Tips for Processing Real-Time Data in a Data Center

Real-time data processing is the handling of data as it arrives, so it is available for use almost as immediately as it is created and collected. This term is most often used in the context of a business data center and refers to the ability to take data that’s been collected and make decisions based on that data as quickly as possible.

It is an essential capability for most enterprises today because it underpins important services such as:

In addition, predictive analytics, artificial intelligence (AI), and machine learning (ML) are all premised on a well-designed and functioning real-time data processing system.

As businesses have begun to adapt to the challenges of the current data landscape, more have developed an approach toward shaping their organization with real-time data processing.

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Challenges with the Current Data Processing Landscape

The data processing landscape has changed dramatically in recent years. Previously, data centers processed data in batches; it was collected over time and then processed all at once.

This approach works well when data isn’t time-sensitive, but real-time data processing has become essential for many organizations as business needs have shifted and data has become more complex.

Currently, the biggest challenge is scaling. Enterprises need to scale real-time resources cost-effectively while simultaneously increasing revenue. Unfortunately, several issues make this difficult.

Massive data growth

The rise of big data has made scaling a challenge. As data centers collect more data than ever before, they need to be able to process it quickly and efficiently. According to a study by Statista, global data creation is projected to exceed 180 zettabytes by 2025. However, the current data processing landscape won’t be able to support this growth.

Increased digitization

The digitization of information and processes is another challenge. As data is increasingly generated in digital formats, it strains existing systems, making it more difficult to process in real time. This is because digital data often needs to be converted into a form that machines can process. As a result, enterprises quickly find that they need to invest in more on-premises or cloud solutions.

Real-time analytics

The need for real-time analytics is also driving the need for real-time data processing. To make decisions quickly, businesses need to be able to analyze data in near real time. This requires a different approach than batch data processing.

Data needs to be processed as it is collected rather than all at once. The problem is that the existing tools are relatively new and present a steep learning curve for users. In addition, they are often quite expensive.

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Importance of Shaping Your Enterprise’s Approach to Real-Time Data Processing

While the challenges of real-time data processing can seem daunting, there are several steps that enterprises can take to shape their approach in a way that will make it more manageable.

• Start Small: Focus on one or two use cases and build from there.
• Have the Right Team in Place: Since the tools and technologies for real-time data processing are still relatively new, it is essential to have a team that is willing and able to learn new things quickly.
• Invest in Proper Infrastructure: Determine the right mix of on-premises and cloud-based solutions.
• Partner with the Right Vendors: Look for vendors with a proven track record in real-time data processing and that offer support and training.

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How to Adapt to the Changing Data Landscape

The landscape for data processing is changing rapidly. In order to stay ahead of the curve, enterprises need to be proactive in their approach. Here are a few things that you can do:

Identify your data processing needs

Identifying your data processing needs will help you determine what type of data processing is right for your organization.

Assess your current infrastructure

Assessing your current infrastructure will help you identify any bottlenecks in your system and determine where you need to make changes.

For example, if you’re using an on-premises data processing system, you may need to migrate to a hybrid solution to allow you to scale faster. But then again, this will depend on your internal security policies and compliance needs.

Also, if you’re using an on-premises solution, it is vital to match your server components to your unique requirements. Since you can only achieve the best real-time performance at scale with the proper server hardware, you’ll need to figure out how that data is processed.

Server memory (DRAM) is costly and uses a lot of power at scale. In addition, hard drives must provide dependable long-term storage. New server persistent memory alternatives are accessible that match the speed of DRAM but are less expensive and store data in the event of a power interruption. In-memory data processing tools and databases can also be a way to speed data processing.

Invest in the right data processing tools

There are a number of data processing tools available, but not all of them are right for every organization. Therefore, it’s important to choose the right tool for your specific needs.

For example, companies in industries that rely on streaming data to operate, such as social media feeds, up-to-the-minute retail inventory management, real-time stock trades, real-time forex, ride-sharing apps, and multiplayer game interactions, require streaming data tools as well as fast, in-memory databases.

Improve data quality

Regardless of the data processing approach you choose, it is important to improve data quality. You can enhance data quality by implementing data governance policies and processes and investing in data cleansing and data enrichment tools.

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Scale-up and scale-out

As data volumes continue to increase, it is crucial to have a data processing solution and architecture that can scale up and scale out. Systems are generally designed to scale up (for example, by adding more resources to an existing server or node) or scale-out (for example, by increasing the number of servers or nodes). Therefore, a database, hardware, and software solution that can scale up and scale out is ideal for real-time data processing.

A scalable data processing solution will be able to handle increased data volume without affecting performance.

Use smart data distribution

You can use data distribution techniques to reduce latency further while increasing resiliency. Some of these techniques include:

• Data sharding is the process of distributing data across multiple servers.
• Query routing is a technique that allows you to route queries to the server that contains the required data.
• Load balancing can be used to distribute data across multiple servers.

Using these techniques, you can distribute loads across multiple servers, improve performance, and eliminate hot spots in your data processing system.

Use data compression

Data compression is a method used to save storage space and improve performance by reducing the amount of data that needs to be read from disk. It is able to reduce the size of data files by removing redundant or unnecessary data.

Data compression can be done using various methods, including data deduplication, data reduction, and data archiving.

Implement data governance policies and processes

Data governance is the process of managing data. It includes defining data standards, policies, and processes. Data governance can help you improve data quality by ensuring data is accurate, consistent, and complete.

Becoming a Proactive Leader in Real-Time Data Processing

When starting your journey to real-time data processing, it’s important to keep in mind that this is an ongoing process. As the landscape continues to change, you’ll need to be proactive in your approach to data processing. This means keeping up with the latest trends and technologies and being willing to experiment with new approaches. Only by doing this will you be able to stay ahead of the curve. And when it comes to data, staying ahead of the curve is a critical competitive requirement these days.

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