The Role of Distributed Systems in Big Data Processing
As our reliance on large-scale data increases, so must ways to handle the vast amount of information. Distributed systems are fundamental in the processing of large datasets and delivering of actionable insights.
But what are distributed systems, and how are engineers using them to meet this ongoing challenge? Harshit Khandelwal, CEO and co-founder of Pioneer Investing Inc., a fintech startup transforming copy trading and stock trading through cutting-edge technology and data-driven insights, explains that distributed systems form the basis of the internet as we use it today.
What Are Distributed Systems?
“Distributed systems consist of multiple devices – called nodes — that work together to do the work that is beyond the capacity of any one system,” he says. “No single machine can efficiently process today’s large-scale data.”
Distributed systems work by coordinating and sharing tasks among multiple computer devices to handle extremely complex workloads. Data can be stored in different locations, providing a safety net in the event of a failure at any one point.
The technology also enables the efficient processing of massive amounts of data, enabling companies to scale their efforts reliably.
What Are the Key Components of Distributed Systems?
A distributed system is a collection of software components spread among different computers that can operate as a single entity. The system can have multiple configurations, including mainframes, workstations, computers, and minicomputers.
However, according to Khandelwal, the most important elements of a distributed computing system include the following.
- Sharing of hardware, software, and data: One machine can tap into the power of another, and so on.
- Concurrent functionality of multiple machines: The computers work together as one unit with many parts.
- Scalability capabilities: As their volume of data grows, companies can add nodes to the network.
- Fault detection and recovery: If one part of the system malfunctions, the rest of the system continues to operate.
- Communication within the system: The devices in a distributed system interact with each other, enabling parallel processing and speedy data analysis.
What Are the Challenges of Distributed Systems?
When vast amounts of data are being processed through multiple channels at the same time, accuracy and consistency are of paramount importance. Khandelwal, who served six years as a senior software engineer for Amazon and led the launch of Social Proofing markers, offers the following tips for organizations looking to scale their operations with distributed systems.
- Make sure all nodes have the same version of the data.
- Use consistent techniques and best-practice protocols to synchronize any changes.
- Optimize configurations to minimize latency.
- Leverage data partitioning strategies to reduce communication between nodes and also minimize latency.
- Use caching mechanisms to store frequently accessed data locally, lowering the burden on the entire network for repetitive tasks.
- Employ monitoring systems to track the performance of the system and resolve any issues quickly.
- Invest in employee education on the complexities of distributed data processing.
- Implement and regularly update robust data security measures.
What Does the Future Hold for Big Data Processing?
As you might expect, artificial intelligence (AI) and machine learning (ML) are becoming fundamental to big data processing.
For example, software engineers like Khandelwal are able to use AI and ML to significantly reduce the need for human intervention in data processing. Experts expect this trend will only continue with new predictive tools to help organizations optimize their operations.
Another trend Khandelwal is watching is the integration of quantum computing with big data processing. “We expect quantum-enhanced AI to change the way we analyze big data,” Khandelwal asserts. “With these changes, we will be able to identify and analyze patterns more quickly and in massive sets of data.”
He explains that business leaders will be able to use these new tools to make better informed and timelier decisions at every level.
