Bye Bye Microservices, Say Hi to Data Streaming

Microservices have taken the data processing world by storm.

They provide a more lightweight and modular architecture that is simpler to manage and scale than traditional monolithic applications. However, there are many use cases where microservices are not the best solution. In this blog post, we will explore some of these alternatives and discuss the future of data processing in the light of cloud presence and stream processing.

What is data processing, actually?

Data processing is the conversion of data into information. It can involve collecting, organizing, analyzing, interpreting, and presenting data. Data processing is used in a variety of fields, including science, business, and government, but it is especially important in the field of information technology. With the ever-growing volume of data being generated every day, data processing is getting more and more complex.

Data processing use cases

There are many data processing use cases, but let us just list a few:

  • batch processing. Batch processing is the process of collecting, sorting, filtering, aggregating, and transforming data into a desired format or structure. This is often used for data preparation before it's loaded into a database or analytics system
  • data cleansing. Data cleansing is the process of identifying and cleaning up invalid or incorrect data entries in a dataset. This is an important step in preparing data for analysis or reporting.
  • processing sensor data. Sensor data is generated by sensors embedded in devices or appliances, and it is used to track the performance of those devices or appliances. This data can be processed and analyzed to generate feedback that helps improve device performance.
  • data transformation. Data transformation is the process of converting data from one format to another. This can be useful when preparing data for analysis or reporting, or when migrating data to a new system.
  • generating reports. Reports are generated by querying data stored in databases or analytics systems. This is a common use case for business intelligence and decision support systems.
  • building dashboards. Dashboards are graphical user interfaces that display information about one or more metrics. This can be used to monitor the performance of a system, or to help make decisions about how to optimize that system.
  • analyzing logs. Log files contain information about the events that have occurred in a system. This data can be processed and analyzed to generate insights about the health and performance of the system.
  • processing payments. Payment processing is the process of accepting and operating payments from customers. This can include authorizing, settling, and clearing payments.
  • processing queries. Queries are requests for information that are sent to a database or analytics system. This data is often used to generate reports or dashboards.

However, data processing doesn't have to be limited to these use cases. In fact, any system that requires the processing of data can benefit from a microservices-based architecture.

Here come microservices

Microservices are a type of software architecture that is composed of small, independent services that work together to form a complete application. This approach is in contrast to the traditional monolithic architecture, where all components are tightly coupled and reside in a single codebase. Microservices are a type of lightweight architecture that can be used for data processing. They provide a more modular and manageable structure than traditional monolithic applications. Monolithic applications are difficult to scale and manage, while microservices can be deployed and scaled independently.

What are the advantages of microservices?

A few advantages to microservices make them appealing to many organizations.

First, they allow for greater flexibility in terms of how you deploy and scale your applications. With monolithic applications, you generally have to deploy and scale the entire app as a unit. With microservices, you can more easily deploy and scale individual services independently. This can be helpful if you have some services that are used more heavily than others - you can just scale those up without affecting the rest of the app.

Furthermore, microservices promote code reusability. Services can be reused across multiple applications, which can save time and effort when developing new apps. And if a service needs to be updated, you can just update that one service without having to deploy a new version of the entire app.

Finally, microservices can make your applications more resilient. If one service goes down, the rest of the app can still continue to function. This is in contrast to monolithic applications, where a single failure can take down the entire app.

So why not always use microservices?

While there are many advantages to using microservices, they are not always the best solution for every problem.

Microservices can be more complex to develop and manage than monolithic applications. You need to be familiar with both the microservice architecture and the individual services that make up your app. And if you have a lot of services, it can be difficult to keep track of them all.

Another potential issue with microservices is that they can introduce latency into your applications. If a service needs to communicate with another service in order to process a request, that communication can take some time. This can be an issue if you need your app to respond quickly to user requests.

Finally, microservices can be more expensive to scale than monolithic applications. You may need to invest in additional resources (like servers and bandwidth) in order to scale your app up.

Microservices are not the only solution

In addition, traditional architectures such as client-server or three-tier architectures may provide better performance in some situations. That's why it's important to know when microservices are the right choice and when they're not.

So if microservices are not always the best solution, what are some of the alternatives?

There are many data processing use cases that are better suited to even simpler, more lightweight architectures than microservices. For example, if you have a small amount of data that doesn't need to be processed in real-time, a traditional monolithic application may be a better choice.

Stream processing may be a better solution if you need to process data in a more controlled environment, such as on-premises or in a private cloud.

We’ll cover them all below.

Batch processing vs data streaming

Batch processing and data streaming are two different ways of handling data.

Batch processing is when you take a set of data and do something to it, like calculate the average or find the largest number.

Streaming is when you take a set of data and keep reading it one piece at a time as it comes in.

Batch processing usually happens all at once, after the data has been collected. Streaming happens while the data is coming in, so the results are updated as new data arrives.

Which one you use depends on what you're trying to do. If you need to work with the data later, like in a spreadsheet or database, then you'll need to process it first. If you just want to see what's happening now, or if you need to respond quickly to changes in the data, then streaming is better.

Data streaming in the light of microservices

When done correctly, streaming can provide many benefits.

Streaming data is a great fit for microservices architectures. By definition, streaming data needs to be processed as soon as possible. This is ideally suited for a distributed system where individual services can be independently scaled to meet the demands of the moment. It simply makes it possible to process data as it arrives, rather than waiting until all the data has been collected. This can be important when dealing with time-sensitive information, or when there are large volumes of data that need to be processed quickly.

Furthermore, streaming data often arrives in discrete batches, which can be easily handled by workers in a queue-based system.

Another key advantage of streaming data is that it makes it possible to process data in a more distributed way. This is especially important in the era of cloud computing, where resources are often spread across multiple servers. By using a streaming architecture, it's possible to take advantage of all those resources and process data as quickly as possible.

And finally, many streaming data systems are based on open source technologies like Kafka and Storm that are well-suited for deployment in a cloud environment.

The future of data processing is moving towards more distributed and streaming-based architectures. Microservices have already revolutionized the way we process data, and we can expect to see more use of streaming data and cloud-based deployments in the future.

The future of data processing: data streaming?

With the rise of cloud computing, the future of data processing is moving towards more distributed and streaming-based architectures. Microservices have already revolutionized the way we process data, and we can expect to see more use of streaming data and cloud-based deployments in the future.

Data processing is an important part of any business, and the way it's done will continue to evolve as new technologies emerge. For now, microservices and streaming data are two of the most important trends in the world of data processing. And as businesses become more reliant on data, these trends are only going to become more important.

Another benefit of streaming is that it helps to reduce latency by allowing services to communicate with each other directly without waiting for all the data to arrive and be processed. This can improve the system's overall responsiveness and help ensure that tasks are completed in a timely manner.

Data processing is a key part of any business or organization. As technology evolves, new ways of handling data will continue to emerge. In particular, stream processing is changing the way we think about data processing.

Stream processing is a computer programming technique for handling continuous streams of data in real time. It can be used to detect and respond to patterns in data, as well as to make decisions based on the most up-to-date information. Stream processing is a key part of many cloud applications, such as social media, Internet of Things (IoT), and financial services. It can also be used for security purposes, such as detecting fraud and malicious activity.

In the world of data processing, microservices are king.

And there's a good reason for that. Microservices allow businesses to break down their systems into smaller, more manageable pieces. Developing and deploying applications becomes easier, and system reliability and scalability improve as well.

But it's about data streaming.

All this data is coming at us so fast and furious that we can't store it all. We need to find ways of dealing with it as it comes in. That's what stream processing is for-to give us the ability to act on data as it comes our way, without having to wait until the end of the day (or week, or month).

And that's one of the reasons why you should be looking at data streaming as part of your data processing strategy.

Not only is it a powerful tool, but it's also becoming more and more popular as businesses become more reliant on data-driven decision making.

If you want to know more about our approach to data processing, read this article on our blog: https://scramjet.org/blog/welcome-to-the-family

Photo by Floriane Vita on Unsplash

Project co-financed by the European Union from the European Regional Development Fund under the Knowledge Education Development Program. The project is carried out as a part of the competition of the National for Research and Development: Szybka Ścieżka.

Author:

Natalia Czapracka

Natalia Czapracka

Marketing expert with 13 years professional experience.

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