Any organization that has a lot of data can benefit from it, but only if it is processed effectively. With the advent of Big Data, the storage requirements have skyrocketed. As an organizational competency, Data Science brings new procedures and capabilities, as well as enormous business opportunities.
Data Scientists are increasingly using Cloud-based services, and as a result, numerous organizations have begun constructing and selling such services. Amazon began the trend with Amazon Web Services (AWS). AWS, which began as a side business in 2006, now generates $14.5 billion in revenue annually. With each passing year, Data Science AWS is becoming more popular.
In this article, you will be introduced to Data Science and Amazon Web Services. You will understand the importance of AWS in Data Science and the features of AWS. Moreover, the article also highlights the Life Cycle of Data Science AWS. Finally, you will explore the Data Science AWS tools used by Data Scientists. So, read along to gain more insights and knowledge about Data Science AWS.
Table of Contents
- Introduction to Data Science
- Introduction to Amazon Web Services (AWS)
- Importance of AWS in Data Science
- Key Features of Data Science AWS
- Life Cycle of Data Science AWS
- 5 Significant Data Science AWS Tools and Services
Introduction to Data Science
Data Science is the study of a huge amount of data using advanced tools and methodologies to uncover patterns, derive relevant information, and make business decisions. In simple words, Data Science is the science of data i.e. you study and analyze the data, understand the data, and generate useful insights from the data using certain tools and technologies. Data Science is the interdisciplinary field of Statistics, Machine Learning, and Algorithms.
A Data Scientist uses problem-solving skills and looks at the data from different perspectives before arriving at a solution. A Data Scientist performs Exploratory Data Analysis(EDA) to gain insights from data and applies advanced Machine Learning techniques to predict the occurrence of a given event in the future.
A Data Scientist analyses business data to derive relevant insights from the information gathered. A Data Scientist also goes through a set of procedures to solve business problems, such as:
- Asking questions that will help you to better grasp a situation
- Gathering data from a variety of sources, including company data, public data, and more
- Processing raw data and converting it into an Analysis-ready format
- Using Machine Learning algorithms or Statistical methods to develop models based on the data fed into the Analytic System
- Conveying and preparing a report to share the data and insights with the right stakeholders such as Business Analysts
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Introduction to Amazon Web Services (AWS)
Amazon Web Services (AWS) is a Cloud Computing platform offered by Amazon that provides services such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) on a pay-as-you-go basis. It was launched in 2006 but was originally used to handle Amazon’s online retail operations.
AWS has 3 main products:
- EC2 (Amazon Elastic Compute Cloud): Users can rent virtual machines/servers on EC2 to execute their applications. As a result, Amazon charges you according to the computing power and capacity of your server (i.e. Hard Drive size and capacity, CPU speed, and Memory) as well as the duration it has been running.
- Glacier: Glacier is a low-cost web service for online file storage that allows users to save files online. As its name suggests, Amazon Glacier is meant to store and retrieve dormant data over the long term.
- S3 (Amazon Simple Storage Services): Through a web service interface, S3 enables object storage, with scalability and high-speed being major boons.
Throughout the years, AWS has introduced many services, making it a cost-effective, highly scalable platform. AWS now has data centers throughout the United States, Japan, Europe, Australia, and Brazil, among other places. Below is a list of some services available in the following domains:
- Compute Services: Compute services provide the processing power required by applications and systems to carry out computational tasks. Examples are EC2 (Elastic Compute Cloud), EKS (Elastic Container Service for Kubernetes), Lambda, Amazon LightSail.
- Database Services: These services provide resources like Databases and Data Warehouses. For example Neptune, Aurora, RedShift, DynamoDB, ElastiCache.
- Security Services: These services protect your cloud-based data, applications, and infrastructure from attacks and threats. For example KMS (Key Management Service), AWS IAM (Identity and Access Management), WAF (Web Application Firewall), Cloud Directory.
- Storage Services: The AWS Storage Gateway is a hybrid storage service that enables your on-premises applications to seamlessly use AWS cloud storage. For example Amazon Glacier, S3 (Simple Storage Service), AWS Snowball, Elastic Block Store.
- Analytical Services: These services help in the data analysis process and helps to analyze the data with a broad selection of analytic tools and engines. For example Kinesis, QuickSight EMR (Elastic Map Reduce), Data Pipeline, CloudSearch, Athena, ElasticSearch.
- Management Tools: These tools provide visibility, accountability, control, and intelligence so that you can scale your business in the cloud. For example CloudWatch, CloudFormation, CloudTrail, OpsWorks, AWS Auto Scaling.
- Messaging Services: These services allow you to send, receive and schedule different types of messages to and from applications. For example Pinpoint, Simple Queue Service (SQS), Simple Email Service (SES), Simple Notification Service (SNS).
Importance of AWS in Data Science
Now that you got a brief overview of both Data Science and Amazon Web Services(AWS), let’s discuss why AWS is important in the Data Science field. To understand this let’s first figure out some of the limitations associated when you do not use AWS:
- The local system on which you execute Data Science activities has poor processing power, which will affect your efficiency.
- Analytics and model training requires a lot of RAM, which the IDE like Jupyter does not have.
- Installing and maintaining your hardware takes a lot of time and money.
- The deployment of models is quite complex and requires maintenance.
So, to overcome these limitations Data Scientists prefer to use Cloud services like AWS. Small businesses benefit from the inexpensive cost of Cloud services, compared to purchasing servers. In addition, due to optimal energy and maintenance, Data Scientists enjoy increased reliability and production at a reduced cost.
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Key Features of Data Science AWS
In this section, you will understand the critical factors associated with Data Science AWS decision-making. Some of the main features include:
- Data Science AWS Feature: Computing Capacity and Scalability
- Data Science AWS Feature: Diverse Tools and Services
- Data Science AWS Feature: Infrastructure
- Data Science AWS Feature: Pricing
- Data Science AWS Feature: Ease-of-Use and Maintenance
1) Data Science AWS Feature: Computing Capacity and Scalability
It’s easy to scale up or down your system on AWS by altering the amount of vCPUs, bandwidth, etc. It’s possible to scale up a system to finish a task and then scale it back down to save money.
2) Data Science AWS Feature: Diverse Tools and Services
AWS offers a wide range of services. When you consider its efficiency, it’s a one-stop-shop for all of your IT and Cloud needs. Product and Feature development can be done by Data Scientists without the assistance of Engineers (or, at least, needing very little help) using pre-built models.
3) Data Science AWS Feature: Infrastructure
AWS is the most comprehensive and reliable Cloud platform, with over 175 fully-featured services available from data centers worldwide. In a single click, you can deploy your application workloads around the globe. You can also build and deploy particular applications closer to your end consumers with millisecond latency.
4) Data Science AWS Feature: Pricing
The AWS Cloud allows you to pay just for the resources you use, such as Hadoop clusters, when you need them. So, when needed, the servers can be started or shut down. The limitations of on-premises storage are overcome by AWS.
5) Data Science AWS Feature: Ease-of-Use and Maintenance
AWS features a well-documented user interface and eliminates the need for on-site servers to meet IT demands. Hence, programs and software can be deployed more easily. AWS can handle all of your needs. Moreover, infrastructure (e.g. Hadoop clusters) and tools may be set up quickly and easily (e.g. Spark). In addition, maintaining the system takes less time because processes like manually backing up data are no longer necessary.
Life Cycle of Data Science AWS
In this section, you will explore the various stages involved in Data Science AWS to achieve the final result. It is important to understand the Life Cycle of Data Science, otherwise, it may lead you into trouble. So, understanding what takes place in each phase is critical to success. The stages can be mainly divided into:
- Ideation and Exploration
- Experimentation and Validation
- Operationalization and Deployment
- Monitoring and Iteration
Let’s discuss each phase in detail:
1) Ideation and Exploration
Quantitative Research begins with choosing the right project, mostly having a positive impact on business. You should start your ideation by researching through the previous work done, available data, and delivery requirements. The team should also set some objectives and consider what exactly they want to build, how long it might take, and what metrics the project should fulfill.
Important points to consider for this phase include:
- Maintain a Central Repository: You should maintain a central repository of past work so that the next team or individual doesn’t have to research the past work again.
- New Tools and Services: Technology is evolving each day. To successfully use the correct tools and services, you need to create a new and isolated environment to not disrupt the previous work.
- Share Results: Sharing results with the team is the most important consideration. It helps others to learn and get insights from it, which may help in future projects.
2) Experimentation and Validation
After the Ideation and Data Exploration phase, you need to experiment with the models you build. In this phase you run test cases, review the results and make changes based on the results. Moreover, you need to validate your results against the metrics set so that the code makes sense to others as well. This phase can be slow and computationally expensive as it involves model training.
Important points to consider for this phase include:
- Remove Bottlenecks: To run the computationally intensive experiments you should scale your resources.
- Collaborate with Other Teams: Data Science teams need to share their ideas with other teams and ask for feedback and suggestions. In addition to this, the Managers need to keep a check on the project to see if it is on track and aligned with the objectives set. Moreover, teams who will work with the results of this project should be updated from time to time.
- Track your Work: You need to keep track of all the experiments conducted and the result outcomes. It will help the team to learn and not repeat the same errors.
3) Operationalization and Deployment
This phase is as important as the other phases. To make your projects operational you need to deploy them which involves a lot of complexity. One of the challenges in this phase is that you don’t know the number of resources beforehand required to deploy your project. So, due to this insufficient knowledge of resources, many projects get stalled or may fail.
Important points to consider for this phase include:
- Ease-of-Use: You should build interactive dashboards, simple GUI, apps so that it is easier for other users to use.
- Ready access to Infrastructure: Using AWS services will help you scale easily, accelerate the deployment and provide the “right-size” hardware.
- Cost of Iteration: The teams should have the optimal iterations that would reduce their overhead to deploy their models and eventually have low operating costs.
4) Monitoring and Iteration
The Data Science team needs to keep track of, monitor, and update the production models. Because models automate decision-making at a high volume which can introduce new risks that might be difficult for companies to understand, for example, with fraud detection models un-updated, criminals can adapt as models evolve. Moreover, the Data Science AWS teams need to be able to detect and react quickly when the models drift away from the objectives.
Important points to consider for this phase:
- Access the Model: The Data Science AWS team needs to access the production models and also the logs for performance monitoring.
- Production Development: The models in production should directly link with the development environment so that the users can understand how the model works.
- Backup: The teams should keep in mind to create a backup of the models as it helps them to retrain the models or roll back to previous versions to update the models.
5 Significant Data Science AWS Tools and Services
In this section, you will explore the 5 significant Data Science AWS Services for Data Scientists:
- Amazon Elastic Compute Cloud (EC2)
- Amazon Simple Storage Service (S3)
- Amazon Relational Database Service (RDS)
- Amazon Redshift
- Amazon Elastic MapReduce (EMR)
1) Amazon Elastic Compute Cloud (EC2)
Amazon Elastic Compute Cloud (Amazon EC2) is a Cloud-based web service that provides safe, scalable computation power. Your computing resources are under your control, and Amazon’s proven computing environment is available for you to run on.
2) Amazon Simple Storage Service (S3)
Amazon Simple Storage Service (Amazon S3) provides industry-leading scalability, data availability, security, and performance for object storage. To help you manage your data, Amazon S3 includes easy-to-use management capabilities.
3) Amazon Relational Database Service (RDS)
Amazon Relational Database Service (Amazon RDS) is a Cloud-based Relational Database Management System that makes it easy to set up, operate, and scale a database. In addition, Amazon RDS provides you with 6 well-known Database engines to pick from, which include:
- Amazon Aurora
- Oracle Database
4) Amazon Redshift
Amazon Redshift is a Cloud-based Data Warehousing solution that can handle petabyte-scale workloads. Redshift allows you to query and aggregate exabytes of Structured and Semi-Structured Data across your Data Warehouse, Operational Database, and Data Lake using standard SQL.
5) Amazon Elastic MapReduce (EMR)
Amazon EMR is the industry’s premier Cloud Big Data platform for processing huge amounts of data using open-source tools such as Apache Spark and Apache Hive, among others. Setting up, operating, and scaling Big Data environments is simplified with Amazon EMR, which automates laborious activities like provisioning and configuring clusters. On huge datasets, EMR can be used to perform Data Transformation Workloads (ETL) on data.
In this article, you learned about Data Science AWS’s significance and features. In addition, you gained an understanding of the Life Cycle of Data Science. You also explored various Data Science AWS tools used by Data Scientists. Due to its popularity among enterprises, Amazon Web Services (AWS) has become one of the most sought-after Cloud Computing platforms in the Data Science field. An advantage in the Data Science race is having hands-on experience with Amazon Web Services (AWS).
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