Distributed Computing Concepts for Data Engineers

The Traditional Client–Server Processing Pattern

The client-server processing pattern is a foundational model in distributed computing. Clients, such as end-user devices, send requests to servers, which process these requests and return responses. This pattern facilitates efficient resource sharing and centralized management across networks. However, it relies on continuous network connectivity and can suffer from server bottlenecks. Despite limitations, it remains widely used in various applications, including web services and database systems, for its scalability and versatility.

Enter Distributed Computing

Distributed computing involves multiple computers working together on a task. It improves performance, fault tolerance, and scalability by distributing processing across networked systems.

Data Physics

In distributed data processing, data physics refers to two main aspects:

• Data Locality (Distributed Computing Economics): This aspect focuses on optimizing data storage and processing by considering the physical location of data within a distributed system. It involves minimizing data movement and maximizing local processing to improve efficiency and reduce costs.
• CAP Theorem Outcomes: The CAP theorem states that a distributed system can guarantee at most two out of three properties: Consistency, Availability, and Partition tolerance. Data physics in this context involves understanding the trade-offs between these properties and designing distributed systems that align with specific requirements and constraints.

Distributed computing frameworks play a crucial role in data engineering by enabling the processing and analysis of large-scale data sets across multiple machines or nodes in a cluster. They provide a scalable and efficient way to handle big data workloads that cannot be effectively processed by a single machine.

Distributed computing frameworks enable scalable, efficient processing of large datasets across many machines, offering fault tolerance, flexible data handling, and cost-effectiveness in data engineering tasks.