Click to learn more about author Andreea Jakab.
Big data and analytics projects can help your business considerably, but their performance directly depends on the hardware used. One common issue is the lack of scalability, when your project starts using an increased amount of resources.
Not being able to grow your infrastructure along with your data volume will cause bottlenecks in your big data and analytics workloads. A non-scalable system means that the infrastructure will eventually reach its resource limit. Migrating to a different infrastructure is a complicated and time-consuming process that will generate significant downtime and costs. Let’s find out how to choose the best infrastructure that can scale along with your analytics projects.
Why is managing big data workloads such a complex process? Here are a few reasons:
- The ways of collecting data and the data sources are wide and varied
- Big data tools are generally specialized
- You need to select the proper big data tools for your use case
- Handling data leads to security concerns
- You need to comply with local and international regulations
- Performance bottlenecks due to hardware and software limitations
Hardware Limitations for Big Data and Analytics Projects
If you start to encounter slow performance, or service outages, you might need to check your infrastructure. There are a number of reasons why your performance might be slow or erratic:
- High CPU usage: Big data projects and analytics projects require high computing power so that the CPU usage doesn’t reach bottlenecks and ultimately slow down performance.
- Low memory: Servers that don’t have enough memory to handle the ingestion load can slow the infrastructure completely, and need a RAM upgrade.
- High disk I/O: Traditional spindle disks might not be enough in terms of read-write speed.
- High disk usage: Maxed-out server disks can cause bottlenecks and need data scaling.
When running big data workloads, due to the high probability of data volume increase, you might run faster into high CPU usage, low memory, and high disk usage and your setup might not be running properly. Many businesses’ databases are overwhelmed with the amount of data they are facing and need scaling.
Why Is Scalability So Important for Big Data and Analytics Projects?
Generally, a big data infrastructure requires a fast network and servers that provide extensive computing power. To run big data and analytics projects, the server infrastructure needs to be powerful and adapted to your company’s size, but also flexible enough to fit your growth path.
Data grows exponentially, and it can overload your data system. A sudden change in data volume can cause your setup to reach bottlenecks that can lead to downtime. Nobody wants downtime.
You’ll want your data processing systems to increase their processing capabilities along with the data volume. This means that the system must anticipate the exponential growth of data and must handle the changing flow of information.
When you decide to scale, there are two ways:
- Scaling up: This vertical type of scaling means changing your server with a faster one that has more powerful resources (processors and memory). Scaling up is generally a feature found in the cloud, as dedicated servers cannot be easily scaled (since the move requires going to the data center to change the server manually and considerable downtime). However, there’s another option available. Bare metal servers are a type of dedicated server with additional features that offer the possibility to scale up and down, whenever needed, from a single UI platform and with minimal downtime.
- Scaling out: This horizontal type of scaling means using more servers for parallel computing. This is considered to be best for a real-time analytics project, as you can design a proper infrastructure for your use case from the beginning and add as many servers as you need going forward. You can also add a load balancer to handle ingestion requests simultaneously and split the load among several servers. Horizontal scalability tends to lead to lower costs in the long run.
Let’s suppose you have a real-time analytics project. Maybe in the beginning you receive only a few requests every several minutes, since you just started out and there’s not much data to analyze. At some point, more requests start coming in and you notice the database is not working properly anymore since the disk space is almost full, CPU is busy 80% of the time, and the RAM fills up rapidly. Now is the moment to scale up and upgrade to a more powerful server. As long as the upgrade takes place automatically and with minimal downtime, like with bare metal servers, you’re set for success.
Fast-forward in time, your business starts growing more and more, and you start receiving a few hundred requests per minute. It’s now time to scale out. You get, let’s say, 20 machines with the same database schema, each machine holding only a part of data, that are connected in such a way (designs here are customizable and depend on your use case) that allows your system to work flawlessly and properly manage and analyze data in real time. The horizontal scalability offered by Hadoop is a strong point to consider for businesses with large data storage, management, and analytics needs, like in this case. MongoDB also supports horizontal scalability by using sharding (automatically balances the data into the cluster by distributing it into physical partitions).
Which Infrastructure to Use?
While the public cloud is known for its scalability feature, running big data and analytics workloads in the cloud is a big no-no. A physical machine, such as a dedicated or a bare metal server, will almost always outperform a virtualized solution, such as the public cloud, especially when discussing real-time ingestion of data. The high volume of data that needs to be analyzed in big data projects can encounter more bottlenecks in the cloud and bring about extra costs than on a dedicated machine, such as a bare metal server.
Bare metal servers offer the both the power of dedicated machines and the flexibility and scalability of the cloud that are so necessary for real-time analytics, big data, predictive analytics, machine learning, and data science. Due to the performance of bare metal servers, and the speed of the Layer 2 Networking, you can set up the powerful infrastructure you need and still benefit from scalability, both horizontally and vertically.