Distributed data processing (DDP) is an operational technique whereby computing and data management are spread across multiple distinct nodes or processing units. In essence, distributed processing involves an extensive network of interconnected computer systems collaborating on a task. Each of these computer systems or nodes contributes to the global computational power which results in the desired outcome.

Herein are the key attributes of distributed data processing:

• Scalability: DDP architecture is scalable, meaning that processing power can be increased by adding more nodes. This is effective in managing the growing needs of the organization without overhauling the entire system.
• Fault Tolerance: DDP systems are designed to tolerate and recover from failures. Owing to their distributed nature, even if one node fails, it hardly affects the entire processing network, ensuring uninterrupted operation.
• Resource Sharing: In a DDP environment, resources such as databases, software tools, and processing power can be shared among different nodes in the network.
• Increased Performance: Because distributed processing shares the load among several nodes, the overall system performance is enhanced.
• Data Integrity: DDP maintains data integrity by using data redundancy. This means that a replica of data is stored on different nodes to avoid data loss in case of failure.

Despite these strengths, organizations also need to weigh the potential challenges associated with deploying distributed data processing infrastructure.

The implementation of a distributed data processing infrastructure demands meticulous planning and management. It is necessary to understand your organization's needs comprehensively and select the infrastructure that suits them best. Given the complexity of DDP, professionals with expertise in distributed systems should helm this. Analyzing cost-versus-benefit, assessing node capacity, investing in security measures, and testing the system thoroughly before full-scale implementation are some of the steps involved in DDP implementation.

In summary, while distributed data processing may seem daunting due to inherent complexities, when executed correctly, it can provide organizations not just with improved performance but also with immense flexibility, scalability, and cost savings. The key is to carefully evaluate the organization's data processing needs and plan for a distributed setup that best meets these requisites.

Distributed data processing is an integral part of the modern technological landscape. As more organizations turn to it for increased efficiency and productivity, understanding its full potential and implications will become ever more critical.

Distributed data processing comes with numerous advantages, some of which are as follows:

• Enhanced Performance: Since processing tasks are distributed among various nodes, the overall performance tends to be superior in comparison to centralized data processing.
• Scalability: As business needs grow, DDP allows for easy scalability. New nodes can be easily added without disturbing existing processes.
• Improved Availability and Reliability: Due to distributed nature, even if one node fails, others can continue processing, enhancing the system's availability and reliability.
• Resource Utilization: In a DDP environment, all available resources are utilized effectively, reducing waste and improving efficiency.
• Cost-Effective: Reduced hardware costs, scalable architecture, and overlapping functionalities often lead to cost reduction.

Despite these advantages, the fact remains that distributed data processing may offer some issues to organizations.

• Complex Architecture: Due to the involvement of multiple nodes and systems, DDP often comes with complex architecture which can be challenging to manage and troubleshoot.
• Data Security: In DDP, data is distributed and stored in multiple nodes, making data security a potential issue. Ensuring that all nodes are secured and data is encrypted is vital to maintain confidentiality.
• Resource Management: Efficiently distributing and managing the resources in a DDP environment can be challenging, especially in a large-scale setup.
• Network Dependency: As the name implies, DDP relies heavily on the network for communication among nodes. Therefore, network failures or slow connections can significantly impact system performance.
• Integration: Integrating distributed systems with existing applications and databases may also present daunting challenges.