Category: Cloud Fundamentals

About Data Jobs in Cloud

What is the difference between a data scientist and a data analyst?

A data analyst works with data to solve business problems using tools like SQL, R, or other programming languages; data visualization software; and statistical analysis.

Data analytics generally focuses on examining existing historical data to solve defined business problems and answer specific questions. Their work involves setting up dashboards, creating automated reports, and building data pipelines , and inform current decision-making.

A data scientist uses more advanced data techniques to make predictions about the future. They may come up with the questions their team should be asking and determine how to use data to answer them.

Data science goes beyond traditional analytics by using advanced techniques like machine learning and statistical modeling to predict future outcomes. Key differences include

Technical depth: Data scientists require strong programming and math skills.
Time orientation: Analytics focuses on describing and diagnosing past events, while data science predicts future outcomes and prescribes actions.
Tools: Data scientists often use complex machine learning and AI frameworks.

Both data analysts and data scientists play valuable, complementary roles in organizations, with analysts laying the groundwork for data scientists to build upon.

In Cloud Technology : Data Analysts vs. Data Scientists

Data analysts typically use cloud services like:

Cloud data warehouses: Amazon Redshift, Google BigQuery, Azure Synapse Analytics
Business intelligence platforms: Tableau Online, Power BI Service, Looker, Google Data Studio
ETL/ELT services: AWS Glue, Azure Data Factory, Google Cloud Data Fusion
SQL query engines: Amazon Athena, Google BigQuery, Azure Cosmos DB

Data scientists leverage advanced cloud offerings. They build, train, and deploy predictive models using scalable cloud infrastructure.

Machine learning platforms: AWS SageMaker, Azure Machine Learning, Google Vertex AI
Container orchestration: Kubernetes (on AWS, Azure, Google Kubernetes Engine)
Specialized computing resources: GPU/TPU instances (AWS EC2, Azure NCv3, Google Cloud AI Platform)
MLOps tools: AWS SageMaker Model Monitor, Azure Machine Learning Model Monitoring, Google Vertex AI Model Monitoring
Big data processing: Amazon EMR, Azure HDInsight, Google Cloud Dataproc, Databricks

Category	Data Analytics in the Cloud	Data Science in the Cloud
Cloud Data Warehouses	Amazon Redshift, Google BigQuery, Azure Synapse Analytics	Data scientists use occasionally for feature engineering and data exploration
BI & Visualization Tools	Tableau Online, Power BI Service, Looker, Google Data Studio	Data scientists use occasionally for model performance dashboards or exploratory analysis
ETL/ELT Services	AWS Glue, Azure Data Factory, Google Cloud Data Fusion	Data scientists use it for querying datasets for model training
SQL Query Engines	Amazon Athena, Google BigQuery (SQL), Azure Cosmos DB	Data scientists use for querying datasets for model training
Machine Learning Platforms	Not for Data Analysts	AWS SageMaker, Azure Machine Learning, Google Vertex AI
Container Orchestration	Not for Data Analysts	Kubernetes (on AWS EKS, Azure AKS, GKE)
Compute Resources	Standard VMs for dashboards and reporting	GPU/TPU instances: AWS EC2 (p-series), Azure NCv3, Google Cloud AI Platform
MLOps Tools	Not for Data Analysts	AWS SageMaker Model Monitor, Azure ML Model Monitoring, Google Vertex AI Model Monitoring
Big Data Processing	Databricks (for ETL & analytics), Amazon EMR (Hive/Spark), Azure HDInsight	Databricks (for model training), Amazon EMR, Azure HDInsight, Google Cloud Dataproc (Apache Spark/MLlib workflows)
Primary Use Cases	Dashboards, reporting, KPI tracking, pipeline building	Predictive modeling, training ML models, deploying AI pipelines, model monitoring

April 23, 2025

Cloud Fundamentals
In the history of the word “cloud” and its development, core models like Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS) and deployment models (public, private, community, and hybrid clouds) were critical in cloud computing.

Data makes the cloud path dependent; “cloud is NOT a utility,” and “cloud is a retail model” due to its complexity and configurability.

Cloud computing’s layered structure allows different experts to focus on specific areas. Users can choose how much control they want, making the cloud more flexible and useful.

Adopting an authentic cloud is very important to deliver the benefits of cloud—cost savings, energy savings,
rapid deployment, customer empowerment etc.

Computer scientist Peter Mell, of the National Institute of Standards and Technology (NIST), had clearly stated the purpose of cloud computing: when agencies or companies use the definition, they have a tool to determine the extent to which the information technology implementations they are considering meet the cloud characteristics and models.

AWS has met the criteria :

Service Models:

SaaS [Amazon WorkSpaces], PaaS [AWS Elastic Beanstalk], IaaS [EC2, S3]

Deployment Models:

Public Cloud: AWS Global Infrastructure
Hybrid Cloud: AWS Outposts, AWS Direct Connect

Elasticity & scalability:

Auto Scaling Groups, Elastic Load Balancing (ELB), AWS Lambda

Measured Service:

AWS Cost Explorer, Billing Dashboard, Detailed Billing Reports

There are three ways to access AWS core services: with the AWS Management Console, the AWS Command Line Interface, and Software Development Kits.
- Advantages of cloud computing over computing on-premises:
  - Avoid large capital purchases
  - Use on-demand capacity
  - Go global in minutes
  - Increase speed and agility
  - NOT: Paying for racking, stacking, and powering servers is not a benefit of cloud computing over on-premises computing.
- Cloud Computing Model
  - Platform as a service
  - Infrastructure as a service
  - Software as a service
  - NOT System administration as a service
April 23, 2025