Small Services, Big Data: How Microservices Handle Big Data
Big data plays a prominent role in today’s marketplace and companies effectively leveraging that data are experiencing big benefits.
In fact, the 2017 market volume for big data was $35 Billion and is estimated to grow to well over $100 Billion by 2027. Need more convincing? Data-driven organizations are 23 times more likely to acquire new customers.
Sounds pretty sweet right?
As of 2017 53% of companies sure thought so, getting in on their piece of the deliciously lucrative big data pie.
Join those companies and start leveraging your data by partnering with KMS. We are analytics and microservices experts.
But, as sweet as it seems, without the right architecture in place..a crust if you will, you could end up with a pretty soggy pie.
That said, we would like to tell you about microservices and why the application architecture works so well for big data.
Not familiar with microservices? No need to worry–we have already written all about it in our post Going From Monolithic to Microservices.
But to give you a speed summary, microservices is a DevOps-based architecture comprised of a collection of small, independently versioned, deployable and scalable services, each designed to implement a single business capability.
Switching to a microservices architecture can help you evenly distribute responsibilities across your system, increasing the scalability, resilience, and agility of both your products and your teams.
Benefits of Microservices for Big Data
So why are these small services an effective foundation for big data?
Let’s face it, big does not even begin to describe the amount of data being produced and this exponential growth shows no signs of stopping. By 2020, 1.7 megabytes of new information will be generated for every person, per second. You read that correctly, per second.
As your company attempts to process and analyze your slice of that data, you do not want your application to become a processing bottleneck and keep you away from those coveted data-driven insights.
As such, it is important that your data processing application is easily scalable.
One way to ensure scalability is to build your application using a microservices architecture. Built for the cloud and on virtualization, by design microservices are inherently more scalable than a traditional monolith application. Given the elasticity of the cloud, you are able to store and work with large volumes of data relatively easily. As big data continues to evolve, cloud providers have the agility to respond and supports these evolutions.
This scalability is what makes microservices an ideal architectural solution when working with massive datasets.
Microservices also helps you control the quality of your data, something that can prove difficult with monolith data solutions.
The way microservices are designed, each service focuses on a specific aspect of the data. This means you can target specific points in the dataset and easily resolve an issue if one arises, ultimately giving you more control over the quality of the data than you would have with a tradition monolith application.
This organizational structure also works well with the different data storage technologies (noSQL, relational database, etc.). The independent services allow you to apply a different technology to each, depending on which technology works best for that specific part of your data.
Overcoming the Challenges
While microservices is a great solution for big data, the complexities of the architecture should not be taken lightly.
The movement to microservices is both a technical and organizational shift for any company. When working with big data, this is no different. Ultimately it takes a well prepared, DevOps oriented team to most successfully leverage microservices for big data.
Leveraging the right technologies can help and fortunately, these technologies integrate very well with microservices. For example, messaging systems, such as Kafka easily integrate. And container orchestration solutions like Kubernetes or managed services such as AWS are perfect for big data and microservices.
The blog post we mentioned earlier tells you everything you need to know about making the switch and strategies you can use to mitigate potential challenges.
Despite the hurdle of preparing your team and organization, ultimately microservices is a worthwhile investment. The architecture is becoming increasingly popular in DevOps environments and offers the scalability and control you need to handle big data.
Once your team is ready to take on microservices and dive into big data or if you simply want a more effective way to manage the data you are already accessing–we hope that we have convinced you that microservices is a strong architectural candidate.
