Introduction to Distributed Systems in Data Engineering
Data Engineering is a field that deals with designing, building, and managing data infrastructure that supports data analytics, machine learning, and other data-driven applications. A significant component of data infrastructure is distributed systems.
A distributed system is a system architecture that consists of multiple interconnected computers that work together to achieve a common goal. These computers communicate with each other through a network, and each computer in the system performs a specific function.
In this blog post, we will look at the fundamentals of distributed systems in data engineering. We will cover different aspects like architecture, communication, and coordination in detail. We will also discuss some popular distributed systems and their use cases.
Distributed System Architecture
The architecture of a distributed system typically consists of three layers:
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Application Layer: This layer comprises the end-users' applications that use the services provided by the distributed system.
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Middleware Layer: This layer provides services and APIs to the application layer. It handles network communication, load balancing, and data processing tasks like data compression and encryption.
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Resource Layer: This is the layer where the actual computation and data storage happens. The resources can include computing nodes, storage clusters, and communication networks.
Communication in Distributed Systems
Communication is a crucial aspect of distributed systems. The different components in the system must communicate with each other to exchange data and coordinate their actions.
There are two main communication models in distributed systems: Synchronous and Asynchronous.
In synchronous communication, the sender and receiver interact in real-time, and they wait for each other's response before proceeding to the next message. This mode of communication is often used in applications that require high consistency and reliability.
In asynchronous communication, the sender sends the message to the receiver and does not wait for a response. The receiver processes the message and responds to it when it is ready. This mode of communication is often used in applications that require high availability and scalability.
Coordination in Distributed Systems
Coordination is the process of ensuring that the distributed system components work together to achieve the desired outcome. Distributed systems rely on different coordination mechanisms to achieve this, some of which are:
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Consistency Protocols: Distributed systems need to ensure that any data updates happen in a coordinated and consistent manner. Consistency protocols, like Paxos (opens in a new tab) and Raft (opens in a new tab), help in achieving this.
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Leader Election: In some distributed systems, like Apache Kafka (opens in a new tab), a leader is elected to ensure that all nodes work together to provide a consistent view of the data.
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Distributed Locks: Distributed locks, like Zookeeper (opens in a new tab), are used to coordinate access to shared resources in a distributed system.
Popular Distributed Systems in Data Engineering
There are several popular distributed systems that are commonly used in data engineering, such as:
Apache Kafka
Apache Kafka is a distributed streaming platform that can handle high-throughput and real-time data feeds. It can store large amounts of data and provide a scalable and fault-tolerant way to stream data.
Kafka is often used in applications that require real-time data processing, log aggregation, and messaging.
Apache Mesos
Apache Mesos is a distributed systems kernel that can manage datacenter resources like computing, storage and network. Mesos can handle large-scale cluster management, and it provides a unified way to manage different applications.
Mesos is often used in applications that require high scalability and resource utilization, like big data and machine learning.
Apache Hadoop
Apache Hadoop is an open-source framework that handles distributed storage and processing of large datasets. Hadoop comprises of two components - Hadoop Distributed File System (HDFS) and MapReduce.
HDFS provides a distributed file system that can store large datasets, while MapReduce provides a scalable and fault-tolerant way to process the data.
Hadoop is often used in applications that require batch processing and large-scale data processing.
Conclusion
Distributed systems are a key component of data engineering, and they play a crucial role in managing and processing large data sets. In this blog post, we covered the fundamentals of distributed systems' architecture, communication, and coordination, along with some popular distributed systems.
Understanding distributed systems will help data engineers build robust and scalable data infrastructure and efficiently handle data-driven applications.
Category: Distributed Systems