Understanding Distributed Systems in Data Engineering
In data engineering, distributed systems play a vital role in enabling the processing and storage of large-scale datasets across multiple machines. With the ever-increasing amount of data being generated every day, distributed systems have become an essential aspect of data engineering.
What Are Distributed Systems?
Distributed systems refer to a collection of independent computers that appear to its users as a single, coherent computing system. These independent computers communicate with each other by passing messages over a network. The computers in a distributed system aim to work together to achieve a common goal by coordinating their actions.
Distributed systems are extensively used in data engineering because they provide a scalable architecture that can handle the processing and storage of massive data sets across multiple nodes. The computers in a distributed system operate independently, which makes them highly fault-tolerant.
Characteristics of Distributed Systems in Data Engineering
Distributed systems have some critical characteristics that make them optimal for data engineering applications. The following are some of the significant features of distributed systems:
Scalability
One of the most significant advantages of distributed systems is their scalability. Adding more nodes to the system increases the processing power and storage capacity of the system without the need to change the existing infrastructure.
Fault Tolerance
Distributed systems are highly fault-tolerant because they operate independently of each other. If one node fails, the remaining nodes can continue processing data without any interruption.
High Availability
Most distributed systems are designed to ensure high availability. The availability is achieved by replicating data across multiple nodes to ensure that data is always accessible.
Consistency
Distributed systems can maintain data consistency by proving ACID (Atomicity, Consistency, Isolation, Durability) properties. These properties ensure that all nodes in the system have the same view of data at all times.
Components of Distributed Systems in Data Engineering
Distributed systems in data engineering consist of three main components: communication, computation, and storage.
Communication
Communication components in distributed systems include networking protocols, message passing, and middleware layers. These components enable nodes in a distributed system to communicate with each other and exchange information.
Computation
Computation components include tools and programming languages used to process data in distributed systems. Some programming languages that are commonly used in distributed systems include Scala, Python, and R.
Storage
Storage components are responsible for storing data in a distributed system. Distributed storage systems are designed to provide high availability and data durability. Examples of distributed storage systems include Hadoop Distributed File System (HDFS) and Apache Cassandra.
Tools Used in Distributed Systems in Data Engineering
Several tools are used in distributed systems to enable data processing and storage. Some of the commonly used tools include:
Apache Hadoop
Apache Hadoop is an open-source software framework for distributed storage and processing of large datasets. The framework is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Apache Spark
Apache Spark is a distributed computing system used for big data processing. Spark can run jobs up to 100 times faster than Hadoop MapReduce in memory, or ten times faster on disk.
Apache Kafka
Apache Kafka is a distributed streaming platform used for building real-time data pipelines and streaming applications. Kafka is designed to provide high throughput and low latency, making it an ideal choice for data engineering applications.
Conclusion
Distributed systems are an essential aspect of data engineering. They offer scalable, fault-tolerant, and highly available infrastructure that enables the processing and storage of massive datasets. Apache Hadoop, Apache Spark, and Apache Kafka are some of the tools commonly used in distributed systems for data engineering applications.
Category: Distributed System