A Comprehensive Guide to Distributed Computing in Data Engineering
Distributed computing has become an essential aspect of data engineering. In today's world of big data, distributed computing makes it possible to process vast amounts of data in a shorter time. Due to the enormous data generated by companies, distributed computing has become a necessity instead of an option. In this post, we will discuss distributed computing in data engineering, including its fundamental knowledge and tools.
What is Distributed Computing?
Distributed computing is a model in which a task is divided into smaller parts and processed independently by several machines in a network. The results of these computations are then combined to produce the final answer. The benefits of distributed computing include faster processing time, high availability, and scalability.
Distributed Computing in Data Engineering
In data engineering, distributed computing can be used to process large datasets, build data pipelines, and perform real-time data processing. Distributed computing can be classified into two categories: distributed storage and distributed processing.
Distributed Storage
Distributed storage is a method of storing data across multiple servers in a network. Each server contains a part of the data, and the data is replicated for redundancy. Distributed storage is used to manage large datasets, where traditional storage methods may not be viable.
Some of the popular distributed storage systems used in data engineering include Apache HDFS, Amazon S3, and Google Cloud Storage.
Distributed Processing
Distributed processing is a method of performing computations by dividing a task into smaller parts and processing them concurrently. The results of these computations are then combined to produce the final result. Distributed processing is used to process large datasets, where traditional processing methods may not be viable.
Some popular distributed processing systems used in data engineering include Apache Spark, Apache Flink, and Apache Beam.
Consensus Algorithms in Distributed Computing
Consensus algorithms are used in distributed computing to achieve a common agreement on a specific data value or a set of values. In distributed systems, achieving consensus is challenging because the nodes may fail or send conflicting data.
Some of the popular consensus algorithms used in distributed systems include:
- Paxos
- Raft
- Zab
- Viewstamped Replication
- Distributed Transactional Consistency
Distributed Computing Tools
Distributed computing requires some tools, including:
Apache Spark
Apache Spark is a widely used, open-source distributed processing system used for big data processing. It is built on top of the Hadoop Distributed File System (HDFS) and offers high-level APIs in Java, Scala, and Python.
Spark offers several libraries for SQL processing, machine learning, and graph processing. Spark has an active community, making it an excellent choice for data engineers.
Apache Flink
Apache Flink is an open-source, distributed processing system used for streaming and batch processing. It is built in Java, but it also offers APIs in Scala and Python. Flink offers a wide range of libraries, including SQL processing, machine learning, and graph processing.
Apache Beam
Apache Beam is an open-source, unified programming model for batch and streaming data processing. Beam offers a simple, flexible API for building both batch and streaming pipelines.
Beam offers portability across different execution engines, including Apache Flink, Apache Spark, and Google Cloud Dataflow. This offers data engineers flexibility in choosing the appropriate execution engine.
Apache Hadoop
Apache Hadoop is an open-source, distributed storage, and processing system used for big data processing. It is built in Java and offers several tools, including MapReduce and HDFS.
Hadoop can be used to process large datasets in batch and stream processing. Hadoop has a large community, which offers support and development of tools.
Conclusion
Distributed computing has become an essential part of data engineering in today's world of big data. Distributed storage and distributed processing are the two categories of distributed computing used in data engineering. Consensus algorithms are used to achieve common agreement in distributed systems. Apache Spark, Apache Flink, Apache Beam, and Apache Hadoop are some of the popular distributed computing tools available for data engineers.
Category: Distributed System