Distributed computing is a model where components of a software system are shared among multiple computers. The intention here is to improve efficiency and performance. It allows us to take advantage of distributed systems like AWS, where each computer (or node) communicates and coordinates its actions by passing messages.
AWS works on clustered computing, a type of distributed computing, where many servers are networked together to achieve a common goal. It enhances scalability, load balancing, and network infrastructure.
For example, consider a situation where you have a significant amount of data to be processed, but a single machine is insufficient. You can distribute the job across many machines in a network using distributed computing, allowing the task to be completed faster.
Key Concepts in Distributed Computing
1. Data Partitioning
This involves breaking down a database into several parts and distributing them across multiple servers.
Types of Data Partitioning:
- Horizontal partitioning: It involves putting different rows into different tables. It’s also known as range partitioning.
- Vertical Partitioning: It involves dividing a table into smaller ones with fewer columns and then distributing these new tables across different servers.
For example, a university might have a database with a table ‘students’ where there are millions of rows. They could distribute the rows based on student ID ranges, say 1-100,000 in one table and 100,001-200,000 in another.
2. Parallel Processing
This method implies processing multiple tasks simultaneously over multiple CPUs. AWS provides Elastic MapReduce (EMR) for parallel processing of large-scale data.
3. Data replication
Data replication involves storing duplicate copies of data on multiple servers to ensure its availability in case one server goes down.
Distributed Computing on AWS
Amazon Web Services (AWS) provides numerous services designed specifically for distributed computing. Some of these include:
- EC2 (Elastic Compute Cloud): You can quickly scale up or down the number of server instances with EC2.
- Elastic MapReduce (EMR): A cloud-based big data platform for processing vast amounts of data using popular frameworks such as Apache Spark and Hadoop.
- AWS Lambda: This allows you to run your functions in the cloud without provisioning or managing servers. The codes start running within milliseconds of an event such as an image upload, insign app activity, or a website click.
- Amazon S3 (Simple Storage Service): This service helps in storing and retrieving data from anywhere on the web.
- Amazon DynamoDB: This is a nonrelational database that delivers reliable performance at any scale.
How to implement Distributed Computing in AWS
- You launch an EC2 instance which acts as the master node.
- You configure and launch additional EC2 instances which act as core and task nodes.
- Hadoop distributes the data across the nodes, and then you run the job. Hadoop splits the job into tasks which are then executed on the nodes.
Note: AWS provides EMR, a managed Hadoop framework. EMR takes care of the undifferentiated heavy lifting involved in setting up a distributed computing environment.
Conclusion
Understanding distributed computing and its practice in AWS is paramount for data engineers. AWS provides numerous services that make the adoption of distributed computing easier, more efficient, and cost-effective. A DEA-C01 certified data engineer leverages these services to design, build, secure, and maintain analytics solutions that provide meaningful data to businesses.
Practice Test
True or False: Distributed Computing refers to multiple computer systems that are situated in different locations working on a single project together.
- True
- False
Answer: True.
Explanation: Distributed Computing is a form of computing which allows the use of multiple computer systems located at various places to work on a single project, thereby increasing efficiency.
Multiple choice: What are the essential components of Distributed Computing?
- a) Data Storage
- b) Processing Power
- c) Bandwidth
- d) All of the above
Answer: d) All of the above.
Explanation: To enable effective distributed computing, you need sufficient data storage, processing power, and bandwidth. They form the foundation of a distributed computing system.
Multiple select: Which are the major types of Distributed Computing?
- a) Grid Computing
- b) Cloud Computing
- c) Client-Server Computing
- d) Cluster Computing
Answer: a) Grid computing, b) Cloud computing, c) Client-Server computing, d) Cluster computing.
Explanation: Grid, Cloud, Client-Server, and Cluster computing are all types of Distributed Computing. They use different approaches but have the same foundation – multiple computer systems working together.
True or False: The Cloud Computing model provides shared resources over a network on demand, thereby reducing costs and increasing agility.
- True
- False
Answer: True.
Explanation: In cloud computing, resources are dynamically provisioned on a fine-grained, self-service basis over the Internet, which reduces costs and increases agility.
Multiple choice: Which one of the following AWS services offers a reliable, scalable, and inexpensive data storage infrastructure?
- a) Amazon S3
- b) Amazon EMR
- c) Amazon Lumberjack
- d) Amazon Cast
Answer: a) Amazon S3
Explanation: Amazon S3 (Simple Storage Service) is an object storage service that offers industry-leading scalability, data availability, security, and performance.
Multiple select: Which of the following AWS services are associated with data processing and analytics?
- a) Amazon EMR (Elastic MapReduce)
- b) Amazon Athena
- c) Amazon Redshift
- d) Amazon Web Services
Answer: a) Amazon EMR, b) Amazon Athena, c) Amazon Redshift.
Explanation: Amazon EMR, Athena, and Redshift are AWS services for data processing and analytics. EMR handles big data, Athena is used for querying data, and Redshift is for data warehousing.
True or False: AWS Elastic Load Balancing automatically distributes incoming application traffic across multiple targets, such as Amazon EC2 instances.
- True
- False
Answer: True.
Explanation: AWS Elastic Load Balancing automatically distributes incoming application traffic across multiple targets, which increases the availability and scalability of your applications.
Multiple choice: What is the main function of AWS Glue?
- a) Data Classification
- b) Data Cataloging
- c) Data Compressing
- d) Data Scheduling
Answer: b) Data Cataloging.
Explanation: AWS Glue is a fully managed extract, transform, and load (ETL) service that enables data discovery, cataloging, and data transformation for immediate and efficient data availability.
Multiple select: Which of the following are managed database services offered by AWS?
- a) Amazon RDS (Relational Database Service)
- b) Amazon DynamoDB
- c) AWS Lamba
- d) AWS Data Pipeline
Answer: a) Amazon RDS, b) Amazon DynamoDB.
Explanation: Amazon RDS and DynamoDB are managed database services by AWS. RDS is relational, DynamoDB is a NoSQL database service.
True or False: Every node in a distributed system holds a copy of the complete database.
- True
- False
Answer: False.
Explanation: One of the key characteristics of a distributed system is that the data is distributed among all the nodes and not every node holds a copy of the complete database.
Multiple choice: In AWS, what is the function of a data lake?
- a) Data storage
- b) Data analysis
- c) Data processing
- d) All of the above
Answer: d) All of the above.
Explanation: A data lake, offered by AWS, is a centralized, curated, and secured reservoir that stores all your data, at any scale, allowing you to run big data analytics, artificial intelligence (AI), machine learning (ML), and real-time analytics.
Interview Questions
What is the main advantage of distributed computing in AWS?
The main advantage of distributed computing in AWS is the ability to scale and handle workloads efficiently by distributing them across multiple resources, increasing the reliability and availability of applications and services.
What is Amazon EMR in the context of distributed computing?
Amazon EMR (Elastic MapReduce) is a cloud-based big data platform that enables processing large amounts of data across a scalable, distributed infrastructure. It supports tools like Apache Spark and Hadoop to distribute data processing tasks across multiple nodes.
Which AWS service is designed specifically for real-time big data processing and is ideal for distributed computing?
The AWS service designed specifically for real-time big data processing and ideal for distributed computing is Amazon Kinesis.
What is the main purpose of Elastic Load Balancer in AWS and how it’s related to distributed computing?
Elastic Load Balancer is a load balancing service for AWS deployments. It automatically distributes incoming application traffic across multiple targets, such as EC2 instances, containers, and IP addresses, in multiple Availability Zones. This distribution increases the fault tolerance of your applications.
What are Lambda functions in AWS and how they fit into distributed computing?
AWS Lambda is a serverless compute service that allows you to run your code without provisioning or managing servers. In the context of distributed computing, Lambda function can run code in response to events, such as changes to data in an S3 bucket or an update to a DynamoDB table, allowing for flexible, scalable, and distributed processing power.
What components make up Amazon’s DynamoDB?
Amazon’s DynamoDB comprises of tables, items, and attributes. It is a fast and flexible NoSQL database service for any scale, and it goes hand in hand with distributed systems providing seamless scalability and performance.
What AWS service would you use to distribute data processing tasks across a vast computing resource?
Amazon Elastic MapReduce (EMR) would be used to distribute data processing tasks across a vast computing resource.
How does the concept of distributed computing apply to a data lake in AWS?
In AWS, a data lake allows storing all your structured and unstructured data and running different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning. The lake employs distributed computing to process this data, often leveraging services like Amazon Redshift or EMR.
What is Amazon S3’s role in distributed computing?
Amazon S3 can store and retrieve any amount of data at any time, from anywhere on the web, making it an ideal cornerstone for a distributed computing environment. It can serve as the data lake for analytics or an integral part of a Big Data solution.
How does AWS Glue fit into the realm of distributed computing?
AWS Glue is a fully managed extract, transform, and load (ETL) service that simplifies the process of preparing and loading your data for analytics. With AWS Glue, data can be available for analysis in a distributed manner, allowing for efficient data querying and transformation.
What database service is used in AWS for building open, flexible, and scalable cloud-native applications?
Amazon DynamoDB is the AWS database service used for building open, flexible, and scalable cloud-native applications.
What is the function of AWS Data Pipeline in distributed data computing?
AWS Data Pipeline is a web service for orchestrating complex data flows across distributed services. It also allows you to process and transfer data between different AWS services and on-premise data sources.
What role does Amazon Athena play in AWS Distributed computing?
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there’s no infrastructure to manage, and you only pay for the queries you run, making it part of the robust distributed computing environment.
What is meant by “fault tolerance” in AWS distributed computing?
In AWS distributed computing, “fault tolerance” refers to the system’s ability to continue functioning correctly even if some parts of the system fail. Services like Amazon Elastic Load Balancers, Kinesis, and S3 among others, are designed to be fault tolerant by automatically rerouting traffic or duplicating data to ensure continuous operation.
What is meant by Shard in the context of distributed computing in AWS Kinesis?
In AWS Kinesis, a shard is basically a sequence data record. Each shard can support up to 5 transactions per second for reads, up to a maximum total data read rate of 2 MB per second and up to 1,000 records per second for writes, up to a maximum total data write rate of 1 MB per second. This contributes to the distributed nature of data in real-time streaming applications.
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