Concepts

Cloud computing provides a way to access servers, storage, databases, networking, software, analytics, and intelligence over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale. AWS (Amazon Web Services) is one of the leading cloud service providers offering a comprehensive suite of cloud computing services.

In AWS, cloud computing is made up of various services such as:

  • Amazon EC2 (Elastic Compute Cloud) for scalable computing capacity.
  • Amazon S3 (Simple Storage Service) for object storage.
  • Amazon RDS (Relational Database Service) for managed relational database service.
  • Amazon Redshift for data warehousing.

These services, among others, are used to build and run a wide range of data engineering solutions in the cloud.

Distributed Computing

Distributed computing, meanwhile, refers to a model in which components of a software system are shared among multiple computers to improve efficiency and performance. The distributed nature of these systems allows for parallel processing, which can lead to significant reductions in processing time.

In the context of AWS, distributed computing is evident in services such as:

  • Amazon EMR (Elastic MapReduce) for processing vast amounts of data across resizable clusters of Amazon EC2 instances using popular distributed frameworks such as Apache Hadoop and Apache Spark.
  • Amazon DynamoDB for a managed NoSQL database that supports key-value and document data structures, and is designed for distributed data structure scalability.

Comparison and Use Cases

Feature Cloud Computing Distributed Computing
Resource management Resources are managed centrally by the cloud service provider. Resources are spread across multiple nodes in the network.
Scalability Can be easily scaled on-demand to meet workload requirements. Scalability is achieved by adding more nodes to the system.
Fault tolerance High fault tolerance due to the redundancy of cloud services. Can be fault-tolerant if designed with redundancy in mind.
Data processing Can be centralized or distributed depending on the service. Inherently distributed across multiple computing nodes.
Examples AWS S3, AWS EC2, AWS RDS, AWS Lambda AWS EMR, Amazon DynamoDB, AWS ECS (for containerized services)

Examples in AWS Data Engineering

As a data engineer preparing for the AWS Certified Data Engineer – Associate (DEA-C01) exam, it’s important to understand both distributed and cloud computing concepts, as well as when and how to apply them.

For instance, you may be tasked with designing a data lake to store structured and unstructured data. AWS offers Amazon S3 as an ideal storage solution for data lakes due to its high durability, availability, and scalability. Coupled with AWS Lake Formation, which makes it easier to set up a secure data lake in days, you can manage, catalog, and clean your data easily.

On the distributed computing side, you could be working with enormous datasets that require processing power beyond what a single machine can offer. To address this, you might use Amazon EMR, which allows you to quickly and cost-effectively process vast amounts of data. Here’s a high-level example:

# Creating a cluster with Amazon EMR using the AWS CLI
aws emr create-cluster \
–name “DataProcessingCluster” \
–use-default-roles \
–release-label emr-5.30.0 \
–instance-count 3 \
–applications Name=Spark Name=Hadoop \
–ec2-attributes KeyName=myKey \
–instance-type m5.xlarge \
–region us-west-2

AWS certification candidates must also understand how to implement a highly available and fault-tolerant data processing architecture. For instance, you can create a multi-AZ deployment of the Amazon RDS to ensure that your relational database service remains operational even if one availability zone becomes unavailable.

Cloud computing and distributed computing principles underpin the architecture of services like Amazon Redshift, a fully managed petabyte-scale data warehouse service that manages the data warehouse workload in the cloud so that data engineers can focus on data analysis rather than infrastructure management.

By grasping the practical applications and distinctions between cloud computing and distributed computing, candidates preparing for the AWS Certified Data Engineer – Associate exam will be well-equipped to design, build, and optimize data systems on the AWS platform. Knowing when to leverage each model based on data processing needs, scalability requirements, and cost considerations is key to building effective, efficient, and fault-tolerant data engineering solutions.

Answer the Questions in Comment Section

True or False: Cloud computing eliminates the need for an organization to have physical hardware on-premises.

  • (1) True
  • (2) False

Answer: True

Explanation: Cloud computing allows organizations to access and manage computing resources over the internet, which reduces the need for maintaining physical hardware on their own premises.

In AWS, which service is primarily used to distribute incoming application traffic across multiple targets, such as EC2 instances?

  • (1) AWS Lambda
  • (2) Amazon S3
  • (3) Amazon EC2
  • (4) Elastic Load Balancing

Answer: Elastic Load Balancing

Explanation: Elastic Load Balancing automatically distributes incoming application traffic across multiple targets, such as EC2 instances, containers, IP addresses, etc.

True or False: Amazon S3 can trigger a Lambda function when a new object is uploaded.

  • (1) True
  • (2) False

Answer: True

Explanation: Using Amazon S3 event notifications, you can configure to trigger a Lambda function when objects are created, updated, or deleted.

What AWS service would you use to process large datasets using distributed computing?

  • (1) Amazon EC2
  • (2) Amazon RDS
  • (3) AWS Glue
  • (4) Amazon EMR

Answer: Amazon EMR

Explanation: Amazon EMR (Elastic MapReduce) is a cloud big data platform for processing large datasets using distributed computing frameworks like Apache Hadoop and Apache Spark.

True or False: AWS Direct Connect provides a private network connection from an on-premises network to the AWS cloud.

  • (1) True
  • (2) False

Answer: True

Explanation: AWS Direct Connect allows establishing a dedicated network connection from your premises to AWS, which can reduce costs, increase bandwidth, and provide a more consistent network experience.

Which AWS database service is fully managed and automatically scales to accommodate your workload?

  • (1) Amazon RDS
  • (2) Amazon Aurora
  • (3) Amazon Redshift
  • (4) Amazon DynamoDB

Answer: Amazon DynamoDB

Explanation: Amazon DynamoDB is a NoSQL database service that provides fast and predictable performance with seamless scalability, making it fully managed and automatically scalable.

True or False: The main advantage of cloud computing over distributed computing is the dynamic allocation of resources.

  • (1) True
  • (2) False

Answer: True

Explanation: One of the key advantages of cloud computing is its ability to dynamically allocate and scale resources as per demand, which is not a characteristic inherent to distributed computing.

In AWS, what is the term for a logically isolated section of the AWS cloud where you can launch AWS resources in a virtual network that you define?

  • (1) AWS Direct Connect
  • (2) Amazon EC2
  • (3) Amazon VPC
  • (4) AWS Global Accelerator

Answer: Amazon VPC

Explanation: Amazon VPC (Virtual Private Cloud) enables you to create a private, isolated section of the AWS Cloud to launch resources in a virtual network with your own defined IP address range, subnets, route tables, and network gateboards.

What AWS service allows you to orchestrate complex data workflows?

  • (1) AWS Lambda
  • (2) AWS Step Functions
  • (3) Amazon S3
  • (4) AWS Glue

Answer: AWS Step Functions

Explanation: AWS Step Functions is a service that allows you to coordinate multiple AWS services into serverless workflows so you can build and update apps quickly.

How does Amazon Redshift primarily store data?

  • (1) As a graph
  • (2) In key-value pairs
  • (3) In document format
  • (4) In columnar format

Answer: In columnar format

Explanation: Amazon Redshift is a fully managed, petabyte-scale data warehouse service that uses columnar storage to improve query performance and reduce the amount of I/O needed to perform queries.

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Luca Henry
6 months ago

Great blog post! This really helps in understanding the differences between cloud computing and distributed computing.

Caleb Ryan
8 months ago

Thanks for this informative post! Looking forward to more AWS Certified Data Engineer tutorials.

Ievfimiya Davidchenko
7 months ago

Can someone explain the key differences between cloud and distributed computing in terms of scalability?

Susie Gibson
8 months ago

I appreciate the detailed comparison. It cleared up a lot of confusion for me.

Zachary Addy
8 months ago

Does anyone have experience with both AWS and Google Cloud? Which one is more reliable for distributed data processing?

Heide-Marie Hase
8 months ago

Is the AWS Certified Data Engineer exam tough? Any study tips?

Darryl Roberts
7 months ago

This post didn’t provide enough advanced topics.

Momčilo Bekić
8 months ago

Interesting discussion on scalability in cloud vs distributed systems.

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