own data center:
Hiring specialists and administrators
Purchasing server equipment
Renting premises
Faster Deployment
Instant access to cloud resources and the ability to configure them flexibly significantly reduces the time:
From the start of MVP development of a new product to its deployment in a production environment
From the start of developing new features to adding them to the finished product
For example, the development process of a new service might look like this:
Managers formalize requirements for the future product.
Developers determine the technology stack.
The development company rents the necessary computing resources from a cloud provider, including cloud servers, Kubernetes clusters, and object storage.
Developers write code, which is automatically deployed in the provider’s cloud environment.
The new product is tested, and user feedback is collected.
Based on the collected data, managers decide whether further support is justified. If the product does not meet expectations, the allocated resources are returned to the provider. If successful, the computing resources can be expanded.
In some cases, a developer may use multiple clouds from completely different providers, forming what is called a multi-cloud.
1. Cloud Server
Cloud servers differ from dedicated servers in that the “physical” configuration is adjusted within a certain range.
For example, Hostman cloud servers can be parameterized using a configurator. In this case, the rental cost corresponds to the capacities selected in the account panel.
Accordingly, when workloads change, a cloud server can be scaled: CPU cores, RAM, disk space, or connection speed can be increased.
2. Cloud Databases
In addition to the computing power provided by the cloud server, most projects require a specialized database.
Hostman provides most popular database engines as a separate cloud service:
MySQL
PostgreSQL
Redis
and others
As with servers, computing resources for a database can be configured based on several parameters:
Number of cores
RAM
NVMe disk size
Rental period
When ordering a database, you don’t need to configure hardware, track software updates, or administer the database: the infrastructure is managed by Hostman.
3. Kubernetes
A crucial component of cloud development is automation of building, deploying, and testing using CI/CD pipelines.
CI/CD is the basis of the DevOps methodology, whose goal is to automate changes to software.
Cloud services provide the needed capabilities for this. For example, GitHub CI/CD and GitLab CI/CD infrastructures have special cloud tools that simplify creating CI/CD pipelines, including test environments, storage, notification functions, and databases.
Modern applications consist of microservices placed in containers. Kubernetes, the standard for container orchestration, is used for management and monitoring.
Kubernetes clusters from Hostman can be a suitable solution.
In this case, the developer avoids the costs associated with maintaining their own infrastructure as the execution of Kubernetes applications is handled by the provider.
Conclusion
In this article, we reviewed what efficient development using cloud services looks like, and also mentioned the most popular developer tools:
Cloud servers
Databases
Monitoring tools (CI/CD, Kubernetes)
While creating your own data centers requires time, expertise, and significant financial investment, the cloud is provided by a provider in a refined and secure form.
A ready-made cloud helps developers immediately focus on the business logic of the application, thus speeding up development and integration of innovations.
Today, public cloud services are available even in areas that were previously predominantly non-cloud. Clouds are being implemented even in enterprises unrelated to IT.
Therefore, cloud technologies not only accelerate development but also accelerate digital transformation as a whole.
24 November 2025 · 8 min to read
Infrastructure
Microservices Architecture: What It Is, Who It’s For, and Tools to Use
Every developer strives to speed up product development while maintaining enough flexibility and confident control over the process. Microservices application architecture helps solve these tasks and, over the past 10 years, has begun actively competing with the traditional monolithic approach. To begin, let’s look at the difference between them.
Microservices Architecture vs. Monolith
The difference between these two software development approaches is easiest to illustrate with an example. Let’s imagine two online stores: one implemented as a monolith and the other as microservices.
A monolithic online store is a single, indivisible structure that combines all components: databases (catalog, customer data), shopping cart, order and payment forms. All of these elements are tightly interconnected and located on the same server.
In a microservices system, each component is an independent module that developers can work on separately. And naturally, nothing requires hosting these modules on a single server.
Thus, microservices architecture is a kind of constructor that allows you to add new elements painlessly as you scale the application. A monolith, on the other hand, can be compared to a solid wall: scaling here is only possible by adding another identical monolith.
It’s worth adding that microservices are sometimes mistakenly perceived as a set of very small services. This is not the case: for example, the database of a large online store may contain millions of records and occupy tens of gigabytes, yet still be just one of the modules within the microservices architecture of the whole application.
Comparing Microservices and Monoliths by Key Criteria
Now let’s look at the main characteristics of microservices technology in comparison with a monolith and see how both approaches solve the same developer tasks.
Release Cycles
Development speed and frequency of updates increase with microservices thanks to modularity: changes are made not to the entire codebase but to individual modules. With a monolith, however, the whole platform must be updated first, which increases testing and debugging time. As a result, development slows down and updates are released less frequently.
Technology Stack
The microservices model offers significantly greater flexibility because each service can be written in its own programming language and may use different libraries and data-storage technologies. With a monolith, the situation is different: changing the technology stack is nearly impossible. Developers are forced to stick to the initial tools.
Developer Onboarding
Each module in a microservices architecture is self-contained, making it possible to bring in programmers who are familiar with the functionality of a specific service. This substantially lowers the onboarding threshold. With a monolith, new developers must dive into the code of the entire application, understand the functions of every block, and only then begin productive work. Thus, maintaining a monolith is more dependent on specific team members.
Optimization Characteristics
The modularity of microservices architecture also positively affects optimization, since developers can optimize each service separately. Optimizing a monolithic structure is more difficult because the team must account for links between indivisible blocks, and updating any one of them inevitably affects the entire application.
Application Scalability
The distributed structure of microservices and their ability to run on separate servers make scaling fast and easy. In monoliths, scaling one component inevitably requires scaling the entire application as a whole.
Fault Tolerance
Because services are hosted on different servers and have a modular structure, microservices architecture achieves independence of each module. This significantly increases system resilience: a failure in one service does not cause the entire application to fail. With a monolith, the situation is different: all components are tightly interconnected, so the failure of one module can make the entire application inoperable.
Do I Need to Switch to Microservices Right Now?
As we’ve seen, microservices have advantages in many key areas. But does this mean you need to abandon monoliths as an outdated approach and immediately switch to microservices? The answer depends on the current state of your project. And let’s say right away: rushing to adopt microservices is not always the right choice. Distributed architecture also has its drawbacks.
First, microservices require ensuring network connectivity between modules. If a network connection is unstable, this leads to delays and data inconsistencies, which create potential problems in application behavior.
Second, each module of a microservices system requires separate testing and health monitoring. Additionally, you will need to allocate cloud resources for each module, which may increase costs.
Third, with the microservices approach, teams responsible for different modules may encounter interaction issues. This means you may need a connecting link in the form of DevOps specialists who can streamline collaboration and speed up development.
All the factors listed above allow us to conclude that the transition to microservices must be timely. Usually, during the early stages of a project, this is unnecessary, especially if developers have limited human or financial resources.
Switching to a microservices architecture makes sense when there is a clear need for significant scaling, and scaling a monolith has already become difficult. Microservices may be right for you if:
You have a large team. In this case, it makes sense to divide the team into separate, independent groups, each responsible for its own service;
You have a complex, branched application. In this case, it’s far more convenient to update and maintain modules separately than to rebalance the entire system each time;
Your application traffic is highly variable. For example, you see sharp spikes in load during certain periods. Microservices' distributed structure allows quick scaling during peak loads, after which you can easily return to normal capacity levels;
Your application is frequently updated. Working with separate modules in this scenario is much simpler, and new releases will be significantly faster.
If your project meets at least one of these criteria, this is a reason to consider breaking it into independent elements. However, if your application is relatively small and does not require frequent updates, it is reasonable not to rush away from monolithic architecture.
Useful Tools for Organizing Microservices
A modern development approach requires a containerization platform. In most cases, developers use Docker for these purposes. Docker tools allow them to isolate the application from the infrastructure, meaning they can work with it equally well locally or in the cloud, which is very convenient for development.
Once containers become numerous, an orchestrator becomes essential for managing and organizing groups of containers. Kubernetes is most commonly used as an orchestrator due to its strong compatibility with Docker.
Another necessary tool is a load balancer, which ensures even distribution of network traffic across all cloud resources. This significantly increases the application’s fault tolerance.
21 November 2025 · 6 min to read
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